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Use for feature prioritization, user research synthesis, requirement documentation, and product strategy development.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33332,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fbusiness-marketing\u002Fproduct-manager-toolkit\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000031","产品经理","PROFESSION","适用职业",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000038","人力资源",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49],"manager","toolkit","product","product manager toolkit","Product Manager","prioritization","user research synthesis","managers","feature prioritization","go-to-market","user research","feature","strategies","rice","interview","go","analysis","templates","prd","development","documentation","customer","research","2026-11-10 10:12:30",false,"---\nname: product-manager-toolkit\ndescription: Comprehensive toolkit for product managers including RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, and go-to-market strategies. Use for feature prioritization, user research synthesis, requirement documentation, and product strategy development.\n---\n\n# Product Manager Toolkit\n\nEssential tools and frameworks for modern product management, from discovery to delivery.\n\n## Quick Start\n\n### For Feature Prioritization\n```bash\npython scripts\u002Frice_prioritizer.py sample  # Create sample CSV\npython scripts\u002Frice_prioritizer.py sample_features.csv --capacity 15\n```\n\n### For Interview Analysis\n```bash\npython scripts\u002Fcustomer_interview_analyzer.py interview_transcript.txt\n```\n\n### For PRD Creation\n1. Choose template from `references\u002Fprd_templates.md`\n2. Fill in sections based on discovery work\n3. Review with stakeholders\n4. Version control in your PM tool\n\n## Core Workflows\n\n### Feature Prioritization Process\n\n1. **Gather Feature Requests**\n   - Customer feedback\n   - Sales requests\n   - Technical debt\n   - Strategic initiatives\n\n2. **Score with RICE**\n   ```bash\n   # Create CSV with: name,reach,impact,confidence,effort\n   python scripts\u002Frice_prioritizer.py features.csv\n   ```\n   - **Reach**: Users affected per quarter\n   - **Impact**: massive\u002Fhigh\u002Fmedium\u002Flow\u002Fminimal\n   - **Confidence**: high\u002Fmedium\u002Flow\n   - **Effort**: xl\u002Fl\u002Fm\u002Fs\u002Fxs (person-months)\n\n3. **Analyze Portfolio**\n   - Review quick wins vs big bets\n   - Check effort distribution\n   - Validate against strategy\n\n4. **Generate Roadmap**\n   - Quarterly capacity planning\n   - Dependency mapping\n   - Stakeholder alignment\n\n### Customer Discovery Process\n\n1. **Conduct Interviews**\n   - Use semi-structured format\n   - Focus on problems, not solutions\n   - Record with permission\n\n2. **Analyze Insights**\n   ```bash\n   python scripts\u002Fcustomer_interview_analyzer.py transcript.txt\n   ```\n   Extracts:\n   - Pain points with severity\n   - Feature requests with priority\n   - Jobs to be done\n   - Sentiment analysis\n   - Key themes and quotes\n\n3. **Synthesize Findings**\n   - Group similar pain points\n   - Identify patterns across interviews\n   - Map to opportunity areas\n\n4. **Validate Solutions**\n   - Create solution hypotheses\n   - Test with prototypes\n   - Measure actual vs expected behavior\n\n### PRD Development Process\n\n1. **Choose Template**\n   - **Standard PRD**: Complex features (6-8 weeks)\n   - **One-Page PRD**: Simple features (2-4 weeks)\n   - **Feature Brief**: Exploration phase (1 week)\n   - **Agile Epic**: Sprint-based delivery\n\n2. **Structure Content**\n   - Problem → Solution → Success Metrics\n   - Always include out-of-scope\n   - Clear acceptance criteria\n\n3. **Collaborate**\n   - Engineering for feasibility\n   - Design for experience\n   - Sales for market validation\n   - Support for operational impact\n\n## Key Scripts\n\n### rice_prioritizer.py\nAdvanced RICE framework implementation with portfolio analysis.\n\n**Features**:\n- RICE score calculation\n- Portfolio balance analysis (quick wins vs big bets)\n- Quarterly roadmap generation\n- Team capacity planning\n- Multiple output formats (text\u002Fjson\u002Fcsv)\n\n**Usage Examples**:\n```bash\n# Basic prioritization\npython scripts\u002Frice_prioritizer.py features.csv\n\n# With custom team capacity (person-months per quarter)\npython scripts\u002Frice_prioritizer.py features.csv --capacity 20\n\n# Output as JSON for integration\npython scripts\u002Frice_prioritizer.py features.csv --output json\n```\n\n### customer_interview_analyzer.py\nNLP-based interview analysis for extracting actionable insights.\n\n**Capabilities**:\n- Pain point extraction with severity assessment\n- Feature request identification and classification\n- Jobs-to-be-done pattern recognition\n- Sentiment analysis\n- Theme extraction\n- Competitor mentions\n- Key quotes identification\n\n**Usage Examples**:\n```bash\n# Analyze single interview\npython scripts\u002Fcustomer_interview_analyzer.py interview.txt\n\n# Output as JSON for aggregation\npython scripts\u002Fcustomer_interview_analyzer.py interview.txt json\n```\n\n## Reference Documents\n\n### prd_templates.md\nMultiple PRD formats for different contexts:\n\n1. **Standard PRD Template**\n   - Comprehensive 11-section format\n   - Best for major features\n   - Includes technical specs\n\n2. **One-Page PRD**\n   - Concise format for quick alignment\n   - Focus on problem\u002Fsolution\u002Fmetrics\n   - Good for smaller features\n\n3. **Agile Epic Template**\n   - Sprint-based delivery\n   - User story mapping\n   - Acceptance criteria focus\n\n4. **Feature Brief**\n   - Lightweight exploration\n   - Hypothesis-driven\n   - Pre-PRD phase\n\n## Prioritization Frameworks\n\n### RICE Framework\n```\nScore = (Reach × Impact × Confidence) \u002F Effort\n\nReach: # of users\u002Fquarter\nImpact: \n  - Massive = 3x\n  - High = 2x\n  - Medium = 1x\n  - Low = 0.5x\n  - Minimal = 0.25x\nConfidence:\n  - High = 100%\n  - Medium = 80%\n  - Low = 50%\nEffort: Person-months\n```\n\n### Value vs Effort Matrix\n```\n         Low Effort    High Effort\n         \nHigh     QUICK WINS    BIG BETS\nValue    [Prioritize]   [Strategic]\n         \nLow      FILL-INS      TIME SINKS\nValue    [Maybe]       [Avoid]\n```\n\n### MoSCoW Method\n- **Must Have**: Critical for launch\n- **Should Have**: Important but not critical\n- **Could Have**: Nice to have\n- **Won't Have**: Out of scope\n\n## Discovery Frameworks\n\n### Customer Interview Guide\n```\n1. Context Questions (5 min)\n   - Role and responsibilities\n   - Current workflow\n   - Tools used\n\n2. Problem Exploration (15 min)\n   - Pain points\n   - Frequency and impact\n   - Current workarounds\n\n3. Solution Validation (10 min)\n   - Reaction to concepts\n   - Value perception\n   - Willingness to pay\n\n4. Wrap-up (5 min)\n   - Other thoughts\n   - Referrals\n   - Follow-up permission\n```\n\n### Hypothesis Template\n```\nWe believe that [building this feature]\nFor [these users]\nWill [achieve this outcome]\nWe'll know we're right when [metric]\n```\n\n### Opportunity Solution Tree\n```\nOutcome\n├── Opportunity 1\n│   ├── Solution A\n│   └── Solution B\n└── Opportunity 2\n    ├── Solution C\n    └── Solution D\n```\n\n## Metrics & Analytics\n\n### North Star Metric Framework\n1. **Identify Core Value**: What's the #1 value to users?\n2. **Make it Measurable**: Quantifiable and trackable\n3. **Ensure It's Actionable**: Teams can influence it\n4. **Check Leading Indicator**: Predicts business success\n\n### Funnel Analysis Template\n```\nAcquisition → Activation → Retention → Revenue → Referral\n\nKey Metrics:\n- Conversion rate at each step\n- Drop-off points\n- Time between steps\n- Cohort variations\n```\n\n### Feature Success Metrics\n- **Adoption**: % of users using feature\n- **Frequency**: Usage per user per time period\n- **Depth**: % of feature capability used\n- **Retention**: Continued usage over time\n- **Satisfaction**: NPS\u002FCSAT for feature\n\n## Best Practices\n\n### Writing Great PRDs\n1. Start with the problem, not solution\n2. Include clear success metrics upfront\n3. Explicitly state what's out of scope\n4. Use visuals (wireframes, flows)\n5. Keep technical details in appendix\n6. Version control changes\n\n### Effective Prioritization\n1. Mix quick wins with strategic bets\n2. Consider opportunity cost\n3. Account for dependencies\n4. Buffer for unexpected work (20%)\n5. Revisit quarterly\n6. Communicate decisions clearly\n\n### Customer Discovery Tips\n1. Ask \"why\" 5 times\n2. Focus on past behavior, not future intentions\n3. Avoid leading questions\n4. Interview in their environment\n5. Look for emotional reactions\n6. Validate with data\n\n### Stakeholder Management\n1. Identify RACI for decisions\n2. Regular async updates\n3. Demo over documentation\n4. Address concerns early\n5. Celebrate wins publicly\n6. Learn from failures openly\n\n## Common Pitfalls to Avoid\n\n1. **Solution-First Thinking**: Jumping to features before understanding problems\n2. **Analysis Paralysis**: Over-researching without shipping\n3. **Feature Factory**: Shipping features without measuring impact\n4. **Ignoring Technical Debt**: Not allocating time for platform health\n5. **Stakeholder Surprise**: Not communicating early and often\n6. **Metric Theater**: Optimizing vanity metrics over real value\n\n## Integration Points\n\nThis toolkit integrates with:\n- **Analytics**: Amplitude, Mixpanel, Google Analytics\n- **Roadmapping**: ProductBoard, Aha!, Roadmunk\n- **Design**: Figma, Sketch, Miro\n- **Development**: Jira, Linear, GitHub\n- **Research**: Dovetail, UserVoice, Pendo\n- **Communication**: Slack, Notion, Confluence\n\n## Quick Commands Cheat Sheet\n\n```bash\n# Prioritization\npython scripts\u002Frice_prioritizer.py features.csv --capacity 15\n\n# Interview Analysis\npython scripts\u002Fcustomer_interview_analyzer.py interview.txt\n\n# Create sample data\npython scripts\u002Frice_prioritizer.py sample\n\n# JSON outputs for integration\npython scripts\u002Frice_prioritizer.py features.csv --output json\npython scripts\u002Fcustomer_interview_analyzer.py interview.txt json\n```\n","# Product Manager Toolkit\n\nEssential tools and frameworks for modern product management, from discovery to delivery.\n\n## Quick Start\n\n### For Feature Prioritization\n```bash\npython scripts\u002Frice_prioritizer.py sample  # Create sample CSV\npython scripts\u002Frice_prioritizer.py sample_features.csv --capacity 15\n```\n\n### For Interview Analysis\n```bash\npython scripts\u002Fcustomer_interview_analyzer.py interview_transcript.txt\n```\n\n### For PRD Creation\n1. Choose template from `references\u002Fprd_templates.md`\n2. Fill in sections based on discovery work\n3. Review with stakeholders\n4. Version control in your PM tool\n\n## Core Workflows\n\n### Feature Prioritization Process\n\n1. **Gather Feature Requests**\n   - Customer feedback\n   - Sales requests\n   - Technical debt\n   - Strategic initiatives\n\n2. **Score with RICE**\n   ```bash\n   # Create CSV with: name,reach,impact,confidence,effort\n   python scripts\u002Frice_prioritizer.py features.csv\n   ```\n   - **Reach**: Users affected per quarter\n   - **Impact**: massive\u002Fhigh\u002Fmedium\u002Flow\u002Fminimal\n   - **Confidence**: high\u002Fmedium\u002Flow\n   - **Effort**: xl\u002Fl\u002Fm\u002Fs\u002Fxs (person-months)\n\n3. **Analyze Portfolio**\n   - Review quick wins vs big bets\n   - Check effort distribution\n   - Validate against strategy\n\n4. **Generate Roadmap**\n   - Quarterly capacity planning\n   - Dependency mapping\n   - Stakeholder alignment\n\n### Customer Discovery Process\n\n1. **Conduct Interviews**\n   - Use semi-structured format\n   - Focus on problems, not solutions\n   - Record with permission\n\n2. **Analyze Insights**\n   ```bash\n   python scripts\u002Fcustomer_interview_analyzer.py transcript.txt\n   ```\n   Extracts:\n   - Pain points with severity\n   - Feature requests with priority\n   - Jobs to be done\n   - Sentiment analysis\n   - Key themes and quotes\n\n3. **Synthesize Findings**\n   - Group similar pain points\n   - Identify patterns across interviews\n   - Map to opportunity areas\n\n4. **Validate Solutions**\n   - Create solution hypotheses\n   - Test with prototypes\n   - Measure actual vs expected behavior\n\n### PRD Development Process\n\n1. **Choose Template**\n   - **Standard PRD**: Complex features (6-8 weeks)\n   - **One-Page PRD**: Simple features (2-4 weeks)\n   - **Feature Brief**: Exploration phase (1 week)\n   - **Agile Epic**: Sprint-based delivery\n\n2. **Structure Content**\n   - Problem → Solution → Success Metrics\n   - Always include out-of-scope\n   - Clear acceptance criteria\n\n3. **Collaborate**\n   - Engineering for feasibility\n   - Design for experience\n   - Sales for market validation\n   - Support for operational impact\n\n## Key Scripts\n\n### rice_prioritizer.py\nAdvanced RICE framework implementation with portfolio analysis.\n\n**Features**:\n- RICE score calculation\n- Portfolio balance analysis (quick wins vs big bets)\n- Quarterly roadmap generation\n- Team capacity planning\n- Multiple output formats (text\u002Fjson\u002Fcsv)\n\n**Usage Examples**:\n```bash\n# Basic prioritization\npython scripts\u002Frice_prioritizer.py features.csv\n\n# With custom team capacity (person-months per quarter)\npython scripts\u002Frice_prioritizer.py features.csv --capacity 20\n\n# Output as JSON for integration\npython scripts\u002Frice_prioritizer.py features.csv --output json\n```\n\n### customer_interview_analyzer.py\nNLP-based interview analysis for extracting actionable insights.\n\n**Capabilities**:\n- Pain point extraction with severity assessment\n- Feature request identification and classification\n- Jobs-to-be-done pattern recognition\n- Sentiment analysis\n- Theme extraction\n- Competitor mentions\n- Key quotes identification\n\n**Usage Examples**:\n```bash\n# Analyze single interview\npython scripts\u002Fcustomer_interview_analyzer.py interview.txt\n\n# Output as JSON for aggregation\npython scripts\u002Fcustomer_interview_analyzer.py interview.txt json\n```\n\n## Reference Documents\n\n### prd_templates.md\nMultiple PRD formats for different contexts:\n\n1. **Standard PRD Template**\n   - Comprehensive 11-section format\n   - Best for major features\n   - Includes technical specs\n\n2. **One-Page PRD**\n   - Concise format for quick alignment\n   - Focus on problem\u002Fsolution\u002Fmetrics\n   - Good for smaller features\n\n3. **Agile Epic Template**\n   - Sprint-based delivery\n   - User story mapping\n   - Acceptance criteria focus\n\n4. **Feature Brief**\n   - Lightweight exploration\n   - Hypothesis-driven\n   - Pre-PRD phase\n\n## Prioritization Frameworks\n\n### RICE Framework\n```\nScore = (Reach × Impact × Confidence) \u002F Effort\n\nReach: # of users\u002Fquarter\nImpact: \n  - Massive = 3x\n  - High = 2x\n  - Medium = 1x\n  - Low = 0.5x\n  - Minimal = 0.25x\nConfidence:\n  - High = 100%\n  - Medium = 80%\n  - Low = 50%\nEffort: Person-months\n```\n\n### Value vs Effort Matrix\n```\n         Low Effort    High Effort\n         \nHigh     QUICK WINS    BIG BETS\nValue    [Prioritize]   [Strategic]\n         \nLow      FILL-INS      TIME SINKS\nValue    [Maybe]       [Avoid]\n```\n\n### MoSCoW Method\n- **Must Have**: Critical for launch\n- **Should Have**: Important but not critical\n- **Could Have**: Nice to have\n- **Won't Have**: Out of scope\n\n## Discovery Frameworks\n\n### Customer Interview Guide\n```\n1. Context Questions (5 min)\n   - Role and responsibilities\n   - Current workflow\n   - Tools used\n\n2. Problem Exploration (15 min)\n   - Pain points\n   - Frequency and impact\n   - Current workarounds\n\n3. Solution Validation (10 min)\n   - Reaction to concepts\n   - Value perception\n   - Willingness to pay\n\n4. Wrap-up (5 min)\n   - Other thoughts\n   - Referrals\n   - Follow-up permission\n```\n\n### Hypothesis Template\n```\nWe believe that [building this feature]\nFor [these users]\nWill [achieve this outcome]\nWe'll know we're right when [metric]\n```\n\n### Opportunity Solution Tree\n```\nOutcome\n├── Opportunity 1\n│   ├── Solution A\n│   └── Solution B\n└── Opportunity 2\n    ├── Solution C\n    └── Solution D\n```\n\n## Metrics & Analytics\n\n### North Star Metric Framework\n1. **Identify Core Value**: What's the #1 value to users?\n2. **Make it Measurable**: Quantifiable and trackable\n3. **Ensure It's Actionable**: Teams can influence it\n4. **Check Leading Indicator**: Predicts business success\n\n### Funnel Analysis Template\n```\nAcquisition → Activation → Retention → Revenue → Referral\n\nKey Metrics:\n- Conversion rate at each step\n- Drop-off points\n- Time between steps\n- Cohort variations\n```\n\n### Feature Success Metrics\n- **Adoption**: % of users using feature\n- **Frequency**: Usage per user per time period\n- **Depth**: % of feature capability used\n- **Retention**: Continued usage over time\n- **Satisfaction**: NPS\u002FCSAT for feature\n\n## Best Practices\n\n### Writing Great PRDs\n1. Start with the problem, not solution\n2. Include clear success metrics upfront\n3. Explicitly state what's out of scope\n4. Use visuals (wireframes, flows)\n5. Keep technical details in appendix\n6. Version control changes\n\n### Effective Prioritization\n1. Mix quick wins with strategic bets\n2. Consider opportunity cost\n3. Account for dependencies\n4. Buffer for unexpected work (20%)\n5. Revisit quarterly\n6. Communicate decisions clearly\n\n### Customer Discovery Tips\n1. Ask \"why\" 5 times\n2. Focus on past behavior, not future intentions\n3. Avoid leading questions\n4. Interview in their environment\n5. Look for emotional reactions\n6. Validate with data\n\n### Stakeholder Management\n1. Identify RACI for decisions\n2. Regular async updates\n3. Demo over documentation\n4. Address concerns early\n5. Celebrate wins publicly\n6. Learn from failures openly\n\n## Common Pitfalls to Avoid\n\n1. **Solution-First Thinking**: Jumping to features before understanding problems\n2. **Analysis Paralysis**: Over-researching without shipping\n3. **Feature Factory**: Shipping features without measuring impact\n4. **Ignoring Technical Debt**: Not allocating time for platform health\n5. **Stakeholder Surprise**: Not communicating early and often\n6. **Metric Theater**: Optimizing vanity metrics over real value\n\n## Integration Points\n\nThis toolkit integrates with:\n- **Analytics**: Amplitude, Mixpanel, Google Analytics\n- **Roadmapping**: ProductBoard, Aha!, Roadmunk\n- **Design**: Figma, Sketch, Miro\n- **Development**: Jira, Linear, GitHub\n- **Research**: Dovetail, UserVoice, Pendo\n- **Communication**: Slack, Notion, Confluence\n\n## Quick Commands Cheat Sheet\n\n```bash\n# Prioritization\npython scripts\u002Frice_prioritizer.py features.csv --capacity 15\n\n# Interview Analysis\npython scripts\u002Fcustomer_interview_analyzer.py interview.txt\n\n# Create sample data\npython scripts\u002Frice_prioritizer.py sample\n\n# JSON outputs for integration\npython scripts\u002Frice_prioritizer.py features.csv --output json\npython scripts\u002Fcustomer_interview_analyzer.py interview.txt json\n```\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fbusiness-marketing\u002Fproduct-manager-toolkit",3911,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-11 21:38:31",{"source":8,"skillName":5,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add davila7\u002Fclaude-code-templates --skill product-manager-toolkit",{"tool":71,"command":72},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill product-manager-toolkit",{"tool":74,"command":75},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill product-manager-toolkit","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fbusiness-marketing\u002Fproduct-manager-toolkit\u002FSKILL.md",true,[79,85,91],{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fprd_templates.md","REFERENCE","参考文档",7249,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fbusiness-marketing\u002Fproduct-manager-toolkit\u002Freferences\u002Fprd_templates.md",{"path":86,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":89,"sourceHtmlUrl":90},"scripts\u002Fcustomer_interview_analyzer.py","SCRIPT","脚本",17066,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fbusiness-marketing\u002Fproduct-manager-toolkit\u002Fscripts\u002Fcustomer_interview_analyzer.py",{"path":92,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":93,"sourceHtmlUrl":94},"scripts\u002Frice_prioritizer.py",11872,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fbusiness-marketing\u002Fproduct-manager-toolkit\u002Fscripts\u002Frice_prioritizer.py",[96,128,155,186,221,244],{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":107,"publishedAt":50},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[102],{"categoryNo":103,"categoryName":104,"taxonomyType":105,"taxonomyTypeDesc":106},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":135,"publishedAt":50},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[134],{"categoryNo":103,"categoryName":104,"taxonomyType":105,"taxonomyTypeDesc":106},[136,137,138,139,140,141,142,143,144,145,146,117,147,148,149,150,151,152,153,154],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":168,"publishedAt":50},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[161,164,165],{"categoryNo":162,"categoryName":163,"taxonomyType":105,"taxonomyTypeDesc":106},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":103,"categoryName":104,"taxonomyType":105,"taxonomyTypeDesc":106},{"categoryNo":166,"categoryName":167,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师",[169,170,171,172,173,174,175,113,176,177,178,179,180,181,182,116,183,184,117,185],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":187,"name":188,"nameZh":6,"description":189,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":190,"licenseFileUrl":191,"sourceHtmlUrl":192,"categories":193,"keywords":197,"publishedAt":50},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[194],{"categoryNo":195,"categoryName":196,"taxonomyType":105,"taxonomyTypeDesc":106},"01JBSEEDCAT000000000000009","前端开发",[198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,113,220],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":222,"name":223,"nameZh":6,"description":224,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":190,"licenseFileUrl":225,"sourceHtmlUrl":226,"categories":227,"keywords":232,"publishedAt":50},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[228,231],{"categoryNo":229,"categoryName":230,"taxonomyType":105,"taxonomyTypeDesc":106},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":195,"categoryName":196,"taxonomyType":105,"taxonomyTypeDesc":106},[233,234,235,236,237,199,238,208,209,239,28,240,241,113,242,119,243],"webapp testing","testing","webapp","browser","playwright","applications","debugging","logs","screenshots","local","behavior",{"skillNo":245,"name":246,"nameZh":6,"description":247,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":190,"licenseFileUrl":248,"sourceHtmlUrl":249,"categories":250,"keywords":258,"publishedAt":50},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[251,254,255],{"categoryNo":252,"categoryName":253,"taxonomyType":105,"taxonomyTypeDesc":106},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":103,"categoryName":104,"taxonomyType":105,"taxonomyTypeDesc":106},{"categoryNo":256,"categoryName":257,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000036","内容创作者",[259,260,261,262,263,264,265,266,201,267,211,147,268,269,270,271,272,181,273,117,274,43,275,276,277,125,278],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]