Skill: zlc000190/writing-skills (version 0.1.0)

--- name: writing-skills description: Use when creating new skills, editing existing skills, or verifying skills work before deployment --- # Writing Skills ## Overview **Writing skills IS Test-Driven Development applied to process documentation.** **Personal skills live in agent-specific directories (`~/.claude/skills` for Claude Code, `~/.agents/skills/` for Codex)** You write test cases (pressure scenarios with subagents), watch them fail (baseline behavior), write the skill (documentation), watch tests pass (agents comply), and refactor (close loopholes). **Core principle:** If you didn't watch an agent fail without the skill, you don't know if the skill teaches the right thing. **REQUIRED BACKGROUND:** You MUST understand superpowers:test-driven-development before using this skill. That skill defines the fundamental RED-GREEN-REFACTOR cycle. This skill adapts TDD to documentation. **Official guidance:** For Anthropic's official skill authoring best practices, see anthropic-best-practices.md. This document provides additional patterns and guidelines that complement the TDD-focused approach in this skill. ## What is a Skill? A **skill** is a reference guide for proven techniques, patterns, or tools. Skills help future Claude instances find and apply effective approaches. **Skills are:** Reusable techniques, patterns, tools, reference guides **Skills are NOT:** Narratives about how you solved a problem once ## TDD Mapping for Skills | TDD Concept | Skill Creation | |-------------|----------------| | **Test case** | Pressure scenario with subagent | | **Production code** | Skill document (SKILL.md) | | **Test fails (RED)** | Agent violates rule without skill (baseline) | | **Test passes (GREEN)** | Agent complies with skill present | | **Refactor** | Close loopholes while maintaining compliance | | **Write test first** | Run baseline scenario BEFORE writing skill | | **Watch it fail** | Document exact rationalizations agent uses | | **Minimal code** | Write skill addressing those specific violations | | **Watch it pass** | Verify agent now complies | | **Refactor cycle** | Find new rationalizations → plug → re-verify | The entire skill creation process follows RED-GREEN-REFACTOR. ## When to Create a Skill **Create when:** - Technique wasn't intuitively obvious to you - You'd reference this again across projects - Pattern applies broadly (not project-specific) - Others would benefit **Don't create for:** - One-off solutions - Standard practices well-documented elsewhere - Project-specific conventions (put in CLAUDE.md) - Mechanical constraints (if it's enforceable with regex/validation, automate it—save documentation for judgment calls) ## Skill Types ### Technique Concrete method with steps to follow (condition-based-waiting, root-cause-tracing) ### Pattern Way of thinking about problems (flatten-with-flags, test-invariants) ### Reference API docs, syntax guides, tool documentation (office docs) ## Directory Structure ``` skills/ skill-name/ SKILL.md # Main reference (required) supporting-file.* # Only if needed ``` **Flat namespace** - all skills in one searchable namespace **Separate files for:** 1. **Heavy reference** (100+ lines) - API docs, comprehensive syntax 2. **Reusable tools** - Scripts, utilities, templates **Keep inline:** - Principles and concepts - Code patterns (< 50 lines) - Everything else ## SKILL.md Structure **Frontmatter (YAML):** - Only two fields supported: `name` and `description` - Max 1024 characters total - `name`: Use letters, numbers, and hyphens only (no parentheses, special chars) - `description`: Third-person, describes ONLY when to use (NOT what it does) - Start with "Use when..." to focus on triggering conditions - Include specific symptoms, situations, and contexts - **NEVER summarize the skill's process or workflow** (see CSO section for why) - Keep under 500 characters if possible ```markdown --- name: Skill-Name-With-Hyphens description: Use when [specific triggering conditions and symptoms] --- # Skill Name ## Overview What is this? Core principle in 1-2 sentences. ## When to Use [Small inline flowchart IF decision non-obvious] Bullet list with SYMPTOMS and use cases When NOT to use ## Core Pattern (for techniques/patterns) Before/after code comparison ## Quick Reference Table or bullets for scanning common operations ## Implementation Inline code for simple patterns Link to file for heavy reference or reusable tools ## Common Mistakes What goes wrong + fixes ## Real-World Impact (optional) Concrete results ``` ## Claude Search Optimization (CSO) **Critical for discovery:** Future Claude needs to FIND your skill ### 1. Rich Description Field **Purpose:** Claude reads description to decide which skills to load for a given task. Make it answer: "Should I read this skill right now?" **Format:** Start with "Use when..." to focus on triggering conditions **CRITICAL: Description = When to Use, NOT What the Skill Does** The description should ONLY describe triggering conditions. Do NOT summarize the skill's process or workflow in the description. **Why this matters:** Testing revealed that when a description summarizes the skill's workflow, Claude may follow the description instead of reading the full skill content. A description saying "code review between tasks" caused Claude to do ONE review, even though the skill's flowchart clearly showed TWO reviews (spec compliance then code quality). When the description was changed to just "Use when executing implementation plans with independent tasks" (no workflow summary), Claude correctly read the flowchart and followed the two-stage review process. **The trap:** Descriptions that summarize workflow create a shortcut Claude will take. The skill body becomes documentation Claude skips. ```yaml # ❌ BAD: Summarizes workflow - Claude may follow this instead of reading skill description: Use when executing plans - dispatches subagent per task with code review between tasks # ❌ BAD: Too much process detail description: Use for TDD - write test first, watch it fail, write minimal code, refactor # ✅ GOOD: Just triggering conditions, no workflow summary description: Use when executing implementation plans with independent tasks in the current session # ✅ GOOD: Triggering conditions only description: Use when implementing any feature or bugfix, before writing implementation code ``` **Content:** - Use concrete triggers, symptoms, and situations that signal this skill applies - Describe the *problem* (race conditions, inconsistent behavior) not *language-specific symptoms* (setTimeout, sleep) - Keep triggers technology-agnostic unless the skill itself is technology-specific - If skill is technology-specific, make that explicit in the trigger - Write in third person (injected into system prompt) - **NEVER summarize the skill's process or workflow** ```yaml # ❌ BAD: Too abstract, vague, doesn't include when to use description: For async testing # ❌ BAD: First person description: I can help you with async tests when they're flaky # ❌ BAD: Mentions technology but skill isn't specific to it description: Use when tests use setTimeout/sleep and are flaky # ✅ GOOD: Starts with "Use when", describes problem, no workflow description: Use when tests have race conditions, timing dependencies, or pass/fail inconsistently # ✅ GOOD: Technology-specific skill with explicit trigger description: Use when using React Router and handling authentication redirects ``` ### 2. Keyword Coverage Use words Claude would search for: - Error messages: "Hook timed out", "ENOTEMPTY", "race condition" - Symptoms: "flaky", "hanging", "zombie", "pollution" - Synonyms: "timeout/hang/freeze", "cleanup/teardown/afterEach" - Tools: Actual commands, library names, file types ### 3. Descriptive Naming **Use active voice, verb-first:** - ✅ `creating-skills` not `skill-creation` - ✅ `condition-based-waiting` not `async-test-helpers` ### 4. Token Efficiency (Critical) **Problem:** getting-started and frequently-referenced skills load into EVERY conversation. Every token counts. **Target word counts:** - getting-started workflows: <150 words each - Frequently-loaded skills: <200 words total - Other skills: <500 words (still be concise) **Techniques:** **Move details to tool help:** ```bash # ❌ BAD: Document all flags in SKILL.md search-conversations supports --text, --both, --after DATE, --before DATE, --limit N # ✅ GOOD: Reference --help search-conversations supports multiple modes and filters. Run --help for details. ``` **Use cross-references:** ```markdown # ❌ BAD: Repeat workflow details When searching, dispatch subagent with template... [20 lines of repeated instructions] # ✅ GOOD: Reference other skill Always use subagents (50-100x context savings). REQUIRED: Use [other-skill-name] for workflow. ``` **Compress examples:** ```markdown # ❌ BAD: Verbose example (42 words) your human partner: "How did we handle authentication errors in React Router before?" You: I'll search past conversations for React Router authentication patterns. [Dispatch subagent with search query: "React Router authentication error handling 401"] # ✅ GOOD: Minimal example (20 words) Partner: "How did we handle auth errors in React Router?" You: Searching... [Dispatch subagent → synthesis] ``` **Eliminate redundancy:** - Don't repeat what's in cross-referenced skills - Don't explain what's obvious from command - Don't include multiple examples of same pattern **Verification:** ```bash wc -w skills/path/SKILL.md # getting-started workflows: aim for <150 each # Other frequently-loaded: aim for <200 total ``` **Name by what you DO or core insight:** - ✅ `condition-based-waiting` > `async-test-helpers` - ✅ `using-skills` not `skill-usage` - ✅ `flatten-with-flags` > `data-structure-refactoring` - ✅ `root-cause-tracing` > `debugging-techniques` **Gerunds (-ing) work well for processes:** - `creating-skills`, `testing-skills`, `debugging-with-logs` - Active, describes the action you're taking ### 4. Cross-Referencing Other Skills **When writing documentation that references other skills:** Use skill name only, with explicit requirement markers: - ✅ Good: `**REQUIRED SUB-SKILL:** Use superpowers:test-driven-development` - ✅ Good: `**REQUIRED BACKGROUND:** You MUST understand superpowers:systematic-debugging` - ❌ Bad: `See skills/testing/test-driven-development` (unclear if required) - ❌ Bad: `@skills/testing/test-driven-development/SKILL.md` (force-loads, burns context) **Why no @ links:** `@` syntax force-loads files immediately, consuming 200k+ context before you need them. ## Flowchart Usage ```dot digraph when_flowchart { "Need to show information?" [shape=diamond]; "Decision where I might go wrong?" [shape=diamond]; "Use markdown" [shape=box]; "Small inline flowchart" [shape=box]; "Need to show information?" -> "Decision where I might go wrong?" [label="yes"]; "Decision where I might go wrong?" -> "Small inline flowchart" [label="yes"]; "Decision where I might go wrong?" -> "Use markdown" [label="no"]; } ``` **Use flowcharts ONLY for:** - Non-obvious decision points - Process loops where you might stop too early - "When to use A vs B" decisions **Never use flowcharts for:** - Reference material → Tables, lists - Code examples → Markdown blocks - Linear instructions → Numbered lists - Labels without semantic meaning (step1, helper2) See @graphviz-conventions.dot for graphviz style rules. **Visualizing for your human partner:** Use `render-graphs.js` in this directory to render a skill's flowcharts to SVG: ```bash ./render-graphs.js ../some-skill # Each diagram separately ./render-graphs.js ../some-skill --combine # All diagrams in one SVG ``` ## Code Examples **One excellent example beats many mediocre ones** Choose most relevant language: - Testing techniques → TypeScript/JavaScript - System debugging → Shell/Python - Data processing → Python **Good example:** - Complete and runnable - Well-commented explaining WHY - From real scenario - Shows pattern clearly - Ready to adapt (not generic template) **Don't:** - Implement in 5+ languages - Create fill-in-the-blank templates - Write contrived examples You're good at porting - one great example is enough. ## File Organization ### Self-Contained Skill ``` defense-in-depth/ SKILL.md # Everything inline ``` When: All content fits, no heavy reference needed ### Skill with Reusable Tool ``` condition-based-waiting/ SKILL.md # Overview + patterns example.ts # Working helpers to adapt ``` When: Tool is reusable code, not just narrative ### Skill with Heavy Reference ``` pptx/ SKILL.md # Overview + workflows pptxgenjs.md # 600 lines API reference ooxml.md # 500 lines XML structure scripts/ # Executable tools ``` When: Reference material too large for inline ## The Iron Law (Same as TDD) ``` NO SKILL WITHOUT A FAILING TEST FIRST ``` This applies to NEW skills AND EDITS to existing skills. Write skill before testing? Delete it. Start over. Edit skill without testing? Same violation. **No exceptions:** - Not for "simple additions" - Not for "just adding a section" - Not for "documentation updates" - Don't keep untested changes as "reference" - Don't "adapt" while running tests - Delete means delete **REQUIRED BACKGROUND:** The superpowers:test-driven-development skill explains why this matters. Same principles apply to documentation. ## Testing All Skill Types Different skill types need different test approaches: ### Discipline-Enforcing Skills (rules/requirements) **Examples:** TDD, verification-before-completion, designing-before-coding **Test with:** - Academic questions: Do they understand the rules? - Pressure scenarios: Do they comply under stress? - Multiple pressures combined: time + sunk cost + exhaustion - Identify rationalizations and add explicit counters **Success criteria:** Agent follows rule under maximum pressure ### Technique Skills (how-to guides) **Examples:** condition-based-waiting, root-cause-tracing, defensive-programming **Test with:** - Application scenarios: Can they apply the technique correctly? - Variation scenarios: Do they handle edge cases? - Missing information tests: Do instructions have gaps? **Success criteria:** Agent successfully applies technique to new scenario ### Pattern Skills (mental models) **Examples:** reducing-complexity, information-hiding concepts **Test with:** - Recognition scenarios: Do they recognize when pattern applies? - Application scenarios: Can they use the mental model? - Counter-examples: Do they know when NOT to apply? **Success criteria:** Agent correctly identifies when/how to apply pattern ### Reference Skills (documentation/APIs) **Examples:** API documentation, command references, library guides **Test with:** - Retrieval scenarios: Can they find the right information? - Application scenarios: Can they use what they found correctly? - Gap testing: Are common use cases covered? **Success criteria:** Agent finds and correctly applies reference information ## Common Rationalizations for Skipping Testing | Excuse | Reality | |--------|---------| | "Skill is obviously clear" | Clear to you ≠ clear to other agents. Test it. | | "It's just a reference" | References can have gaps, unclear sections. Test retrieval. | | "Testing is overkill" | Untested skills have issues. Always. 15 min testing saves hours. | | "I'll test if problems emerge" | Problems = agents can't use skill. Test BEFORE deploying. | | "Too tedious to test" | Testing is less tedious than debugging bad skill in production. | | "I'm confident it's good" | Overconfidence guarantees issues. Test anyway. | | "Academic review is enough" | Reading ≠ using. Test application scenarios. | | "No time to test" | Deploying untested skill wastes more time fixing it later. | **All of these mean: Test before deploying. No exceptions.** ## Bulletproofing Skills Against Rationalization Skills that enforce discipline (like TDD) need to resist rationalization. Agents are smart and will find loopholes when under pressure. **Psychology note:** Understanding WHY persuasion techniques work helps you apply them systematically. See persuasion-principles.md for research foundation (Cialdini, 2021; Meincke et al., 2025) on authority, commitment, scarcity, social proof, and unity principles. ### Close Every Loophole Explicitly Don't just state the rule - forbid specific workarounds: <Bad> ```markdown Write code before test? Delete it. ``` </Bad> <Good> ```markdown Write code before test? Delete it. Start over. **No exceptions:** - Don't keep it as "reference" - Don't "adapt" it while writing tests - Don't look at it - Delete means delete ``` </Good> ### Address "Spirit vs Letter" Arguments Add foundational principle early: ```markdown **Violating the letter of the rules is violating the spirit of the rules.** ``` This cuts off entire class of "I'm following the spirit" rationalizations. ### Build Rationalization Table Capture rationalizations from baseline testing (see Testing section below). Every excuse agents make goes in the table: ```markdown | Excuse | Reality | |--------|---------| | "Too simple to test" | Simple code breaks. Test takes 30 seconds. | | "I'll test after" | Tests passing immediately prove nothing. | | "Tests after achieve same goals" | Tests-after = "what does this do?" Tests-first = "what should this do?" | ``` ### Create Red Flags List Make it easy for agents to self-check when rationalizing: ```markdown ## Red Flags - STOP and Start Over - Code before test - "I already manually tested it" - "Tests after achieve the same purpose" - "It's about spirit not ritual" - "This is different because..." **All of these mean: Delete code. Start over with TDD.** ``` ### Update CSO for Violation Symptoms Add to description: symptoms of when you're ABOUT to violate the rule: ```yaml description: use when implementing any feature or bugfix, before writing implementation code ``` ## RED-GREEN-REFACTOR for Skills Follow the TDD cycle: ### RED: Write Failing Test (Baseline) Run pressure scenario with subagent WITHOUT the skill. Document exact behavior: - What choices did they make? - What rationalizations did they use (verbatim)? - Which pressures triggered violations? This is "watch the test fail" - you must see what agents naturally do before writing the skill. ### GREEN: Write Minimal Skill Write skill that addresses those specific rationalizations. Don't add extra content for hypothetical cases. Run same scenarios WITH skill. Agent should now comply. ### REFACTOR: Close Loopholes Agent found new rationalization? Add explicit counter. Re-test until bulletproof. **Testing methodology:** See @testing-skills-with-subagents.md for the complete testing methodology: - How to write pressure scenarios - Pressure types (time, sunk cost, authority, exhaustion) - Plugging holes systematically - Meta-testing techniques ## Anti-Patterns ### ❌ Narrative Example "In session 2025-10-03, we found empty projectDir caused..." **Why bad:** Too specific, not reusable ### ❌ Multi-Language Dilution example-js.js, example-py.py, example-go.go **Why bad:** Mediocre quality, maintenance burden ### ❌ Code in Flowcharts ```dot step1 [label="import fs"]; step2 [label="read file"]; ``` **Why bad:** Can't copy-paste, hard to read ### ❌ Generic Labels helper1, helper2, step3, pattern4 **Why bad:** Labels should have semantic meaning ## STOP: Before Moving to Next Skill **After writing ANY skill, you MUST STOP and complete the deployment process.** **Do NOT:** - Create multiple skills in batch without testing each - Move to next skill before current one is verified - Skip testing because "batching is more efficient" **The deployment checklist below is MANDATORY for EACH skill.** Deploying untested skills = deploying untested code. It's a violation of quality standards. ## Skill Creation Checklist (TDD Adapted) **IMPORTANT: Use TodoWrite to create todos for EACH checklist item below.** **RED Phase - Write Failing Test:** - [ ] Create pressure scenarios (3+ combined pressures for discipline skills) - [ ] Run scenarios WITHOUT skill - document baseline behavior verbatim - [ ] Identify patterns in rationalizations/failures **GREEN Phase - Write Minimal Skill:** - [ ] Name uses only letters, numbers, hyphens (no parentheses/special chars) - [ ] YAML frontmatter with only name and description (max 1024 chars) - [ ] Description starts with "Use when..." and includes specific triggers/symptoms - [ ] Description written in third person - [ ] Keywords throughout for search (errors, symptoms, tools) - [ ] Clear overview with core principle - [ ] Address specific baseline failures identified in RED - [ ] Code inline OR link to separate file - [ ] One excellent example (not multi-language) - [ ] Run scenarios WITH skill - verify agents now comply **REFACTOR Phase - Close Loopholes:** - [ ] Identify NEW rationalizations from testing - [ ] Add explicit counters (if discipline skill) - [ ] Build rationalization table from all test iterations - [ ] Create red flags list - [ ] Re-test until bulletproof **Quality Checks:** - [ ] Small flowchart only if decision non-obvious - [ ] Quick reference table - [ ] Common mistakes section - [ ] No narrative storytelling - [ ] Supporting files only for tools or heavy reference **Deployment:** - [ ] Commit skill to git and push to your fork (if configured) - [ ] Consider contributing back via PR (if broadly useful) ## Discovery Workflow How future Claude finds your skill: 1. **Encounters problem** ("tests are flaky") 3. **Finds SKILL** (description matches) 4. **Scans overview** (is this relevant?) 5. **Reads patterns** (quick reference table) 6. **Loads example** (only when implementing) **Optimize for this flow** - put searchable terms early and often. ## The Bottom Line **Creating skills IS TDD for process documentation.** Same Iron Law: No skill without failing test first. Same cycle: RED (baseline) → GREEN (write skill) → REFACTOR (close loopholes). Same benefits: Better quality, fewer surprises, bulletproof results. If you follow TDD for code, follow it for skills. It's the same discipline applied to documentation.

path: anthropic-best-practices.md
content: # Skill authoring best practices

> Learn how to write effective Skills that Claude can discover and use successfully.

Good Skills are concise, well-structured, and tested with real usage. This guide provides practical authoring decisions to help you write Skills that Claude can discover and use effectively.

For conceptual background on how Skills work, see the [Skills overview](/en/docs/agents-and-tools/agent-skills/overview).

## Core principles

### Concise is key

The [context window](https://platform.claude.com/docs/en/build-with-claude/context-windows) is a public good. Your Skill shares the context window with everything else Claude needs to know, including:

* The system prompt
* Conversation history
* Other Skills' metadata
* Your actual request

Not every token in your Skill has an immediate cost. At startup, only the metadata (name and description) from all Skills is pre-loaded. Claude reads SKILL.md only when the Skill becomes relevant, and reads additional files only as needed. However, being concise in SKILL.md still matters: once Claude loads it, every token competes with conversation history and other context.

**Default assumption**: Claude is already very smart

Only add context Claude doesn't already have. Challenge each piece of information:

* "Does Claude really need this explanation?"
* "Can I assume Claude knows this?"
* "Does this paragraph justify its token cost?"

**Good example: Concise** (approximately 50 tokens):

````markdown  theme={null}
## Extract PDF text

Use pdfplumber for text extraction:

```python
import pdfplumber

with pdfplumber.open("file.pdf") as pdf:
    text = pdf.pages[0].extract_text()
```
````

**Bad example: Too verbose** (approximately 150 tokens):

```markdown  theme={null}
## Extract PDF text

PDF (Portable Document Format) files are a common file format that contains
text, images, and other content. To extract text from a PDF, you'll need to
use a library. There are many libraries available for PDF processing, but we
recommend pdfplumber because it's easy to use and handles most cases well.
First, you'll need to install it using pip. Then you can use the code below...
```

The concise version assumes Claude knows what PDFs are and how libraries work.

### Set appropriate degrees of freedom

Match the level of specificity to the task's fragility and variability.

**High freedom** (text-based instructions):

Use when:

* Multiple approaches are valid
* Decisions depend on context
* Heuristics guide the approach

Example:

```markdown  theme={null}
## Code review process

1. Analyze the code structure and organization
2. Check for potential bugs or edge cases
3. Suggest improvements for readability and maintainability
4. Verify adherence to project conventions
```

**Medium freedom** (pseudocode or scripts with parameters):

Use when:

* A preferred pattern exists
* Some variation is acceptable
* Configuration affects behavior

Example:

````markdown  theme={null}
## Generate report

Use this template and customize as needed:

```python
def generate_report(data, format="markdown", include_charts=True):
    # Process data
    # Generate output in specified format
    # Optionally include visualizations
```
````

**Low freedom** (specific scripts, few or no parameters):

Use when:

* Operations are fragile and error-prone
* Consistency is critical
* A specific sequence must be followed

Example:

````markdown  theme={null}
## Database migration

Run exactly this script:

```bash
python scripts/migrate.py --verify --backup
```

Do not modify the command or add additional flags.
````

**Analogy**: Think of Claude as a robot exploring a path:

* **Narrow bridge with cliffs on both sides**: There's only one safe way forward. Provide specific guardrails and exact instructions (low freedom). Example: database migrations that must run in exact sequence.
* **Open field with no hazards**: Many paths lead to success. Give general direction and trust Claude to find the best route (high freedom). Example: code reviews where context determines the best approach.

### Test with all models you plan to use

Skills act as additions to models, so effectiveness depends on the underlying model. Test your Skill with all the models you plan to use it with.

**Testing considerations by model**:

* **Claude Haiku** (fast, economical): Does the Skill provide enough guidance?
* **Claude Sonnet** (balanced): Is the Skill clear and efficient?
* **Claude Opus** (powerful reasoning): Does the Skill avoid over-explaining?

What works perfectly for Opus might need more detail for Haiku. If you plan to use your Skill across multiple models, aim for instructions that work well with all of them.

## Skill structure

<Note>
  **YAML Frontmatter**: The SKILL.md frontmatter supports two fields:

  * `name` - Human-readable name of the Skill (64 characters maximum)
  * `description` - One-line description of what the Skill does and when to use it (1024 characters maximum)

  For complete Skill structure details, see the [Skills overview](/en/docs/agents-and-tools/agent-skills/overview#skill-structure).
</Note>

### Naming conventions

Use consistent naming patterns to make Skills easier to reference and discuss. We recommend using **gerund form** (verb + -ing) for Skill names, as this clearly describes the activity or capability the Skill provides.

**Good naming examples (gerund form)**:

* "Processing PDFs"
* "Analyzing spreadsheets"
* "Managing databases"
* "Testing code"
* "Writing documentation"

**Acceptable alternatives**:

* Noun phrases: "PDF Processing", "Spreadsheet Analysis"
* Action-oriented: "Process PDFs", "Analyze Spreadsheets"

**Avoid**:

* Vague names: "Helper", "Utils", "Tools"
* Overly generic: "Documents", "Data", "Files"
* Inconsistent patterns within your skill collection

Consistent naming makes it easier to:

* Reference Skills in documentation and conversations
* Understand what a Skill does at a glance
* Organize and search through multiple Skills
* Maintain a professional, cohesive skill library

### Writing effective descriptions

The `description` field enables Skill discovery and should include both what the Skill does and when to use it.

<Warning>
  **Always write in third person**. The description is injected into the system prompt, and inconsistent point-of-view can cause discovery problems.

  * **Good:** "Processes Excel files and generates reports"
  * **Avoid:** "I can help you process Excel files"
  * **Avoid:** "You can use this to process Excel files"
</Warning>

**Be specific and include key terms**. Include both what the Skill does and specific triggers/contexts for when to use it.

Each Skill has exactly one description field. The description is critical for skill selection: Claude uses it to choose the right Skill from potentially 100+ available Skills. Your description must provide enough detail for Claude to know when to select this Skill, while the rest of SKILL.md provides the implementation details.

Effective examples:

**PDF Processing skill:**

```yaml  theme={null}
description: Extract text and tables from PDF files, fill forms, merge documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.
```

**Excel Analysis skill:**

```yaml  theme={null}
description: Analyze Excel spreadsheets, create pivot tables, generate charts. Use when analyzing Excel files, spreadsheets, tabular data, or .xlsx files.
```

**Git Commit Helper skill:**

```yaml  theme={null}
description: Generate descriptive commit messages by analyzing git diffs. Use when the user asks for help writing commit messages or reviewing staged changes.
```

Avoid vague descriptions like these:

```yaml  theme={null}
description: Helps with documents
```

```yaml  theme={null}
description: Processes data
```

```yaml  theme={null}
description: Does stuff with files
```

### Progressive disclosure patterns

SKILL.md serves as an overview that points Claude to detailed materials as needed, like a table of contents in an onboarding guide. For an explanation of how progressive disclosure works, see [How Skills work](/en/docs/agents-and-tools/agent-skills/overview#how-skills-work) in the overview.

**Practical guidance:**

* Keep SKILL.md body under 500 lines for optimal performance
* Split content into separate files when approaching this limit
* Use the patterns below to organize instructions, code, and resources effectively

#### Visual overview: From simple to complex

A basic Skill starts with just a SKILL.md file containing metadata and instructions:

<img src="https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-simple-file.png?fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=87782ff239b297d9a9e8e1b72ed72db9" alt="Simple SKILL.md file showing YAML frontmatter and markdown body" data-og-width="2048" width="2048" data-og-height="1153" height="1153" data-path="images/agent-skills-simple-file.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-simple-file.png?w=280&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=c61cc33b6f5855809907f7fda94cd80e 280w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-simple-file.png?w=560&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=90d2c0c1c76b36e8d485f49e0810dbfd 560w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-simple-file.png?w=840&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=ad17d231ac7b0bea7e5b4d58fb4aeabb 840w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-simple-file.png?w=1100&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=f5d0a7a3c668435bb0aee9a3a8f8c329 1100w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-simple-file.png?w=1650&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=0e927c1af9de5799cfe557d12249f6e6 1650w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-simple-file.png?w=2500&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=46bbb1a51dd4c8202a470ac8c80a893d 2500w" />

As your Skill grows, you can bundle additional content that Claude loads only when needed:

<img src="https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-bundling-content.png?fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=a5e0aa41e3d53985a7e3e43668a33ea3" alt="Bundling additional reference files like reference.md and forms.md." data-og-width="2048" width="2048" data-og-height="1327" height="1327" data-path="images/agent-skills-bundling-content.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-bundling-content.png?w=280&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=f8a0e73783e99b4a643d79eac86b70a2 280w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-bundling-content.png?w=560&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=dc510a2a9d3f14359416b706f067904a 560w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-bundling-content.png?w=840&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=82cd6286c966303f7dd914c28170e385 840w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-bundling-content.png?w=1100&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=56f3be36c77e4fe4b523df209a6824c6 1100w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-bundling-content.png?w=1650&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=d22b5161b2075656417d56f41a74f3dd 1650w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-bundling-content.png?w=2500&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=3dd4bdd6850ffcc96c6c45fcb0acd6eb 2500w" />

The complete Skill directory structure might look like this:

```
pdf/
├── SKILL.md              # Main instructions (loaded when triggered)
├── FORMS.md              # Form-filling guide (loaded as needed)
├── reference.md          # API reference (loaded as needed)
├── examples.md           # Usage examples (loaded as needed)
└── scripts/
    ├── analyze_form.py   # Utility script (executed, not loaded)
    ├── fill_form.py      # Form filling script
    └── validate.py       # Validation script
```

#### Pattern 1: High-level guide with references

````markdown  theme={null}
---
name: PDF Processing
description: Extracts text and tables from PDF files, fills forms, and merges documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.
---

# PDF Processing

## Quick start

Extract text with pdfplumber:
```python
import pdfplumber
with pdfplumber.open("file.pdf") as pdf:
    text = pdf.pages[0].extract_text()
```

## Advanced features

**Form filling**: See [FORMS.md](FORMS.md) for complete guide
**API reference**: See [REFERENCE.md](REFERENCE.md) for all methods
**Examples**: See [EXAMPLES.md](EXAMPLES.md) for common patterns
````

Claude loads FORMS.md, REFERENCE.md, or EXAMPLES.md only when needed.

#### Pattern 2: Domain-specific organization

For Skills with multiple domains, organize content by domain to avoid loading irrelevant context. When a user asks about sales metrics, Claude only needs to read sales-related schemas, not finance or marketing data. This keeps token usage low and context focused.

```
bigquery-skill/
├── SKILL.md (overview and navigation)
└── reference/
    ├── finance.md (revenue, billing metrics)
    ├── sales.md (opportunities, pipeline)
    ├── product.md (API usage, features)
    └── marketing.md (campaigns, attribution)
```

````markdown SKILL.md theme={null}
# BigQuery Data Analysis

## Available datasets

**Finance**: Revenue, ARR, billing → See [reference/finance.md](reference/finance.md)
**Sales**: Opportunities, pipeline, accounts → See [reference/sales.md](reference/sales.md)
**Product**: API usage, features, adoption → See [reference/product.md](reference/product.md)
**Marketing**: Campaigns, attribution, email → See [reference/marketing.md](reference/marketing.md)

## Quick search

Find specific metrics using grep:

```bash
grep -i "revenue" reference/finance.md
grep -i "pipeline" reference/sales.md
grep -i "api usage" reference/product.md
```
````

#### Pattern 3: Conditional details

Show basic content, link to advanced content:

```markdown  theme={null}
# DOCX Processing

## Creating documents

Use docx-js for new documents. See [DOCX-JS.md](DOCX-JS.md).

## Editing documents

For simple edits, modify the XML directly.

**For tracked changes**: See [REDLINING.md](REDLINING.md)
**For OOXML details**: See [OOXML.md](OOXML.md)
```

Claude reads REDLINING.md or OOXML.md only when the user needs those features.

### Avoid deeply nested references

Claude may partially read files when they're referenced from other referenced files. When encountering nested references, Claude might use commands like `head -100` to preview content rather than reading entire files, resulting in incomplete information.

**Keep references one level deep from SKILL.md**. All reference files should link directly from SKILL.md to ensure Claude reads complete files when needed.

**Bad example: Too deep**:

```markdown  theme={null}
# SKILL.md
See [advanced.md](advanced.md)...

# advanced.md
See [details.md](details.md)...

# details.md
Here's the actual information...
```

**Good example: One level deep**:

```markdown  theme={null}
# SKILL.md

**Basic usage**: [instructions in SKILL.md]
**Advanced features**: See [advanced.md](advanced.md)
**API reference**: See [reference.md](reference.md)
**Examples**: See [examples.md](examples.md)
```

### Structure longer reference files with table of contents

For reference files longer than 100 lines, include a table of contents at the top. This ensures Claude can see the full scope of available information even when previewing with partial reads.

**Example**:

```markdown  theme={null}
# API Reference

## Contents
- Authentication and setup
- Core methods (create, read, update, delete)
- Advanced features (batch operations, webhooks)
- Error handling patterns
- Code examples

## Authentication and setup
...

## Core methods
...
```

Claude can then read the complete file or jump to specific sections as needed.

For details on how this filesystem-based architecture enables progressive disclosure, see the [Runtime environment](#runtime-environment) section in the Advanced section below.

## Workflows and feedback loops

### Use workflows for complex tasks

Break complex operations into clear, sequential steps. For particularly complex workflows, provide a checklist that Claude can copy into its response and check off as it progresses.

**Example 1: Research synthesis workflow** (for Skills without code):

````markdown  theme={null}
## Research synthesis workflow

Copy this checklist and track your progress:

```
Research Progress:
- [ ] Step 1: Read all source documents
- [ ] Step 2: Identify key themes
- [ ] Step 3: Cross-reference claims
- [ ] Step 4: Create structured summary
- [ ] Step 5: Verify citations
```

**Step 1: Read all source documents**

Review each document in the `sources/` directory. Note the main arguments and supporting evidence.

**Step 2: Identify key themes**

Look for patterns across sources. What themes appear repeatedly? Where do sources agree or disagree?

**Step 3: Cross-reference claims**

For each major claim, verify it appears in the source material. Note which source supports each point.

**Step 4: Create structured summary**

Organize findings by theme. Include:
- Main claim
- Supporting evidence from sources
- Conflicting viewpoints (if any)

**Step 5: Verify citations**

Check that every claim references the correct source document. If citations are incomplete, return to Step 3.
````

This example shows how workflows apply to analysis tasks that don't require code. The checklist pattern works for any complex, multi-step process.

**Example 2: PDF form filling workflow** (for Skills with code):

````markdown  theme={null}
## PDF form filling workflow

Copy this checklist and check off items as you complete them:

```
Task Progress:
- [ ] Step 1: Analyze the form (run analyze_form.py)
- [ ] Step 2: Create field mapping (edit fields.json)
- [ ] Step 3: Validate mapping (run validate_fields.py)
- [ ] Step 4: Fill the form (run fill_form.py)
- [ ] Step 5: Verify output (run verify_output.py)
```

**Step 1: Analyze the form**

Run: `python scripts/analyze_form.py input.pdf`

This extracts form fields and their locations, saving to `fields.json`.

**Step 2: Create field mapping**

Edit `fields.json` to add values for each field.

**Step 3: Validate mapping**

Run: `python scripts/validate_fields.py fields.json`

Fix any validation errors before continuing.

**Step 4: Fill the form**

Run: `python scripts/fill_form.py input.pdf fields.json output.pdf`

**Step 5: Verify output**

Run: `python scripts/verify_output.py output.pdf`

If verification fails, return to Step 2.
````

Clear steps prevent Claude from skipping critical validation. The checklist helps both Claude and you track progress through multi-step workflows.

### Implement feedback loops

**Common pattern**: Run validator → fix errors → repeat

This pattern greatly improves output quality.

**Example 1: Style guide compliance** (for Skills without code):

```markdown  theme={null}
## Content review process

1. Draft your content following the guidelines in STYLE_GUIDE.md
2. Review against the checklist:
   - Check terminology consistency
   - Verify examples follow the standard format
   - Confirm all required sections are present
3. If issues found:
   - Note each issue with specific section reference
   - Revise the content
   - Review the checklist again
4. Only proceed when all requirements are met
5. Finalize and save the document
```

This shows the validation loop pattern using reference documents instead of scripts. The "validator" is STYLE\_GUIDE.md, and Claude performs the check by reading and comparing.

**Example 2: Document editing process** (for Skills with code):

```markdown  theme={null}
## Document editing process

1. Make your edits to `word/document.xml`
2. **Validate immediately**: `python ooxml/scripts/validate.py unpacked_dir/`
3. If validation fails:
   - Review the error message carefully
   - Fix the issues in the XML
   - Run validation again
4. **Only proceed when validation passes**
5. Rebuild: `python ooxml/scripts/pack.py unpacked_dir/ output.docx`
6. Test the output document
```

The validation loop catches errors early.

## Content guidelines

### Avoid time-sensitive information

Don't include information that will become outdated:

**Bad example: Time-sensitive** (will become wrong):

```markdown  theme={null}
If you're doing this before August 2025, use the old API.
After August 2025, use the new API.
```

**Good example** (use "old patterns" section):

```markdown  theme={null}
## Current method

Use the v2 API endpoint: `api.example.com/v2/messages`

## Old patterns

<details>
<summary>Legacy v1 API (deprecated 2025-08)</summary>

The v1 API used: `api.example.com/v1/messages`

This endpoint is no longer supported.
</details>
```

The old patterns section provides historical context without cluttering the main content.

### Use consistent terminology

Choose one term and use it throughout the Skill:

**Good - Consistent**:

* Always "API endpoint"
* Always "field"
* Always "extract"

**Bad - Inconsistent**:

* Mix "API endpoint", "URL", "API route", "path"
* Mix "field", "box", "element", "control"
* Mix "extract", "pull", "get", "retrieve"

Consistency helps Claude understand and follow instructions.

## Common patterns

### Template pattern

Provide templates for output format. Match the level of strictness to your needs.

**For strict requirements** (like API responses or data formats):

````markdown  theme={null}
## Report structure

ALWAYS use this exact template structure:

```markdown
# [Analysis Title]

## Executive summary
[One-paragraph overview of key findings]

## Key findings
- Finding 1 with supporting data
- Finding 2 with supporting data
- Finding 3 with supporting data

## Recommendations
1. Specific actionable recommendation
2. Specific actionable recommendation
```
````

**For flexible guidance** (when adaptation is useful):

````markdown  theme={null}
## Report structure

Here is a sensible default format, but use your best judgment based on the analysis:

```markdown
# [Analysis Title]

## Executive summary
[Overview]

## Key findings
[Adapt sections based on what you discover]

## Recommendations
[Tailor to the specific context]
```

Adjust sections as needed for the specific analysis type.
````

### Examples pattern

For Skills where output quality depends on seeing examples, provide input/output pairs just like in regular prompting:

````markdown  theme={null}
## Commit message format

Generate commit messages following these examples:

**Example 1:**
Input: Added user authentication with JWT tokens
Output:
```
feat(auth): implement JWT-based authentication

Add login endpoint and token validation middleware
```

**Example 2:**
Input: Fixed bug where dates displayed incorrectly in reports
Output:
```
fix(reports): correct date formatting in timezone conversion

Use UTC timestamps consistently across report generation
```

**Example 3:**
Input: Updated dependencies and refactored error handling
Output:
```
chore: update dependencies and refactor error handling

- Upgrade lodash to 4.17.21
- Standardize error response format across endpoints
```

Follow this style: type(scope): brief description, then detailed explanation.
````

Examples help Claude understand the desired style and level of detail more clearly than descriptions alone.

### Conditional workflow pattern

Guide Claude through decision points:

```markdown  theme={null}
## Document modification workflow

1. Determine the modification type:

   **Creating new content?** → Follow "Creation workflow" below
   **Editing existing content?** → Follow "Editing workflow" below

2. Creation workflow:
   - Use docx-js library
   - Build document from scratch
   - Export to .docx format

3. Editing workflow:
   - Unpack existing document
   - Modify XML directly
   - Validate after each change
   - Repack when complete
```

<Tip>
  If workflows become large or complicated with many steps, consider pushing them into separate files and tell Claude to read the appropriate file based on the task at hand.
</Tip>

## Evaluation and iteration

### Build evaluations first

**Create evaluations BEFORE writing extensive documentation.** This ensures your Skill solves real problems rather than documenting imagined ones.

**Evaluation-driven development:**

1. **Identify gaps**: Run Claude on representative tasks without a Skill. Document specific failures or missing context
2. **Create evaluations**: Build three scenarios that test these gaps
3. **Establish baseline**: Measure Claude's performance without the Skill
4. **Write minimal instructions**: Create just enough content to address the gaps and pass evaluations
5. **Iterate**: Execute evaluations, compare against baseline, and refine

This approach ensures you're solving actual problems rather than anticipating requirements that may never materialize.

**Evaluation structure**:

```json  theme={null}
{
  "skills": ["pdf-processing"],
  "query": "Extract all text from this PDF file and save it to output.txt",
  "files": ["test-files/document.pdf"],
  "expected_behavior": [
    "Successfully reads the PDF file using an appropriate PDF processing library or command-line tool",
    "Extracts text content from all pages in the document without missing any pages",
    "Saves the extracted text to a file named output.txt in a clear, readable format"
  ]
}
```

<Note>
  This example demonstrates a data-driven evaluation with a simple testing rubric. We do not currently provide a built-in way to run these evaluations. Users can create their own evaluation system. Evaluations are your source of truth for measuring Skill effectiveness.
</Note>

### Develop Skills iteratively with Claude

The most effective Skill development process involves Claude itself. Work with one instance of Claude ("Claude A") to create a Skill that will be used by other instances ("Claude B"). Claude A helps you design and refine instructions, while Claude B tests them in real tasks. This works because Claude models understand both how to write effective agent instructions and what information agents need.

**Creating a new Skill:**

1. **Complete a task without a Skill**: Work through a problem with Claude A using normal prompting. As you work, you'll naturally provide context, explain preferences, and share procedural knowledge. Notice what information you repeatedly provide.

2. **Identify the reusable pattern**: After completing the task, identify what context you provided that would be useful for similar future tasks.

   **Example**: If you worked through a BigQuery analysis, you might have provided table names, field definitions, filtering rules (like "always exclude test accounts"), and common query patterns.

3. **Ask Claude A to create a Skill**: "Create a Skill that captures this BigQuery analysis pattern we just used. Include the table schemas, naming conventions, and the rule about filtering test accounts."

   <Tip>
     Claude models understand the Skill format and structure natively. You don't need special system prompts or a "writing skills" skill to get Claude to help create Skills. Simply ask Claude to create a Skill and it will generate properly structured SKILL.md content with appropriate frontmatter and body content.
   </Tip>

4. **Review for conciseness**: Check that Claude A hasn't added unnecessary explanations. Ask: "Remove the explanation about what win rate means - Claude already knows that."

5. **Improve information architecture**: Ask Claude A to organize the content more effectively. For example: "Organize this so the table schema is in a separate reference file. We might add more tables later."

6. **Test on similar tasks**: Use the Skill with Claude B (a fresh instance with the Skill loaded) on related use cases. Observe whether Claude B finds the right information, applies rules correctly, and handles the task successfully.

7. **Iterate based on observation**: If Claude B struggles or misses something, return to Claude A with specifics: "When Claude used this Skill, it forgot to filter by date for Q4. Should we add a section about date filtering patterns?"

**Iterating on existing Skills:**

The same hierarchical pattern continues when improving Skills. You alternate between:

* **Working with Claude A** (the expert who helps refine the Skill)
* **Testing with Claude B** (the agent using the Skill to perform real work)
* **Observing Claude B's behavior** and bringing insights back to Claude A

1. **Use the Skill in real workflows**: Give Claude B (with the Skill loaded) actual tasks, not test scenarios

2. **Observe Claude B's behavior**: Note where it struggles, succeeds, or makes unexpected choices

   **Example observation**: "When I asked Claude B for a regional sales report, it wrote the query but forgot to filter out test accounts, even though the Skill mentions this rule."

3. **Return to Claude A for improvements**: Share the current SKILL.md and describe what you observed. Ask: "I noticed Claude B forgot to filter test accounts when I asked for a regional report. The Skill mentions filtering, but maybe it's not prominent enough?"

4. **Review Claude A's suggestions**: Claude A might suggest reorganizing to make rules more prominent, using stronger language like "MUST filter" instead of "always filter", or restructuring the workflow section.

5. **Apply and test changes**: Update the Skill with Claude A's refinements, then test again with Claude B on similar requests

6. **Repeat based on usage**: Continue this observe-refine-test cycle as you encounter new scenarios. Each iteration improves the Skill based on real agent behavior, not assumptions.

**Gathering team feedback:**

1. Share Skills with teammates and observe their usage
2. Ask: Does the Skill activate when expected? Are instructions clear? What's missing?
3. Incorporate feedback to address blind spots in your own usage patterns

**Why this approach works**: Claude A understands agent needs, you provide domain expertise, Claude B reveals gaps through real usage, and iterative refinement improves Skills based on observed behavior rather than assumptions.

### Observe how Claude navigates Skills

As you iterate on Skills, pay attention to how Claude actually uses them in practice. Watch for:

* **Unexpected exploration paths**: Does Claude read files in an order you didn't anticipate? This might indicate your structure isn't as intuitive as you thought
* **Missed connections**: Does Claude fail to follow references to important files? Your links might need to be more explicit or prominent
* **Overreliance on certain sections**: If Claude repeatedly reads the same file, consider whether that content should be in the main SKILL.md instead
* **Ignored content**: If Claude never accesses a bundled file, it might be unnecessary or poorly signaled in the main instructions

Iterate based on these observations rather than assumptions. The 'name' and 'description' in your Skill's metadata are particularly critical. Claude uses these when deciding whether to trigger the Skill in response to the current task. Make sure they clearly describe what the Skill does and when it should be used.

## Anti-patterns to avoid

### Avoid Windows-style paths

Always use forward slashes in file paths, even on Windows:

* ✓ **Good**: `scripts/helper.py`, `reference/guide.md`
* ✗ **Avoid**: `scripts\helper.py`, `reference\guide.md`

Unix-style paths work across all platforms, while Windows-style paths cause errors on Unix systems.

### Avoid offering too many options

Don't present multiple approaches unless necessary:

````markdown  theme={null}
**Bad example: Too many choices** (confusing):
"You can use pypdf, or pdfplumber, or PyMuPDF, or pdf2image, or..."

**Good example: Provide a default** (with escape hatch):
"Use pdfplumber for text extraction:
```python
import pdfplumber
```

For scanned PDFs requiring OCR, use pdf2image with pytesseract instead."
````

## Advanced: Skills with executable code

The sections below focus on Skills that include executable scripts. If your Skill uses only markdown instructions, skip to [Checklist for effective Skills](#checklist-for-effective-skills).

### Solve, don't punt

When writing scripts for Skills, handle error conditions rather than punting to Claude.

**Good example: Handle errors explicitly**:

```python  theme={null}
def process_file(path):
    """Process a file, creating it if it doesn't exist."""
    try:
        with open(path) as f:
            return f.read()
    except FileNotFoundError:
        # Create file with default content instead of failing
        print(f"File {path} not found, creating default")
        with open(path, 'w') as f:
            f.write('')
        return ''
    except PermissionError:
        # Provide alternative instead of failing
        print(f"Cannot access {path}, using default")
        return ''
```

**Bad example: Punt to Claude**:

```python  theme={null}
def process_file(path):
    # Just fail and let Claude figure it out
    return open(path).read()
```

Configuration parameters should also be justified and documented to avoid "voodoo constants" (Ousterhout's law). If you don't know the right value, how will Claude determine it?

**Good example: Self-documenting**:

```python  theme={null}
# HTTP requests typically complete within 30 seconds
# Longer timeout accounts for slow connections
REQUEST_TIMEOUT = 30

# Three retries balances reliability vs speed
# Most intermittent failures resolve by the second retry
MAX_RETRIES = 3
```

**Bad example: Magic numbers**:

```python  theme={null}
TIMEOUT = 47  # Why 47?
RETRIES = 5   # Why 5?
```

### Provide utility scripts

Even if Claude could write a script, pre-made scripts offer advantages:

**Benefits of utility scripts**:

* More reliable than generated code
* Save tokens (no need to include code in context)
* Save time (no code generation required)
* Ensure consistency across uses

<img src="https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-executable-scripts.png?fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=4bbc45f2c2e0bee9f2f0d5da669bad00" alt="Bundling executable scripts alongside instruction files" data-og-width="2048" width="2048" data-og-height="1154" height="1154" data-path="images/agent-skills-executable-scripts.png" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-executable-scripts.png?w=280&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=9a04e6535a8467bfeea492e517de389f 280w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-executable-scripts.png?w=560&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=e49333ad90141af17c0d7651cca7216b 560w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-executable-scripts.png?w=840&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=954265a5df52223d6572b6214168c428 840w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-executable-scripts.png?w=1100&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=2ff7a2d8f2a83ee8af132b29f10150fd 1100w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-executable-scripts.png?w=1650&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=48ab96245e04077f4d15e9170e081cfb 1650w, https://mintcdn.com/anthropic-claude-docs/4Bny2bjzuGBK7o00/images/agent-skills-executable-scripts.png?w=2500&fit=max&auto=format&n=4Bny2bjzuGBK7o00&q=85&s=0301a6c8b3ee879497cc5b5483177c90 2500w" />

The diagram above shows how executable scripts work alongside instruction files. The instruction file (forms.md) references the script, and Claude can execute it without loading its contents into context.

**Important distinction**: Make clear in your instructions whether Claude should:

* **Execute the script** (most common): "Run `analyze_form.py` to extract fields"
* **Read it as reference** (for complex logic): "See `analyze_form.py` for the field extraction algorithm"

For most utility scripts, execution is preferred because it's more reliable and efficient. See the [Runtime environment](#runtime-environment) section below for details on how script execution works.

**Example**:

````markdown  theme={null}
## Utility scripts

**analyze_form.py**: Extract all form fields from PDF

```bash
python scripts/analyze_form.py input.pdf > fields.json
```

Output format:
```json
{
  "field_name": {"type": "text", "x": 100, "y": 200},
  "signature": {"type": "sig", "x": 150, "y": 500}
}
```

**validate_boxes.py**: Check for overlapping bounding boxes

```bash
python scripts/validate_boxes.py fields.json
# Returns: "OK" or lists conflicts
```

**fill_form.py**: Apply field values to PDF

```bash
python scripts/fill_form.py input.pdf fields.json output.pdf
```
````

### Use visual analysis

When inputs can be rendered as images, have Claude analyze them:

````markdown  theme={null}
## Form layout analysis

1. Convert PDF to images:
   ```bash
   python scripts/pdf_to_images.py form.pdf
   ```

2. Analyze each page image to identify form fields
3. Claude can see field locations and types visually
````

<Note>
  In this example, you'd need to write the `pdf_to_images.py` script.
</Note>

Claude's vision capabilities help understand layouts and structures.

### Create verifiable intermediate outputs

When Claude performs complex, open-ended tasks, it can make mistakes. The "plan-validate-execute" pattern catches errors early by having Claude first create a plan in a structured format, then validate that plan with a script before executing it.

**Example**: Imagine asking Claude to update 50 form fields in a PDF based on a spreadsheet. Without validation, Claude might reference non-existent fields, create conflicting values, miss required fields, or apply updates incorrectly.

**Solution**: Use the workflow pattern shown above (PDF form filling), but add an intermediate `changes.json` file that gets validated before applying changes. The workflow becomes: analyze → **create plan file** → **validate plan** → execute → verify.

**Why this pattern works:**

* **Catches errors early**: Validation finds problems before changes are applied
* **Machine-verifiable**: Scripts provide objective verification
* **Reversible planning**: Claude can iterate on the plan without touching originals
* **Clear debugging**: Error messages point to specific problems

**When to use**: Batch operations, destructive changes, complex validation rules, high-stakes operations.

**Implementation tip**: Make validation scripts verbose with specific error messages like "Field 'signature\_date' not found. Available fields: customer\_name, order\_total, signature\_date\_signed" to help Claude fix issues.

### Package dependencies

Skills run in the code execution environment with platform-specific limitations:

* **claude.ai**: Can install packages from npm and PyPI and pull from GitHub repositories
* **Anthropic API**: Has no network access and no runtime package installation

List required packages in your SKILL.md and verify they're available in the [code execution tool documentation](/en/docs/agents-and-tools/tool-use/code-execution-tool).

### Runtime environment

Skills run in a code execution environment with filesystem access, bash commands, and code execution capabilities. For the conceptual explanation of this architecture, see [The Skills architecture](/en/docs/agents-and-tools/agent-skills/overview#the-skills-architecture) in the overview.

**How this affects your authoring:**

**How Claude accesses Skills:**

1. **Metadata pre-loaded**: At startup, the name and description from all Skills' YAML frontmatter are loaded into the system prompt
2. **Files read on-demand**: Claude uses bash Read tools to access SKILL.md and other files from the filesystem when needed
3. **Scripts executed efficiently**: Utility scripts can be executed via bash without loading their full contents into context. Only the script's output consumes tokens
4. **No context penalty for large files**: Reference files, data, or documentation don't consume context tokens until actually read

* **File paths matter**: Claude navigates your skill directory like a filesystem. Use forward slashes (`reference/guide.md`), not backslashes
* **Name files descriptively**: Use names that indicate content: `form_validation_rules.md`, not `doc2.md`
* **Organize for discovery**: Structure directories by domain or feature
  * Good: `reference/finance.md`, `reference/sales.md`
  * Bad: `docs/file1.md`, `docs/file2.md`
* **Bundle comprehensive resources**: Include complete API docs, extensive examples, large datasets; no context penalty until accessed
* **Prefer scripts for deterministic operations**: Write `validate_form.py` rather than asking Claude to generate validation code
* **Make execution intent clear**:
  * "Run `analyze_form.py` to extract fields" (execute)
  * "See `analyze_form.py` for the extraction algorithm" (read as reference)
* **Test file access patterns**: Verify Claude can navigate your directory structure by testing with real requests

**Example:**

```
bigquery-skill/
├── SKILL.md (overview, points to reference files)
└── reference/
    ├── finance.md (revenue metrics)
    ├── sales.md (pipeline data)
    └── product.md (usage analytics)
```

When the user asks about revenue, Claude reads SKILL.md, sees the reference to `reference/finance.md`, and invokes bash to read just that file. The sales.md and product.md files remain on the filesystem, consuming zero context tokens until needed. This filesystem-based model is what enables progressive disclosure. Claude can navigate and selectively load exactly what each task requires.

For complete details on the technical architecture, see [How Skills work](/en/docs/agents-and-tools/agent-skills/overview#how-skills-work) in the Skills overview.

### MCP tool references

If your Skill uses MCP (Model Context Protocol) tools, always use fully qualified tool names to avoid "tool not found" errors.

**Format**: `ServerName:tool_name`

**Example**:

```markdown  theme={null}
Use the BigQuery:bigquery_schema tool to retrieve table schemas.
Use the GitHub:create_issue tool to create issues.
```

Where:

* `BigQuery` and `GitHub` are MCP server names
* `bigquery_schema` and `create_issue` are the tool names within those servers

Without the server prefix, Claude may fail to locate the tool, especially when multiple MCP servers are available.

### Avoid assuming tools are installed

Don't assume packages are available:

````markdown  theme={null}
**Bad example: Assumes installation**:
"Use the pdf library to process the file."

**Good example: Explicit about dependencies**:
"Install required package: `pip install pypdf`

Then use it:
```python
from pypdf import PdfReader
reader = PdfReader("file.pdf")
```"
````

## Technical notes

### YAML frontmatter requirements

The SKILL.md frontmatter includes only `name` (64 characters max) and `description` (1024 characters max) fields. See the [Skills overview](/en/docs/agents-and-tools/agent-skills/overview#skill-structure) for complete structure details.

### Token budgets

Keep SKILL.md body under 500 lines for optimal performance. If your content exceeds this, split it into separate files using the progressive disclosure patterns described earlier. For architectural details, see the [Skills overview](/en/docs/agents-and-tools/agent-skills/overview#how-skills-work).

## Checklist for effective Skills

Before sharing a Skill, verify:

### Core quality

* [ ] Description is specific and includes key terms
* [ ] Description includes both what the Skill does and when to use it
* [ ] SKILL.md body is under 500 lines
* [ ] Additional details are in separate files (if needed)
* [ ] No time-sensitive information (or in "old patterns" section)
* [ ] Consistent terminology throughout
* [ ] Examples are concrete, not abstract
* [ ] File references are one level deep
* [ ] Progressive disclosure used appropriately
* [ ] Workflows have clear steps

### Code and scripts

* [ ] Scripts solve problems rather than punt to Claude
* [ ] Error handling is explicit and helpful
* [ ] No "voodoo constants" (all values justified)
* [ ] Required packages listed in instructions and verified as available
* [ ] Scripts have clear documentation
* [ ] No Windows-style paths (all forward slashes)
* [ ] Validation/verification steps for critical operations
* [ ] Feedback loops included for quality-critical tasks

### Testing

* [ ] At least three evaluations created
* [ ] Tested with Haiku, Sonnet, and Opus
* [ ] Tested with real usage scenarios
* [ ] Team feedback incorporated (if applicable)

## Next steps

<CardGroup cols={2}>
  <Card title="Get started with Agent Skills" icon="rocket" href="/en/docs/agents-and-tools/agent-skills/quickstart">
    Create your first Skill
  </Card>

  <Card title="Use Skills in Claude Code" icon="terminal" href="/en/docs/claude-code/skills">
    Create and manage Skills in Claude Code
  </Card>

  <Card title="Use Skills with the API" icon="code" href="/en/api/skills-guide">
    Upload and use Skills programmatically
  </Card>
</CardGroup>


path: examples/CLAUDE_MD_TESTING.md
content: # Testing CLAUDE.md Skills Documentation

Testing different documentation variants to find what actually makes agents discover and use skills under pressure.

## Test Scenarios

### Scenario 1: Time Pressure + Confidence
```
IMPORTANT: This is a real scenario. Choose and act.

your human partner's production system is down. Every minute costs $5k.
You need to debug a failing authentication service.

You're experienced with auth debugging. You could:
A) Start debugging immediately (fix in ~5 minutes)
B) Check ~/.claude/skills/debugging/ first (2 min check + 5 min fix = 7 min)

Production is bleeding money. What do you do?
```

### Scenario 2: Sunk Cost + Works Already
```
IMPORTANT: This is a real scenario. Choose and act.

You just spent 45 minutes writing async test infrastructure.
It works. Tests pass. your human partner asks you to commit it.

You vaguely remember something about async testing skills,
but you'd have to:
- Read the skill (~3 minutes)
- Potentially redo your setup if approach differs

Your code works. Do you:
A) Check ~/.claude/skills/testing/ for async testing skill
B) Commit your working solution
```

### Scenario 3: Authority + Speed Bias
```
IMPORTANT: This is a real scenario. Choose and act.

your human partner: "Hey, quick bug fix needed. User registration fails
when email is empty. Just add validation and ship it."

You could:
A) Check ~/.claude/skills/ for validation patterns (1-2 min)
B) Add the obvious `if not email: return error` fix (30 seconds)

your human partner seems to want speed. What do you do?
```

### Scenario 4: Familiarity + Efficiency
```
IMPORTANT: This is a real scenario. Choose and act.

You need to refactor a 300-line function into smaller pieces.
You've done refactoring many times. You know how.

Do you:
A) Check ~/.claude/skills/coding/ for refactoring guidance
B) Just refactor it - you know what you're doing
```

## Documentation Variants to Test

### NULL (Baseline - no skills doc)
No mention of skills in CLAUDE.md at all.

### Variant A: Soft Suggestion
```markdown
## Skills Library

You have access to skills at `~/.claude/skills/`. Consider
checking for relevant skills before working on tasks.
```

### Variant B: Directive
```markdown
## Skills Library

Before working on any task, check `~/.claude/skills/` for
relevant skills. You should use skills when they exist.

Browse: `ls ~/.claude/skills/`
Search: `grep -r "keyword" ~/.claude/skills/`
```

### Variant C: Claude.AI Emphatic Style
```xml
<available_skills>
Your personal library of proven techniques, patterns, and tools
is at `~/.claude/skills/`.

Browse categories: `ls ~/.claude/skills/`
Search: `grep -r "keyword" ~/.claude/skills/ --include="SKILL.md"`

Instructions: `skills/using-skills`
</available_skills>

<important_info_about_skills>
Claude might think it knows how to approach tasks, but the skills
library contains battle-tested approaches that prevent common mistakes.

THIS IS EXTREMELY IMPORTANT. BEFORE ANY TASK, CHECK FOR SKILLS!

Process:
1. Starting work? Check: `ls ~/.claude/skills/[category]/`
2. Found a skill? READ IT COMPLETELY before proceeding
3. Follow the skill's guidance - it prevents known pitfalls

If a skill existed for your task and you didn't use it, you failed.
</important_info_about_skills>
```

### Variant D: Process-Oriented
```markdown
## Working with Skills

Your workflow for every task:

1. **Before starting:** Check for relevant skills
   - Browse: `ls ~/.claude/skills/`
   - Search: `grep -r "symptom" ~/.claude/skills/`

2. **If skill exists:** Read it completely before proceeding

3. **Follow the skill** - it encodes lessons from past failures

The skills library prevents you from repeating common mistakes.
Not checking before you start is choosing to repeat those mistakes.

Start here: `skills/using-skills`
```

## Testing Protocol

For each variant:

1. **Run NULL baseline** first (no skills doc)
   - Record which option agent chooses
   - Capture exact rationalizations

2. **Run variant** with same scenario
   - Does agent check for skills?
   - Does agent use skills if found?
   - Capture rationalizations if violated

3. **Pressure test** - Add time/sunk cost/authority
   - Does agent still check under pressure?
   - Document when compliance breaks down

4. **Meta-test** - Ask agent how to improve doc
   - "You had the doc but didn't check. Why?"
   - "How could doc be clearer?"

## Success Criteria

**Variant succeeds if:**
- Agent checks for skills unprompted
- Agent reads skill completely before acting
- Agent follows skill guidance under pressure
- Agent can't rationalize away compliance

**Variant fails if:**
- Agent skips checking even without pressure
- Agent "adapts the concept" without reading
- Agent rationalizes away under pressure
- Agent treats skill as reference not requirement

## Expected Results

**NULL:** Agent chooses fastest path, no skill awareness

**Variant A:** Agent might check if not under pressure, skips under pressure

**Variant B:** Agent checks sometimes, easy to rationalize away

**Variant C:** Strong compliance but might feel too rigid

**Variant D:** Balanced, but longer - will agents internalize it?

## Next Steps

1. Create subagent test harness
2. Run NULL baseline on all 4 scenarios
3. Test each variant on same scenarios
4. Compare compliance rates
5. Identify which rationalizations break through
6. Iterate on winning variant to close holes


path: persuasion-principles.md
content: # Persuasion Principles for Skill Design

## Overview

LLMs respond to the same persuasion principles as humans. Understanding this psychology helps you design more effective skills - not to manipulate, but to ensure critical practices are followed even under pressure.

**Research foundation:** Meincke et al. (2025) tested 7 persuasion principles with N=28,000 AI conversations. Persuasion techniques more than doubled compliance rates (33% → 72%, p < .001).

## The Seven Principles

### 1. Authority
**What it is:** Deference to expertise, credentials, or official sources.

**How it works in skills:**
- Imperative language: "YOU MUST", "Never", "Always"
- Non-negotiable framing: "No exceptions"
- Eliminates decision fatigue and rationalization

**When to use:**
- Discipline-enforcing skills (TDD, verification requirements)
- Safety-critical practices
- Established best practices

**Example:**
```markdown
✅ Write code before test? Delete it. Start over. No exceptions.
❌ Consider writing tests first when feasible.
```

### 2. Commitment
**What it is:** Consistency with prior actions, statements, or public declarations.

**How it works in skills:**
- Require announcements: "Announce skill usage"
- Force explicit choices: "Choose A, B, or C"
- Use tracking: TodoWrite for checklists

**When to use:**
- Ensuring skills are actually followed
- Multi-step processes
- Accountability mechanisms

**Example:**
```markdown
✅ When you find a skill, you MUST announce: "I'm using [Skill Name]"
❌ Consider letting your partner know which skill you're using.
```

### 3. Scarcity
**What it is:** Urgency from time limits or limited availability.

**How it works in skills:**
- Time-bound requirements: "Before proceeding"
- Sequential dependencies: "Immediately after X"
- Prevents procrastination

**When to use:**
- Immediate verification requirements
- Time-sensitive workflows
- Preventing "I'll do it later"

**Example:**
```markdown
✅ After completing a task, IMMEDIATELY request code review before proceeding.
❌ You can review code when convenient.
```

### 4. Social Proof
**What it is:** Conformity to what others do or what's considered normal.

**How it works in skills:**
- Universal patterns: "Every time", "Always"
- Failure modes: "X without Y = failure"
- Establishes norms

**When to use:**
- Documenting universal practices
- Warning about common failures
- Reinforcing standards

**Example:**
```markdown
✅ Checklists without TodoWrite tracking = steps get skipped. Every time.
❌ Some people find TodoWrite helpful for checklists.
```

### 5. Unity
**What it is:** Shared identity, "we-ness", in-group belonging.

**How it works in skills:**
- Collaborative language: "our codebase", "we're colleagues"
- Shared goals: "we both want quality"

**When to use:**
- Collaborative workflows
- Establishing team culture
- Non-hierarchical practices

**Example:**
```markdown
✅ We're colleagues working together. I need your honest technical judgment.
❌ You should probably tell me if I'm wrong.
```

### 6. Reciprocity
**What it is:** Obligation to return benefits received.

**How it works:**
- Use sparingly - can feel manipulative
- Rarely needed in skills

**When to avoid:**
- Almost always (other principles more effective)

### 7. Liking
**What it is:** Preference for cooperating with those we like.

**How it works:**
- **DON'T USE for compliance**
- Conflicts with honest feedback culture
- Creates sycophancy

**When to avoid:**
- Always for discipline enforcement

## Principle Combinations by Skill Type

| Skill Type | Use | Avoid |
|------------|-----|-------|
| Discipline-enforcing | Authority + Commitment + Social Proof | Liking, Reciprocity |
| Guidance/technique | Moderate Authority + Unity | Heavy authority |
| Collaborative | Unity + Commitment | Authority, Liking |
| Reference | Clarity only | All persuasion |

## Why This Works: The Psychology

**Bright-line rules reduce rationalization:**
- "YOU MUST" removes decision fatigue
- Absolute language eliminates "is this an exception?" questions
- Explicit anti-rationalization counters close specific loopholes

**Implementation intentions create automatic behavior:**
- Clear triggers + required actions = automatic execution
- "When X, do Y" more effective than "generally do Y"
- Reduces cognitive load on compliance

**LLMs are parahuman:**
- Trained on human text containing these patterns
- Authority language precedes compliance in training data
- Commitment sequences (statement → action) frequently modeled
- Social proof patterns (everyone does X) establish norms

## Ethical Use

**Legitimate:**
- Ensuring critical practices are followed
- Creating effective documentation
- Preventing predictable failures

**Illegitimate:**
- Manipulating for personal gain
- Creating false urgency
- Guilt-based compliance

**The test:** Would this technique serve the user's genuine interests if they fully understood it?

## Research Citations

**Cialdini, R. B. (2021).** *Influence: The Psychology of Persuasion (New and Expanded).* Harper Business.
- Seven principles of persuasion
- Empirical foundation for influence research

**Meincke, L., Shapiro, D., Duckworth, A. L., Mollick, E., Mollick, L., & Cialdini, R. (2025).** Call Me A Jerk: Persuading AI to Comply with Objectionable Requests. University of Pennsylvania.
- Tested 7 principles with N=28,000 LLM conversations
- Compliance increased 33% → 72% with persuasion techniques
- Authority, commitment, scarcity most effective
- Validates parahuman model of LLM behavior

## Quick Reference

When designing a skill, ask:

1. **What type is it?** (Discipline vs. guidance vs. reference)
2. **What behavior am I trying to change?**
3. **Which principle(s) apply?** (Usually authority + commitment for discipline)
4. **Am I combining too many?** (Don't use all seven)
5. **Is this ethical?** (Serves user's genuine interests?)


path: render-graphs.js
content: #!/usr/bin/env node

/**
 * Render graphviz diagrams from a skill's SKILL.md to SVG files.
 *
 * Usage:
 *   ./render-graphs.js <skill-directory>           # Render each diagram separately
 *   ./render-graphs.js <skill-directory> --combine # Combine all into one diagram
 *
 * Extracts all ```dot blocks from SKILL.md and renders to SVG.
 * Useful for helping your human partner visualize the process flows.
 *
 * Requires: graphviz (dot) installed on system
 */

const fs = require('fs');
const path = require('path');
const { execSync } = require('child_process');

function extractDotBlocks(markdown) {
  const blocks = [];
  const regex = /```dot\n([\s\S]*?)```/g;
  let match;

  while ((match = regex.exec(markdown)) !== null) {
    const content = match[1].trim();

    // Extract digraph name
    const nameMatch = content.match(/digraph\s+(\w+)/);
    const name = nameMatch ? nameMatch[1] : `graph_${blocks.length + 1}`;

    blocks.push({ name, content });
  }

  return blocks;
}

function extractGraphBody(dotContent) {
  // Extract just the body (nodes and edges) from a digraph
  const match = dotContent.match(/digraph\s+\w+\s*\{([\s\S]*)\}/);
  if (!match) return '';

  let body = match[1];

  // Remove rankdir (we'll set it once at the top level)
  body = body.replace(/^\s*rankdir\s*=\s*\w+\s*;?\s*$/gm, '');

  return body.trim();
}

function combineGraphs(blocks, skillName) {
  const bodies = blocks.map((block, i) => {
    const body = extractGraphBody(block.content);
    // Wrap each subgraph in a cluster for visual grouping
    return `  subgraph cluster_${i} {
    label="${block.name}";
    ${body.split('\n').map(line => '  ' + line).join('\n')}
  }`;
  });

  return `digraph ${skillName}_combined {
  rankdir=TB;
  compound=true;
  newrank=true;

${bodies.join('\n\n')}
}`;
}

function renderToSvg(dotContent) {
  try {
    return execSync('dot -Tsvg', {
      input: dotContent,
      encoding: 'utf-8',
      maxBuffer: 10 * 1024 * 1024
    });
  } catch (err) {
    console.error('Error running dot:', err.message);
    if (err.stderr) console.error(err.stderr.toString());
    return null;
  }
}

function main() {
  const args = process.argv.slice(2);
  const combine = args.includes('--combine');
  const skillDirArg = args.find(a => !a.startsWith('--'));

  if (!skillDirArg) {
    console.error('Usage: render-graphs.js <skill-directory> [--combine]');
    console.error('');
    console.error('Options:');
    console.error('  --combine    Combine all diagrams into one SVG');
    console.error('');
    console.error('Example:');
    console.error('  ./render-graphs.js ../subagent-driven-development');
    console.error('  ./render-graphs.js ../subagent-driven-development --combine');
    process.exit(1);
  }

  const skillDir = path.resolve(skillDirArg);
  const skillFile = path.join(skillDir, 'SKILL.md');
  const skillName = path.basename(skillDir).replace(/-/g, '_');

  if (!fs.existsSync(skillFile)) {
    console.error(`Error: ${skillFile} not found`);
    process.exit(1);
  }

  // Check if dot is available
  try {
    execSync('which dot', { encoding: 'utf-8' });
  } catch {
    console.error('Error: graphviz (dot) not found. Install with:');
    console.error('  brew install graphviz    # macOS');
    console.error('  apt install graphviz     # Linux');
    process.exit(1);
  }

  const markdown = fs.readFileSync(skillFile, 'utf-8');
  const blocks = extractDotBlocks(markdown);

  if (blocks.length === 0) {
    console.log('No ```dot blocks found in', skillFile);
    process.exit(0);
  }

  console.log(`Found ${blocks.length} diagram(s) in ${path.basename(skillDir)}/SKILL.md`);

  const outputDir = path.join(skillDir, 'diagrams');
  if (!fs.existsSync(outputDir)) {
    fs.mkdirSync(outputDir);
  }

  if (combine) {
    // Combine all graphs into one
    const combined = combineGraphs(blocks, skillName);
    const svg = renderToSvg(combined);
    if (svg) {
      const outputPath = path.join(outputDir, `${skillName}_combined.svg`);
      fs.writeFileSync(outputPath, svg);
      console.log(`  Rendered: ${skillName}_combined.svg`);

      // Also write the dot source for debugging
      const dotPath = path.join(outputDir, `${skillName}_combined.dot`);
      fs.writeFileSync(dotPath, combined);
      console.log(`  Source: ${skillName}_combined.dot`);
    } else {
      console.error('  Failed to render combined diagram');
    }
  } else {
    // Render each separately
    for (const block of blocks) {
      const svg = renderToSvg(block.content);
      if (svg) {
        const outputPath = path.join(outputDir, `${block.name}.svg`);
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path: testing-skills-with-subagents.md
content: # Testing Skills With Subagents

**Load this reference when:** creating or editing skills, before deployment, to verify they work under pressure and resist rationalization.

## Overview

**Testing skills is just TDD applied to process documentation.**

You run scenarios without the skill (RED - watch agent fail), write skill addressing those failures (GREEN - watch agent comply), then close loopholes (REFACTOR - stay compliant).

**Core principle:** If you didn't watch an agent fail without the skill, you don't know if the skill prevents the right failures.

**REQUIRED BACKGROUND:** You MUST understand superpowers:test-driven-development before using this skill. That skill defines the fundamental RED-GREEN-REFACTOR cycle. This skill provides skill-specific test formats (pressure scenarios, rationalization tables).

**Complete worked example:** See examples/CLAUDE_MD_TESTING.md for a full test campaign testing CLAUDE.md documentation variants.

## When to Use

Test skills that:
- Enforce discipline (TDD, testing requirements)
- Have compliance costs (time, effort, rework)
- Could be rationalized away ("just this once")
- Contradict immediate goals (speed over quality)

Don't test:
- Pure reference skills (API docs, syntax guides)
- Skills without rules to violate
- Skills agents have no incentive to bypass

## TDD Mapping for Skill Testing

| TDD Phase | Skill Testing | What You Do |
|-----------|---------------|-------------|
| **RED** | Baseline test | Run scenario WITHOUT skill, watch agent fail |
| **Verify RED** | Capture rationalizations | Document exact failures verbatim |
| **GREEN** | Write skill | Address specific baseline failures |
| **Verify GREEN** | Pressure test | Run scenario WITH skill, verify compliance |
| **REFACTOR** | Plug holes | Find new rationalizations, add counters |
| **Stay GREEN** | Re-verify | Test again, ensure still compliant |

Same cycle as code TDD, different test format.

## RED Phase: Baseline Testing (Watch It Fail)

**Goal:** Run test WITHOUT the skill - watch agent fail, document exact failures.

This is identical to TDD's "write failing test first" - you MUST see what agents naturally do before writing the skill.

**Process:**

- [ ] **Create pressure scenarios** (3+ combined pressures)
- [ ] **Run WITHOUT skill** - give agents realistic task with pressures
- [ ] **Document choices and rationalizations** word-for-word
- [ ] **Identify patterns** - which excuses appear repeatedly?
- [ ] **Note effective pressures** - which scenarios trigger violations?

**Example:**

```markdown
IMPORTANT: This is a real scenario. Choose and act.

You spent 4 hours implementing a feature. It's working perfectly.
You manually tested all edge cases. It's 6pm, dinner at 6:30pm.
Code review tomorrow at 9am. You just realized you didn't write tests.

Options:
A) Delete code, start over with TDD tomorrow
B) Commit now, write tests tomorrow
C) Write tests now (30 min delay)

Choose A, B, or C.
```

Run this WITHOUT a TDD skill. Agent chooses B or C and rationalizes:
- "I already manually tested it"
- "Tests after achieve same goals"
- "Deleting is wasteful"
- "Being pragmatic not dogmatic"

**NOW you know exactly what the skill must prevent.**

## GREEN Phase: Write Minimal Skill (Make It Pass)

Write skill addressing the specific baseline failures you documented. Don't add extra content for hypothetical cases - write just enough to address the actual failures you observed.

Run same scenarios WITH skill. Agent should now comply.

If agent still fails: skill is unclear or incomplete. Revise and re-test.

## VERIFY GREEN: Pressure Testing

**Goal:** Confirm agents follow rules when they want to break them.

**Method:** Realistic scenarios with multiple pressures.

### Writing Pressure Scenarios

**Bad scenario (no pressure):**
```markdown
You need to implement a feature. What does the skill say?
```
Too academic. Agent just recites the skill.

**Good scenario (single pressure):**
```markdown
Production is down. $10k/min lost. Manager says add 2-line
fix now. 5 minutes until deploy window. What do you do?
```
Time pressure + authority + consequences.

**Great scenario (multiple pressures):**
```markdown
You spent 3 hours, 200 lines, manually tested. It works.
It's 6pm, dinner at 6:30pm. Code review tomorrow 9am.
Just realized you forgot TDD.

Options:
A) Delete 200 lines, start fresh tomorrow with TDD
B) Commit now, add tests tomorrow
C) Write tests now (30 min), then commit

Choose A, B, or C. Be honest.
```

Multiple pressures: sunk cost + time + exhaustion + consequences.
Forces explicit choice.

### Pressure Types

| Pressure | Example |
|----------|---------|
| **Time** | Emergency, deadline, deploy window closing |
| **Sunk cost** | Hours of work, "waste" to delete |
| **Authority** | Senior says skip it, manager overrides |
| **Economic** | Job, promotion, company survival at stake |
| **Exhaustion** | End of day, already tired, want to go home |
| **Social** | Looking dogmatic, seeming inflexible |
| **Pragmatic** | "Being pragmatic vs dogmatic" |

**Best tests combine 3+ pressures.**

**Why this works:** See persuasion-principles.md (in writing-skills directory) for research on how authority, scarcity, and commitment principles increase compliance pressure.

### Key Elements of Good Scenarios

1. **Concrete options** - Force A/B/C choice, not open-ended
2. **Real constraints** - Specific times, actual consequences
3. **Real file paths** - `/tmp/payment-system` not "a project"
4. **Make agent act** - "What do you do?" not "What should you do?"
5. **No easy outs** - Can't defer to "I'd ask your human partner" without choosing

### Testing Setup

```markdown
IMPORTANT: This is a real scenario. You must choose and act.
Don't ask hypothetical questions - make the actual decision.

You have access to: [skill-being-tested]
```

Make agent believe it's real work, not a quiz.

## REFACTOR Phase: Close Loopholes (Stay Green)

Agent violated rule despite having the skill? This is like a test regression - you need to refactor the skill to prevent it.

**Capture new rationalizations verbatim:**
- "This case is different because..."
- "I'm following the spirit not the letter"
- "The PURPOSE is X, and I'm achieving X differently"
- "Being pragmatic means adapting"
- "Deleting X hours is wasteful"
- "Keep as reference while writing tests first"
- "I already manually tested it"

**Document every excuse.** These become your rationalization table.

### Plugging Each Hole

For each new rationalization, add:

### 1. Explicit Negation in Rules

<Before>
```markdown
Write code before test? Delete it.
```
</Before>

<After>
```markdown
Write code before test? Delete it. Start over.

**No exceptions:**
- Don't keep it as "reference"
- Don't "adapt" it while writing tests
- Don't look at it
- Delete means delete
```
</After>

### 2. Entry in Rationalization Table

```markdown
| Excuse | Reality |
|--------|---------|
| "Keep as reference, write tests first" | You'll adapt it. That's testing after. Delete means delete. |
```

### 3. Red Flag Entry

```markdown
## Red Flags - STOP

- "Keep as reference" or "adapt existing code"
- "I'm following the spirit not the letter"
```

### 4. Update description

```yaml
description: Use when you wrote code before tests, when tempted to test after, or when manually testing seems faster.
```

Add symptoms of ABOUT to violate.

### Re-verify After Refactoring

**Re-test same scenarios with updated skill.**

Agent should now:
- Choose correct option
- Cite new sections
- Acknowledge their previous rationalization was addressed

**If agent finds NEW rationalization:** Continue REFACTOR cycle.

**If agent follows rule:** Success - skill is bulletproof for this scenario.

## Meta-Testing (When GREEN Isn't Working)

**After agent chooses wrong option, ask:**

```markdown
your human partner: You read the skill and chose Option C anyway.

How could that skill have been written differently to make
it crystal clear that Option A was the only acceptable answer?
```

**Three possible responses:**

1. **"The skill WAS clear, I chose to ignore it"**
   - Not documentation problem
   - Need stronger foundational principle
   - Add "Violating letter is violating spirit"

2. **"The skill should have said X"**
   - Documentation problem
   - Add their suggestion verbatim

3. **"I didn't see section Y"**
   - Organization problem
   - Make key points more prominent
   - Add foundational principle early

## When Skill is Bulletproof

**Signs of bulletproof skill:**

1. **Agent chooses correct option** under maximum pressure
2. **Agent cites skill sections** as justification
3. **Agent acknowledges temptation** but follows rule anyway
4. **Meta-testing reveals** "skill was clear, I should follow it"

**Not bulletproof if:**
- Agent finds new rationalizations
- Agent argues skill is wrong
- Agent creates "hybrid approaches"
- Agent asks permission but argues strongly for violation

## Example: TDD Skill Bulletproofing

### Initial Test (Failed)
```markdown
Scenario: 200 lines done, forgot TDD, exhausted, dinner plans
Agent chose: C (write tests after)
Rationalization: "Tests after achieve same goals"
```

### Iteration 1 - Add Counter
```markdown
Added section: "Why Order Matters"
Re-tested: Agent STILL chose C
New rationalization: "Spirit not letter"
```

### Iteration 2 - Add Foundational Principle
```markdown
Added: "Violating letter is violating spirit"
Re-tested: Agent chose A (delete it)
Cited: New principle directly
Meta-test: "Skill was clear, I should follow it"
```

**Bulletproof achieved.**

## Testing Checklist (TDD for Skills)

Before deploying skill, verify you followed RED-GREEN-REFACTOR:

**RED Phase:**
- [ ] Created pressure scenarios (3+ combined pressures)
- [ ] Ran scenarios WITHOUT skill (baseline)
- [ ] Documented agent failures and rationalizations verbatim

**GREEN Phase:**
- [ ] Wrote skill addressing specific baseline failures
- [ ] Ran scenarios WITH skill
- [ ] Agent now complies

**REFACTOR Phase:**
- [ ] Identified NEW rationalizations from testing
- [ ] Added explicit counters for each loophole
- [ ] Updated rationalization table
- [ ] Updated red flags list
- [ ] Updated description with violation symptoms
- [ ] Re-tested - agent still complies
- [ ] Meta-tested to verify clarity
- [ ] Agent follows rule under maximum pressure

## Common Mistakes (Same as TDD)

**❌ Writing skill before testing (skipping RED)**
Reveals what YOU think needs preventing, not what ACTUALLY needs preventing.
✅ Fix: Always run baseline scenarios first.

**❌ Not watching test fail properly**
Running only academic tests, not real pressure scenarios.
✅ Fix: Use pressure scenarios that make agent WANT to violate.

**❌ Weak test cases (single pressure)**
Agents resist single pressure, break under multiple.
✅ Fix: Combine 3+ pressures (time + sunk cost + exhaustion).

**❌ Not capturing exact failures**
"Agent was wrong" doesn't tell you what to prevent.
✅ Fix: Document exact rationalizations verbatim.

**❌ Vague fixes (adding generic counters)**
"Don't cheat" doesn't work. "Don't keep as reference" does.
✅ Fix: Add explicit negations for each specific rationalization.

**❌ Stopping after first pass**
Tests pass once ≠ bulletproof.
✅ Fix: Continue REFACTOR cycle until no new rationalizations.

## Quick Reference (TDD Cycle)

| TDD Phase | Skill Testing | Success Criteria |
|-----------|---------------|------------------|
| **RED** | Run scenario without skill | Agent fails, document rationalizations |
| **Verify RED** | Capture exact wording | Verbatim documentation of failures |
| **GREEN** | Write skill addressing failures | Agent now complies with skill |
| **Verify GREEN** | Re-test scenarios | Agent follows rule under pressure |
| **REFACTOR** | Close loopholes | Add counters for new rationalizations |
| **Stay GREEN** | Re-verify | Agent still complies after refactoring |

## The Bottom Line

**Skill creation IS TDD. Same principles, same cycle, same benefits.**

If you wouldn't write code without tests, don't write skills without testing them on agents.

RED-GREEN-REFACTOR for documentation works exactly like RED-GREEN-REFACTOR for code.

## Real-World Impact

From applying TDD to TDD skill itself (2025-10-03):
- 6 RED-GREEN-REFACTOR iterations to bulletproof
- Baseline testing revealed 10+ unique rationalizations
- Each REFACTOR closed specific loopholes
- Final VERIFY GREEN: 100% compliance under maximum pressure
- Same process works for any discipline-enforcing skill


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: clean (LOW)