Skill: anderskev/write-adr (version 1.1.2)

--- description: "Use when you want to generate Architecture Decision Records from this session. Triggers on \"write ADRs\", \"document our decisions\", \"create decision records\", \"record the choices we made\". Also useful after design discussions where decisions were reached but not documented. Does NOT extract decisions alone (use adr-decision-extraction) or provide MADR template (use adr-writing). Orchestrates the full workflow: subagent extraction, user confirmation, parallel generation, and verification." name: write-adr disable-model-invocation: true --- # Write ADR Generate Architecture Decision Records (ADRs) from decisions made during the current session. ## Workflow Overview 1. **Context** - Gather repository context and existing ADRs 2. **Extract** - Analyze conversation for decisions using a subagent 3. **Confirm** - Present decisions to user for selection 4. **Write** - Generate ADRs in parallel using subagents 5. **Report** - Summarize created files and status 6. **Verify** - Validate generated ADRs against Definition of Done ## Step 1: Gather Context ```bash # Get current branch and recent commits git branch --show-current git log --oneline -5 # Check for existing ADRs ls docs/adrs/ 2>/dev/null || echo "No ADR directory found" # Count existing ADRs for numbering find docs/adrs -name "*.md" 2>/dev/null | wc -l ``` This context helps the ADR writer: - Reference related commits in the ADR - Avoid duplicate ADRs for already-documented decisions - Determine correct sequence numbering ## Step 2: Extract Decisions Launch a subagent to analyze the current conversation for architectural decisions: ```text Task( description: "Analyze conversation and extract architectural decisions", model: "sonnet", prompt: | Load the skill: Skill(skill: "beagle-analysis:adr-decision-extraction") Analyze the conversation for decisions that warrant ADRs: - Technology choices, architecture patterns, design trade-offs - Rejected alternatives, significant implementation approaches Return JSON: { "decisions": [ { "id": 1, "title": "Use PostgreSQL for primary datastore", "context": "Brief context about why this came up", "decision": "What was decided", "alternatives": ["What was considered but rejected"], "rationale": "Why this choice was made" } ] } ) ``` If the subagent returns an empty `decisions` array, skip to Step 5 with message: "No architectural decisions detected in this session." ## Step 3: Confirm with User **Display all extracted decisions with full details**, then ask user to select: ```text ## Detected Decisions ### 1. Use PostgreSQL for primary datastore **Confidence:** high **Problem:** Need ACID transactions for financial records **Decision:** PostgreSQL for user data storage **Alternatives discussed:** - MongoDB - SQLite **Rationale:** ACID compliance, team familiarity, mature ecosystem **Source:** Discussion about database selection in planning phase --- ### 2. Implement event sourcing for audit trail **Confidence:** medium **Problem:** Compliance requires complete audit history **Decision:** Event sourcing pattern for state changes **Alternatives discussed:** - Database triggers - Application-level logging **Rationale:** Immutable audit trail, temporal queries, debugging capability **Source:** Compliance requirements discussion --- ## Selection Which decisions should I write ADRs for? - Enter numbers (e.g., "1,2" or "1-2"), "all", or "none" to skip ``` **Important:** Always display the full decision details (problem, decision, alternatives, rationale) from the extraction output BEFORE asking for selection. Do not truncate to just title and context. Parse user response: - `"all"` - Process all decisions - `"none"` or empty - Skip with message "No ADRs will be created." - `"1,2"` or `"1-2"` - Process specified decisions ## Step 4: Write ADRs (Parallel) **Pre-allocate ADR numbers before launching subagents** to prevent numbering conflicts: ```bash # Pre-allocate numbers for all confirmed decisions # Example: If user selected 3 decisions python skills/adr-writing/scripts/next_adr_number.py --count 3 # Output: # 0003 # 0004 # 0005 ``` **Assign each pre-allocated number to its corresponding decision** before launching subagents. For each confirmed decision, launch an ADR Writer subagent in background with its **pre-assigned number**: ```text Task( description: "Write ADR for: {decision.title}", model: "sonnet", run_in_background: true, prompt: | Load the skill: Skill(skill: "beagle-analysis:adr-writing") Write an ADR for this decision: ```json {decision JSON} ``` **IMPORTANT: Use this pre-assigned ADR number: {assigned_number}** Instructions: 1. Explore codebase for additional context 2. Write MADR-formatted ADR to docs/adr/ 3. Use the pre-assigned number {assigned_number} - DO NOT call next_adr_number.py 4. Filename format: {assigned_number}-slugified-title.md 5. Return created file path ) ``` **Critical:** Pass the pre-allocated number to each subagent. Subagents must NOT call `next_adr_number.py` themselves - this causes duplicate numbers when running in parallel. All subagents run in parallel. Wait for all to complete before proceeding. ## Step 5: Report Results Collect outputs from all subagents and present summary: ```markdown ## ADR Generation Complete | File | Decision | Status | |------|----------|--------| | docs/adr/0003-use-postgresql.md | Use PostgreSQL for primary datastore | Draft | ### Next Steps - Review generated ADRs for accuracy - Update status from "proposed" to "accepted" when finalized ### Gaps Requiring Investigation - [List any decisions where subagent noted missing context] ``` If no decisions were processed: ```text No ADRs were created. Run this command again after making architectural decisions. ``` ## Step 6: Verify Generated ADRs For each created ADR, validate against Definition of Done: ```markdown ## Verification Checklist | ADR | E | C | A | D | R | Status | |-----|---|---|---|---|---|--------| | 0003-use-postgresql.md | ✓ | ✓ | ✓ | ⚠ | ✗ | Incomplete | Legend: E=Evidence, C=Criteria, A=Agreement, D=Documentation, R=Realization ``` **Verification steps:** 1. Open each generated ADR file 2. Confirm filename follows `NNNN-slugified-title.md` pattern 3. **Verify YAML frontmatter exists at file start:** - File MUST begin with `---` - Contains `status: draft` (or valid status) - Contains `date: YYYY-MM-DD` (actual date) - Ends with `---` before title - If frontmatter is missing, add it immediately 4. Review for `[INVESTIGATE]` prompts - these need follow-up 5. Verify at least 2 alternatives are documented 6. Confirm consequences section has both Good and Bad items **If gaps exist:** - Keep status as `draft` until gaps are resolved - Use `[INVESTIGATE]` prompts to guide follow-up session - Schedule review with stakeholders before changing to `accepted` ## Output Location ADRs are written to `docs/adr/`. If no ADR directory exists, create it with an initial `0000-use-madr.md` template record. ## MADR Format Reference ```markdown --- status: draft date: YYYY-MM-DD --- # {TITLE} ## Context and Problem Statement {What is the issue motivating this decision?} ## Decision Drivers * {driver 1} * {driver 2} ## Decision Outcome Chosen option: "{option}", because {reason}. ### Consequences * Good, because {positive} * Bad, because {negative} ```


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