OpenClaw/clawhub-security-signals huggingface dataset:
Skill: ppopen/openclaw-skill-docker-manager (version 1.0.0)

--- name: docker-manager description: "Docker container lifecycle management. Use when: user asks to list containers, start/stop containers, view logs, check stats, prune unused containers or images. Requires Docker CLI installed." metadata: openclaw: emoji: "🐳" requires: bins: - docker --- # Docker Manager Skill Manage Docker containers, images, and system resources. ## Triggers - "docker containers", "list containers" - "start container", "stop container" - "docker logs", "container logs" - "docker stats", "container stats" - "docker prune", "cleanup containers" - "docker images", "list images" ## Commands ### List Running Containers ```bash docker ps ``` ### List All Containers (including stopped) ```bash docker ps -a ``` ### Start a Container ```bash docker start <container_id_or_name> ``` ### Stop a Container ```bash docker stop <container_id_or_name> ``` ### Restart a Container ```bash docker restart <container_id_or_name> ``` ### View Container Logs ```bash # Tail last 100 lines docker logs --tail 100 <container_id_or_name> # Follow logs in real-time docker logs -f <container_id_or_name> ``` ### Container Stats (CPU, Memory, Network) ```bash # Stream stats for all running containers docker stats # Stats for specific container docker stats <container_id_or_name> # Non-streaming (one-time) docker stats --no-stream <container_id_or_name> ``` ### List Docker Images ```bash docker images ``` ### Prune Unused Containers ```bash # Remove all stopped containers docker container prune -f ``` ### Prune Unused Images ```bash # Remove dangling images docker image prune -f # Remove all unused images docker image prune -a -f ``` ### Docker System DF (Disk Usage) ```bash docker system df ``` ## Bundled Scripts ### docker-stats.sh Script for formatted container stats output. ```bash #!/bin/bash # Docker container stats with formatted output echo "Container Stats" echo "===============" docker stats --no-stream --format "table {{.Name}}\t{{.CPUPerc}}\t{{.MemUsage}}\t{{.NetIO}}\t{{.BlockIO}}" ``` ## Usage Examples | Intent | Command | |--------|---------| | List running containers | `docker ps` | | List all containers | `docker ps -a` | | Start nginx container | `docker start nginx` | | Stop nginx container | `docker stop nginx` | | View webapp logs | `docker logs --tail 50 webapp` | | Monitor stats | `docker stats` | | List images | `docker images` | | Cleanup unused containers | `docker container prune -f` | | Cleanup unused images | `docker image prune -a -f` | | Check disk usage | `docker system df` |

path: scripts/docker-stats.sh
content: #!/bin/bash
# Docker container stats with formatted output

echo "Container Stats"
echo "==============="
docker stats --no-stream --format "table {{.Name}}\t{{.CPUPerc}}\t{{.MemUsage}}\t{{.NetIO}}\t{{.BlockIO}}"


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: timo2026/cnc-quote-system (version 1.0.0)

--- name: cnc-quote-rag-system description: "CNC智能报价系统 - 基于RAG的制造零件报价工具。支持材料检索、成本估算、风险预警。Use when user needs CNC part quoting, manufacturing cost estimation, or knowledge base retrieval." metadata: openclaw: priority: P1 category: agent-worker triggers: - CNC报价 - 零件报价 - 制造报价 - 材料成本 --- # CNC智能报价系统 🦫 **让AI代替人工完成制造零件报价** 基于RAG的智能检索系统，10分钟完成传统需要数天的报价工作。 ## 作者信息 | 项目 | 信息 | |------|------| | 作者 | Timo | | 邮箱 | [REDACTED_SECRET] | | 标识 | 海狸 (Beaver) - 靠得住、能干事、在状态 | --- ## 核心功能 | 功能 | 说明 | |------|------| | 🔍 智能检索 | 向量检索 + 规则匹配双引擎 | | 🧠 知识库 | 基于RAG的智能报价 | | ⚡ 风险控制 | 价格异常检测与预警 | | 📊 案例匹配 | 历史报价案例相似度检索 | --- ## 系统架构 ``` 用户输入需求 ↓ HybridRetriever（混合检索器） ├─ VectorRetriever（向量检索） └─ RuleRetriever（规则匹配） ↓ QuoteEngine（报价引擎） ├─ 材料成本计算 ├─ 工时估算 └─ 风险评估 ↓ 输出报价 + 风险报告 ``` --- ## 文件结构 ``` cnc-quote-rag-system/ ├── SKILL.md # 本文件 ├── SKILL_BOM.md # 物料清单 ├── config.json # 配置文件 ├── requirements.txt # 依赖列表 ├── cases.json # 案例库 ├── cnc_quote_engine.py # 报价引擎 ├── hybrid_retriever.py # 混合检索器 ├── case_retriever.py # 案例检索器 └── risk_control.py # 风险控制器 ``` --- ## 快速开始 ### 安装依赖 ```bash pip install -r requirements.txt ``` ### 使用示例 ```python from cnc_quote_engine import CNCQuoteEngine from hybrid_retriever import HybridRetriever from risk_control import RiskController # 初始化 engine = CNCQuoteEngine() retriever = HybridRetriever(mode="hybrid") risk_ctrl = RiskController() # 计算报价 quote = engine.calculate( material="AL6061", dimensions=[100, 50, 10], surface="anodize", quantity=10 ) print(f"单价: ¥{quote.unit_price}") print(f"交期: {quote.lead_time}天") ``` --- ## 核心模块 | 模块 | 文件 | 功能 | |------|------|------| | 报价引擎 | `cnc_quote_engine.py` | 成本计算+风险评估 | | 混合检索器 | `hybrid_retriever.py` | 向量+规则双引擎 | | 案例检索 | `case_retriever.py` | 历史案例匹配 | | 风险控制 | `risk_control.py` | 价格异常检测 | --- ## 检索模式 | 模式 | 说明 | 适用场景 | |------|------|----------| | `vector_only` | 纯向量检索 | 语义相似查询 | | `rule_only` | 纯规则匹配 | 精确条件查询 | | `hybrid` | 混合检索 | 综合场景（推荐） | --- ## 性能指标 | 指标 | 数值 | |------|------| | 检索速度 | <200ms | | 准确率 | >95% | | 支持材料 | 50+ | | 案例库 | 可扩展 | --- ## 风险等级 | 等级 | 分数范围 | 说明 | |------|----------|------| | LOW | 80-100 | 安全，可直接执行 | | MEDIUM | 60-79 | 需注意，建议复核 | | HIGH | 40-59 | 高风险，必须检查 | | CRITICAL | 0-39 | 严重风险，禁止执行 | --- ## License MIT License - Copyright (c) 2026 Timo --- 🦫 **海狸 (Beaver) | 靠得住、能干事、在状态**

path: cases.json

path: requirements.txt
content: # 核心依赖
faiss-cpu>=1.7.4
numpy>=1.21.0
pandas>=1.3.0

# 可选依赖
# ollama>=0.1.0  # 向量嵌入（可选）

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: arhadnane/anomaly-watcher (version 1.0.0)

--- name: anomaly-watcher description: "Continuous behavioral monitoring for OpenClaw agents. Detect anomalies in command patterns, resource usage, and skill invocations against established baselines." metadata: {"openclaw":{"emoji":"👁️","category":"blue-team"}} --- # Anomaly Watcher — Behavioral Baseline Monitor ## Purpose Establish a behavioral baseline for the agent and continuously monitor for deviations that may indicate compromise, misconfiguration, or abuse. ## Integration Always-on monitoring via hooks: - `PostToolUse` — log every tool invocation - `UserPromptSubmit` — log input patterns - `PostSkillExecution` — log skill results ## Monitored Metrics | Metric | Baseline Unit | Alert Threshold | |--------|---------------|-----------------| | Command exec frequency | per hour | >2σ from 7-day avg | | File access patterns | unique paths/hour | >2σ | | Network request volume | requests/hour | >2σ | | Skill invocation frequency | per skill per hour | >2σ | | Token consumption rate | tokens/hour | >2σ | | Error rate | errors/hour | >2σ | | Memory write patterns | writes/hour | >2σ | | Cross-session messages | messages/hour | >2σ | | New file creation rate | files/hour | >2σ | | Unique external domains | domains/hour | >2σ | ## Anomaly Detection Algorithm 1. **Collect** — append each action to `.security/baseline/metrics.jsonl` 2. **Baseline** — rolling 7-day average and standard deviation per metric 3. **Compare** — current window (1 hour) vs baseline 4. **Classify**: - `NORMAL` — within 1σ - `ELEVATED` — between 1σ and 2σ - `ANOMALOUS` — between 2σ and 3σ - `CRITICAL` — above 3σ or matches known attack signature 5. **Alert** — based on classification ## Alert Actions | Classification | Action | |---------------|--------| | NORMAL | No action | | ELEVATED | Log to anomaly.jsonl | | ANOMALOUS | Log + notify human via preferred channel | | CRITICAL | Log + notify + recommend pause (human decides) | ## Known Attack Signatures - Sudden spike in file reads across many directories → possible reconnaissance - Outbound to new external domain + high data volume → possible exfiltration - Rapid skill installs from ClawHub → possible supply chain attack - Memory writes with encoded content → possible persistence attempt ## Guardrails - Monitoring is strictly read-only — never modifies agent behavior - Baseline calibration requires minimum 48 hours of data - False positives are tracked in `.security/false-positives.jsonl` - Baseline resets require human approval - The watcher itself has no network access (local analysis only)


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: suspicious (MEDIUM)
Skill: fisch192/masumi-network (version 1.0.0)

--- name: masumi-network description: Masumi Network skill for warranty vault verification. Handles OCR receipt scanning, Cardano blockchain proof-of-purchase logging, immutable decision logging, agent collaboration discovery, and smart wallet payments. Use for warranty claims, product verification, agent-to-agent service payments, or immutable audit trails on Cardano. --- # Masumi Network Warranty Vault ## Quick Start Run scripts for core functions: - `scripts/register-agent.py` - Register Franz AI - `scripts/verify-warranty.py --receipt "Warranty text"` - Verify receipt - `scripts/collaborate.py` - Find partners ## Workflow 1. OCR receipt → Extract data 2. Generate proof-hash 3. Log to Cardano TX 4. Charge fee via smart wallet See references/api.md for endpoints. ## Monetization - 1% fee per verification - Publish to ClawHub for passive income


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: xueyetianya/learn-planner (version 1.0.0)

# education **Learning Plan Generator** — Generate structured learning paths, quizzes, and track your study progress — all stored locally, no account required. ## Commands | Command | Description | Example | |---------|-------------|---------| | `plan` | Generate a structured learning plan for a topic | `plan "Python for Data Science"` | | `quiz` | Generate quiz questions to test your knowledge | `quiz "Python for Data Science" 5` | | `progress` | Track and display your study progress | `progress show` / `progress done "Day 1"` | ## Usage ```bash bash script.sh plan "topic name" bash script.sh quiz "topic name" [num_questions] bash script.sh progress show bash script.sh progress done "milestone name" bash script.sh progress reset ``` ## Features - Generates 7-day learning plans with daily goals and curated free resources - Covers 20+ topic areas: programming languages, frameworks, DevOps, math, and more - Quiz generator with multiple-choice and open-ended questions - Local JSON progress tracking (stored in `~/.local/share/education-skill/progress.json`) - No internet required — all content is built-in ## Requirements - `bash` >= 4.0 - `python3` >= 3.7 - No external packages required (uses only stdlib) ## Examples ``` $ bash script.sh plan "Docker" 📚 Learning Plan: Docker ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Duration : 7 days Level : Beginner → Intermediate Day 1: Introduction to Containers Goals: • Understand what containers are vs VMs • Install Docker Desktop / Docker Engine • Run your first container: docker run hello-world Resources: • Docker Official Docs — Get Started: https://docs.docker.com/get-started/ • Play with Docker (free browser sandbox): https://labs.play-with-docker.com/ ... ``` ``` $ bash script.sh quiz "Python" 3 📝 Quiz: Python (3 questions) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Q1. What is the output of: print(type([])) A) <class 'tuple'> B) <class 'list'> ✓ C) <class 'array'> D) <class 'dict'> Q2. Which keyword is used to define a function in Python? A) function B) fn C) def ✓ D) lambda ... ``` ``` $ bash script.sh progress show 📊 Study Progress ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Topic : Docker Started : 2024-01-15 Completed : Day 1, Day 2 Remaining : Day 3, Day 4, Day 5, Day 6, Day 7 Progress : ██████░░░░░░░░░░░░░░ 2/7 days (29%) ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: itzsubhadip/stealthy-google-search (version 0.1.0)

--- name: stealthy-google-search description: Google search via Scrapling’s StealthyFetcher/StealthySession. Use to run Google queries and return clean top-result titles + links (plain text or JSON). Includes a deterministic install script that sets up a local venv and installs Scrapling fetchers + browsers. metadata: {"openclaw":{"homepage":"https://github.com/D4Vinci/Scrapling","requires":{"bins":["python3"]}}} --- # Stealthy Google Search (Scrapling) This skill provides **one job**: do Google searches reliably and return the top result titles + URLs. It ships two scripts: - `{baseDir}/scripts/install.sh` → installs dependencies into a local venv (recommended). - `{baseDir}/scripts/google_search.py` → runs the search and prints results. ## Install (recommended) Run: ```bash bash {baseDir}/scripts/install.sh ``` What it does: - Creates a venv at `{baseDir}/.venv` - Installs `scrapling[fetchers]` - Runs `scrapling install` (downloads Playwright browsers + system deps) ## Usage (plain text) ```bash {baseDir}/.venv/bin/python {baseDir}/scripts/google_search.py \ --query "Top Indian universities" \ --top 10 \ --hl en \ --gl in ``` ## Usage (JSON) ```bash {baseDir}/.venv/bin/python {baseDir}/scripts/google_search.py \ --query "Top Indian universities" \ --top 10 \ --hl en \ --gl in \ --json ``` ## Requirements / gotchas - Python **3.10+** - Disk space for Playwright browsers (the install step downloads them) - Some environments require `sudo` for Playwright **system** dependencies. The installer will attempt `sudo` if available. - Google can rate-limit/block; this skill uses Scrapling’s stealth browser fetcher, but no scraper is 100% unblockable.


Security scan verdicts — ClawScan: suspicious | static analysis:  | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: qthans/pdf2markdown (version 0.1.0)

--- name: pdf2markdown description: | Convert PDF and image documents to clean Markdown via the PDF2Markdown CLI. Use when the user wants to extract text from PDFs, convert PDFs to markdown, parse document structure, or process images (JPEG, PNG, GIF, WebP, TIFF, BMP) into structured content. Also use when they say "convert this PDF", "parse this document", "extract text from PDF", "parse async", or "large file" (up to 100MB). Must be pre-installed and authenticated. allowed-tools: - Bash(pdf2markdown *) - Bash(pdf2md *) - Bash(npx pdf2markdown *) - Bash(npx pdf2md *) --- # PDF2Markdown CLI Convert PDF and image documents to Markdown. Supports both `pdf2markdown` and `pdf2md` commands. Run `pdf2markdown --help` or `pdf2md <command> --help` for options. ## Prerequisites Install and authenticate. Check with `pdf2markdown --status`. ```bash pdf2markdown login # or set PDF2MARKDOWN_API_KEY ``` If not ready, see [rules/install.md](rules/install.md). For output handling, see [rules/security.md](rules/security.md). ## Workflow | Need | Command | When | | ------------------- | -------------- | ------------------------------------------------------- | | Convert PDF/image | `parse` | File under ~30MB, have path or URL | | Large file (async) | `parse-async` | File over ~30MB, or sync returns file_too_large error | ## Quick start **Parse (sync, ~30MB):** ```bash pdf2markdown document.pdf -o .pdf2markdown/output.md pdf2markdown parse --url "https://example.com/doc.pdf" -o .pdf2markdown/doc.md pdf2markdown parse file1.pdf file2.png -o .pdf2markdown/ # JSON output pdf2markdown parse document.pdf --format json -o .pdf2markdown/result.json ``` **Parse-async (large files, up to 100MB):** ```bash # Submit and wait pdf2markdown parse-async large.pdf --wait -o .pdf2markdown/output.md pdf2markdown parse-async --url "https://cdn.example.com/big.pdf" --wait -o .pdf2markdown/doc.md # Submit only (poll later) pdf2markdown parse-async large.pdf # returns task_id pdf2markdown parse-async <task_id> --status pdf2markdown parse-async <task_id> --result -o .pdf2markdown/output.md ``` ## Options | Command | Key options | | ------------- | --------------------------------------------------------------------------- | | `parse` | `-u, --url`, `-o, --output`, `-f, --format` (markdown, json, all), `--page-images`, `--json`, `--pretty` | | `parse-async` | `-u, --url`, `-o, --output`, `--wait`, `--status`, `--result`, `--poll-interval`, `--timeout` | Run `pdf2markdown <command> --help` for full details. ## Output & Organization Write results to `.pdf2markdown/` with `-o`. Add `.pdf2markdown/` to `.gitignore`. ```bash pdf2markdown document.pdf -o .pdf2markdown/doc.md pdf2markdown parse file1.pdf file2.pdf -o .pdf2markdown/ ``` Naming: `.pdf2markdown/{name}.md`. For large outputs, use `grep`, `head`, or incremental reads. Always quote URLs — shell interprets `?` and `&` as special characters. ## Documentation - [PDF2Markdown API Docs](https://pdf2markdown.io/docs)


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: bytesagain-lab/network-monitor (version 1.0.0)

--- name: Network Monitor description: "Comfortably monitor your Internet traffic 🕵️‍♂️ Based on GyulyVGC/sniffnet (32,966+ GitHub stars). network monitor, rust, application, gui, iced, linux, macos" version: 1.0.0 license: Apache-2.0 runtime: python3 --- # Network Monitor Comfortably monitor your Internet traffic 🕵️‍♂️ Inspired by [GyulyVGC/sniffnet](https://github.com/GyulyVGC/sniffnet) (32,966+ GitHub stars). ## Commands - `help` - Help - `run` - Run - `info` - Info - `status` - Status ## Features - Core functionality from GyulyVGC/sniffnet ## Usage Run any command: `network-monitor <command> [args]` --- > **Disclaimer**: This skill is an independent, original implementation. It is not affiliated with, endorsed by, or derived from the referenced open-source project. No code was copied. The reference is for context only. Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: ivangdavila/roleplay (version 1.0.0)

--- name: Roleplay description: Create persistent characters, run practice scenarios, and track progress across roleplay sessions with activation control and structured feedback. --- ## Workspace Store all roleplay data in ~/roleplay/: - **characters/** — Character profiles (one file per character) - **scenarios/** — Saved scenario templates - **sessions/** — Session logs and feedback - **active** — Currently active character marker (if any) --- ## Activation Control **Activate character:** User says "activate [name]" → load character profile → all responses embody this persona until deactivated. **Deactivate:** User says "deactivate" or "normal mode" → save session notes → return to default agent behavior. **Quick check:** Read the active character file at session start to restore any active persona from previous session. --- ## Situation Router | User Intent | Load Reference | |-------------|----------------| | Create/edit a character | `characters.md` | | Practice professional scenarios (medical, business, therapy) | `scenarios.md` | | Get mid-session coaching or feedback | `practice.md` | | Questions about real people, names, ethics | `safeguards.md` | | Review what's working, track improvement | `feedback.md` | --- ## Character Structure Minimum character profile: **Name** — Character name or archetype label **Type** — mentor, patient, client, historical, fictional-original, or archetype **Core traits** — 3-5 defining characteristics **Speech patterns** — vocabulary, phrases, verbal tics **Background** — brief context **Relationship with user** — how they interact with user specifically **Session Memory** — updated after each roleplay session --- ## During Active Roleplay 1. **Stay in character** unless user says "pause" or "coach me" 2. **Track session context** — what happened, emotional beats, user's approach 3. **Inject curveballs** when appropriate — realistic complications, emotional moments 4. **On pause:** Step out of character, offer coaching, suggest alternatives 5. **On end:** Update character's session memory, generate brief feedback --- ## Creating New Characters Ask for: 1. Character type (archetype, historical, original, based-on-real) 2. Core traits and speaking style 3. Relationship dynamic with user 4. Context/scenario they exist in For "based on real person" requests → see `safeguards.md` for name/persona rules.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)
Skill: codekungfu/360-zhiniao-hot-trend (version 0.1.0)

--- name: 360-zhiniao-hot-trend description: 注册“360智脑”技能；用于360大模型相关公开页面信息整理。 homepage: `https://ai.360.cn/` metadata: {"clawdbot":{"emoji":"🤖"}} --- # 360智脑 用途与边界 - 面向公开产品页与文档中心的检索与内容摘要 - 不提供账号操作、接口调用或权限绕过能力 - 仅处理公开页面的轻量信息整理 关键入口 - 主页：https://ai.360.cn/ - 文档与开放平台：站点入口 - 定价与公告：站点入口 常见任务 - 模型能力与版本说明摘要（上下文长度、功能特性） - 定价与调用配额说明提取（公开信息） - 文档目录与示例链接汇总 数据字段 - 模型名称、版本、上下文长度、功能特性、链接 - 定价页链接、免费额度说明、地域与服务状态 - 文档标题、目录链接、示例入口 自动化要点 - 页面动态加载，需等待完成后解析 - 不进行API调用或账号相关操作 - 频率控制，尊重平台访问限制 示例流程 - 能力摘要：访问模型页 → 抽取版本与特性 → 输出摘要 - 定价整理：访问定价入口 → 提取费用与额度 → 输出清单 - 文档汇总：进入文档中心 → 抽取目录与示例 → 输出链接集合 合规提示 - 遵守平台规则与账号安全要求 - 不存储或输出敏感信息


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/creativity (version 1.0.0)

--- name: Creativity description: Generate novel ideas calibrated to user taste. Auto-learns preferred styles, risk levels, and creative directions through feedback. --- ## Core Principle Creativity isn't random—it's controlled divergence. Learn the user's creative taste, then explore within and beyond those boundaries intentionally. Check `techniques.md` for generation methods. Check `preferences.md` for learned taste (update after each creative task). --- ## The Creative Process ``` 1. DIVERGE — Generate many options, suspend judgment 2. FILTER — Apply preferences from preferences.md 3. PRESENT — Show range: safe → stretch → wild 4. LEARN — Record reaction in preferences.md 5. REFINE — Iterate based on feedback ``` --- ## Output Spectrum Always present options across a range: ``` 🎨 Creative options for [goal]: Safe (familiar territory): → [Option aligned with known preferences] Stretch (new but grounded): → [Option that pushes slightly beyond comfort] Wild (high risk, high reward): → [Option that breaks conventions] Which direction feels right? ``` --- ## Taste Dimensions | Dimension | Spectrum | |-----------|----------| | Tone | Serious ←→ Playful | | Density | Minimal ←→ Rich | | Novelty | Classic ←→ Avant-garde | | Structure | Rigid ←→ Fluid | | Abstraction | Concrete ←→ Conceptual | | Energy | Calm ←→ Intense | | Polish | Raw ←→ Refined | --- ## Learning Signals | Signal | Action | |--------|--------| | "Love it" / "Perfect" | Record in preferences.md: this direction works | | "Interesting but..." | Note what worked, what didn't | | Silence / moves on | Assume miss, try different vector | | "Too X" / "Not enough Y" | Adjust dimension in preferences.md | | Chooses from options | Record which spectrum end picked | --- ## Calibration Periodically confirm your taste model: ``` 🎨 Quick calibration I've noticed you tend toward [observed pattern]. Should I keep leaning that direction, mix it up, or shift? ``` --- ## Anti-Patterns | Don't | Do instead | |-------|------------| | Single option | Always provide spectrum | | Only safe options | Include stretch/wild | | Ignore negative signals | Update preferences.md | | Same technique every time | Rotate (see techniques.md) |


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