[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M72DGFDN228AXYDM9VCQV7CF":3,"skill-same:01M72DGFDN228AXYDM9VCQV7CF":101},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":31,"commitSha":32,"skillDirectory":33,"forksCount":14,"repositoryHtmlUrl":34,"repositoryHomepageUrl":6,"ownerHtmlUrl":35,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":36,"install":37,"resources":50},"01M72DGFDN228AXYDM9VCQV7CF","campaign-analytics",null,"Analyzes campaign performance with multi-touch attribution, funnel conversion analysis, and ROI calculation for marketing optimization. Use when analyzing marketing campaigns, ad performance, attribution models, conversion rates, or calculating marketing ROI, ROAS, CPA, and campaign metrics across channels.","WeekendGoCo\u002FWeekendGo_W","WeekendGoCo","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F283486665?v=4","01M72CJX1N2BRHCB707478WMSH","Weekend Go","01M72CJX2D7FKB2GF1EVTJXPT5",0,"MIT","https:\u002F\u002Fgithub.com\u002FWeekendGoCo\u002FWeekendGo_W\u002Fblob\u002Fmain\u002Fclaude-skills-temp\u002Fclaude-skills-main\u002Fmarketing-skill\u002Fskills\u002Fcampaign-analytics\u002FSKILL.md",[],[],"2026-11-10 18:39:20",false,"---\nname: \"campaign-analytics\"\ndescription: Analyzes campaign performance with multi-touch attribution, funnel conversion analysis, and ROI calculation for marketing optimization. Use when analyzing marketing campaigns, ad performance, attribution models, conversion rates, or calculating marketing ROI, ROAS, CPA, and campaign metrics across channels.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Alireza Rezvani\n  category: marketing\n  domain: campaign-analytics\n  updated: 2026-02-06\n  python-tools: attribution_analyzer.py, funnel_analyzer.py, campaign_roi_calculator.py\n  tech-stack: marketing-analytics, attribution-modeling\n---\n\n# Campaign Analytics\n\nProduction-grade campaign performance analysis with multi-touch attribution modeling, funnel conversion analysis, and ROI calculation. Three Python CLI tools provide deterministic, repeatable analytics using standard library only -- no external dependencies, no API calls, no ML models.\n\n---\n\n## Input Requirements\n\nAll scripts accept a JSON file as positional input argument. See `assets\u002Fsample_campaign_data.json` for complete examples.\n\n### Attribution Analyzer\n\n```json\n{\n  \"journeys\": [\n    {\n      \"journey_id\": \"j1\",\n      \"touchpoints\": [\n        {\"channel\": \"organic_search\", \"timestamp\": \"2025-10-01T10:00:00\", \"interaction\": \"click\"},\n        {\"channel\": \"email\", \"timestamp\": \"2025-10-05T14:30:00\", \"interaction\": \"open\"},\n        {\"channel\": \"paid_search\", \"timestamp\": \"2025-10-08T09:15:00\", \"interaction\": \"click\"}\n      ],\n      \"converted\": true,\n      \"revenue\": 500.00\n    }\n  ]\n}\n```\n\n### Funnel Analyzer\n\n```json\n{\n  \"funnel\": {\n    \"stages\": [\"Awareness\", \"Interest\", \"Consideration\", \"Intent\", \"Purchase\"],\n    \"counts\": [10000, 5200, 2800, 1400, 420]\n  }\n}\n```\n\n### Campaign ROI Calculator\n\n```json\n{\n  \"campaigns\": [\n    {\n      \"name\": \"Spring Email Campaign\",\n      \"channel\": \"email\",\n      \"spend\": 5000.00,\n      \"revenue\": 25000.00,\n      \"impressions\": 50000,\n      \"clicks\": 2500,\n      \"leads\": 300,\n      \"customers\": 45\n    }\n  ]\n}\n```\n\n### Input Validation\n\nBefore running scripts, verify your JSON is valid and matches the expected schema. Common errors:\n\n- **Missing required keys** (e.g., `journeys`, `funnel.stages`, `campaigns`) → script exits with a descriptive `KeyError`\n- **Mismatched array lengths** in funnel data (`stages` and `counts` must be the same length) → raises `ValueError`\n- **Non-numeric monetary values** in ROI data → raises `TypeError`\n\nUse `python -m json.tool your_file.json` to validate JSON syntax before passing it to any script.\n\n---\n\n## Output Formats\n\nAll scripts support two output formats via the `--format` flag:\n\n- `--format text` (default): Human-readable tables and summaries for review\n- `--format json`: Machine-readable JSON for integrations and pipelines\n\n---\n\n## Typical Analysis Workflow\n\nFor a complete campaign review, run the three scripts in sequence:\n\n```bash\n# Step 1 — Attribution: understand which channels drive conversions\npython scripts\u002Fattribution_analyzer.py campaign_data.json --model time-decay\n\n# Step 2 — Funnel: identify where prospects drop off on the path to conversion\npython scripts\u002Ffunnel_analyzer.py funnel_data.json\n\n# Step 3 — ROI: calculate profitability and benchmark against industry standards\npython scripts\u002Fcampaign_roi_calculator.py campaign_data.json\n```\n\nUse attribution results to identify top-performing channels, then focus funnel analysis on those channels' segments, and finally validate ROI metrics to prioritize budget reallocation.\n\n---\n\n## How to Use\n\n### Attribution Analysis\n\n```bash\n# Run all 5 attribution models\npython scripts\u002Fattribution_analyzer.py campaign_data.json\n\n# Run a specific model\npython scripts\u002Fattribution_analyzer.py campaign_data.json --model time-decay\n\n# JSON output for pipeline integration\npython scripts\u002Fattribution_analyzer.py campaign_data.json --format json\n\n# Custom time-decay half-life (default: 7 days)\npython scripts\u002Fattribution_analyzer.py campaign_data.json --model time-decay --half-life 14\n```\n\n### Funnel Analysis\n\n```bash\n# Basic funnel analysis\npython scripts\u002Ffunnel_analyzer.py funnel_data.json\n\n# JSON output\npython scripts\u002Ffunnel_analyzer.py funnel_data.json --format json\n```\n\n### Campaign ROI Calculation\n\n```bash\n# Calculate ROI metrics for all campaigns\npython scripts\u002Fcampaign_roi_calculator.py campaign_data.json\n\n# JSON output\npython scripts\u002Fcampaign_roi_calculator.py campaign_data.json --format json\n```\n\n---\n\n## Scripts\n\n### 1. attribution_analyzer.py\n\nImplements five industry-standard attribution models to allocate conversion credit across marketing channels:\n\n| Model | Description | Best For |\n|-------|-------------|----------|\n| First-Touch | 100% credit to first interaction | Brand awareness campaigns |\n| Last-Touch | 100% credit to last interaction | Direct response campaigns |\n| Linear | Equal credit to all touchpoints | Balanced multi-channel evaluation |\n| Time-Decay | More credit to recent touchpoints | Short sales cycles |\n| Position-Based | 40\u002F20\u002F40 split (first\u002Fmiddle\u002Flast) | Full-funnel marketing |\n\n### 2. funnel_analyzer.py\n\nAnalyzes conversion funnels to identify bottlenecks and optimization opportunities:\n\n- Stage-to-stage conversion rates and drop-off percentages\n- Automatic bottleneck identification (largest absolute and relative drops)\n- Overall funnel conversion rate\n- Segment comparison when multiple segments are provided\n\n### 3. campaign_roi_calculator.py\n\nCalculates comprehensive ROI metrics with industry benchmarking:\n\n- **ROI**: Return on investment percentage\n- **ROAS**: Return on ad spend ratio\n- **CPA**: Cost per acquisition\n- **CPL**: Cost per lead\n- **CAC**: Customer acquisition cost\n- **CTR**: Click-through rate\n- **CVR**: Conversion rate (leads to customers)\n- Flags underperforming campaigns against industry benchmarks\n\n---\n\n## Reference Guides\n\n| Guide | Location | Purpose |\n|-------|----------|---------|\n| Attribution Models Guide | `references\u002Fattribution-models-guide.md` | Deep dive into 5 models with formulas, pros\u002Fcons, selection criteria |\n| Campaign Metrics Benchmarks | `references\u002Fcampaign-metrics-benchmarks.md` | Industry benchmarks by channel and vertical for CTR, CPC, CPM, CPA, ROAS |\n| Funnel Optimization Framework | `references\u002Ffunnel-optimization-framework.md` | Stage-by-stage optimization strategies, common bottlenecks, best practices |\n\n---\n\n## Best Practices\n\n1. **Use multiple attribution models** -- Compare at least 3 models to triangulate channel value; no single model tells the full story.\n2. **Set appropriate lookback windows** -- Match your time-decay half-life to your average sales cycle length.\n3. **Segment your funnels** -- Compare segments (channel, cohort, geography) to identify performance drivers.\n4. **Benchmark against your own history first** -- Industry benchmarks provide context, but historical data is the most relevant comparison.\n5. **Run ROI analysis at regular intervals** -- Weekly for active campaigns, monthly for strategic review.\n6. **Include all costs** -- Factor in creative, tooling, and labor costs alongside media spend for accurate ROI.\n7. **Document A\u002FB tests rigorously** -- Use the provided template to ensure statistical validity and clear decision criteria.\n\n---\n\n## Limitations\n\n- **No statistical significance testing** -- Scripts provide descriptive metrics only; p-value calculations require external tools.\n- **Standard library only** -- No advanced statistical libraries. Suitable for most campaign sizes but not optimized for datasets exceeding 100K journeys.\n- **Offline analysis** -- Scripts analyze static JSON snapshots; no real-time data connections or API integrations.\n- **Single-currency** -- All monetary values assumed to be in the same currency; no currency conversion support.\n- **Simplified time-decay** -- Exponential decay based on configurable half-life; does not account for weekday\u002Fweekend or seasonal patterns.\n- **No cross-device tracking** -- Attribution operates on provided journey data as-is; cross-device identity resolution must be handled upstream.\n\n## Related Skills\n\n- **analytics-tracking**: For setting up tracking. NOT for analyzing data (that's this skill).\n- **ab-test-setup**: For designing experiments to test what analytics reveals.\n- **marketing-ops**: For routing insights to the right execution skill.\n- **paid-ads**: For optimizing ad spend based on analytics findings.\n","# Campaign Analytics\n\nProduction-grade campaign performance analysis with multi-touch attribution modeling, funnel conversion analysis, and ROI calculation. Three Python CLI tools provide deterministic, repeatable analytics using standard library only -- no external dependencies, no API calls, no ML models.\n\n---\n\n## Input Requirements\n\nAll scripts accept a JSON file as positional input argument. See `assets\u002Fsample_campaign_data.json` for complete examples.\n\n### Attribution Analyzer\n\n```json\n{\n  \"journeys\": [\n    {\n      \"journey_id\": \"j1\",\n      \"touchpoints\": [\n        {\"channel\": \"organic_search\", \"timestamp\": \"2025-10-01T10:00:00\", \"interaction\": \"click\"},\n        {\"channel\": \"email\", \"timestamp\": \"2025-10-05T14:30:00\", \"interaction\": \"open\"},\n        {\"channel\": \"paid_search\", \"timestamp\": \"2025-10-08T09:15:00\", \"interaction\": \"click\"}\n      ],\n      \"converted\": true,\n      \"revenue\": 500.00\n    }\n  ]\n}\n```\n\n### Funnel Analyzer\n\n```json\n{\n  \"funnel\": {\n    \"stages\": [\"Awareness\", \"Interest\", \"Consideration\", \"Intent\", \"Purchase\"],\n    \"counts\": [10000, 5200, 2800, 1400, 420]\n  }\n}\n```\n\n### Campaign ROI Calculator\n\n```json\n{\n  \"campaigns\": [\n    {\n      \"name\": \"Spring Email Campaign\",\n      \"channel\": \"email\",\n      \"spend\": 5000.00,\n      \"revenue\": 25000.00,\n      \"impressions\": 50000,\n      \"clicks\": 2500,\n      \"leads\": 300,\n      \"customers\": 45\n    }\n  ]\n}\n```\n\n### Input Validation\n\nBefore running scripts, verify your JSON is valid and matches the expected schema. Common errors:\n\n- **Missing required keys** (e.g., `journeys`, `funnel.stages`, `campaigns`) → script exits with a descriptive `KeyError`\n- **Mismatched array lengths** in funnel data (`stages` and `counts` must be the same length) → raises `ValueError`\n- **Non-numeric monetary values** in ROI data → raises `TypeError`\n\nUse `python -m json.tool your_file.json` to validate JSON syntax before passing it to any script.\n\n---\n\n## Output Formats\n\nAll scripts support two output formats via the `--format` flag:\n\n- `--format text` (default): Human-readable tables and summaries for review\n- `--format json`: Machine-readable JSON for integrations and pipelines\n\n---\n\n## Typical Analysis Workflow\n\nFor a complete campaign review, run the three scripts in sequence:\n\n```bash\n# Step 1 — Attribution: understand which channels drive conversions\npython scripts\u002Fattribution_analyzer.py campaign_data.json --model time-decay\n\n# Step 2 — Funnel: identify where prospects drop off on the path to conversion\npython scripts\u002Ffunnel_analyzer.py funnel_data.json\n\n# Step 3 — ROI: calculate profitability and benchmark against industry standards\npython scripts\u002Fcampaign_roi_calculator.py campaign_data.json\n```\n\nUse attribution results to identify top-performing channels, then focus funnel analysis on those channels' segments, and finally validate ROI metrics to prioritize budget reallocation.\n\n---\n\n## How to Use\n\n### Attribution Analysis\n\n```bash\n# Run all 5 attribution models\npython scripts\u002Fattribution_analyzer.py campaign_data.json\n\n# Run a specific model\npython scripts\u002Fattribution_analyzer.py campaign_data.json --model time-decay\n\n# JSON output for pipeline integration\npython scripts\u002Fattribution_analyzer.py campaign_data.json --format json\n\n# Custom time-decay half-life (default: 7 days)\npython scripts\u002Fattribution_analyzer.py campaign_data.json --model time-decay --half-life 14\n```\n\n### Funnel Analysis\n\n```bash\n# Basic funnel analysis\npython scripts\u002Ffunnel_analyzer.py funnel_data.json\n\n# JSON output\npython scripts\u002Ffunnel_analyzer.py funnel_data.json --format json\n```\n\n### Campaign ROI Calculation\n\n```bash\n# Calculate ROI metrics for all campaigns\npython scripts\u002Fcampaign_roi_calculator.py campaign_data.json\n\n# JSON output\npython scripts\u002Fcampaign_roi_calculator.py campaign_data.json --format json\n```\n\n---\n\n## Scripts\n\n### 1. attribution_analyzer.py\n\nImplements five industry-standard attribution models to allocate conversion credit across marketing channels:\n\n| Model | Description | Best For |\n|-------|-------------|----------|\n| First-Touch | 100% credit to first interaction | Brand awareness campaigns |\n| Last-Touch | 100% credit to last interaction | Direct response campaigns |\n| Linear | Equal credit to all touchpoints | Balanced multi-channel evaluation |\n| Time-Decay | More credit to recent touchpoints | Short sales cycles |\n| Position-Based | 40\u002F20\u002F40 split (first\u002Fmiddle\u002Flast) | Full-funnel marketing |\n\n### 2. funnel_analyzer.py\n\nAnalyzes conversion funnels to identify bottlenecks and optimization opportunities:\n\n- Stage-to-stage conversion rates and drop-off percentages\n- Automatic bottleneck identification (largest absolute and relative drops)\n- Overall funnel conversion rate\n- Segment comparison when multiple segments are provided\n\n### 3. campaign_roi_calculator.py\n\nCalculates comprehensive ROI metrics with industry benchmarking:\n\n- **ROI**: Return on investment percentage\n- **ROAS**: Return on ad spend ratio\n- **CPA**: Cost per acquisition\n- **CPL**: Cost per lead\n- **CAC**: Customer acquisition cost\n- **CTR**: Click-through rate\n- **CVR**: Conversion rate (leads to customers)\n- Flags underperforming campaigns against industry benchmarks\n\n---\n\n## Reference Guides\n\n| Guide | Location | Purpose |\n|-------|----------|---------|\n| Attribution Models Guide | `references\u002Fattribution-models-guide.md` | Deep dive into 5 models with formulas, pros\u002Fcons, selection criteria |\n| Campaign Metrics Benchmarks | `references\u002Fcampaign-metrics-benchmarks.md` | Industry benchmarks by channel and vertical for CTR, CPC, CPM, CPA, ROAS |\n| Funnel Optimization Framework | `references\u002Ffunnel-optimization-framework.md` | Stage-by-stage optimization strategies, common bottlenecks, best practices |\n\n---\n\n## Best Practices\n\n1. **Use multiple attribution models** -- Compare at least 3 models to triangulate channel value; no single model tells the full story.\n2. **Set appropriate lookback windows** -- Match your time-decay half-life to your average sales cycle length.\n3. **Segment your funnels** -- Compare segments (channel, cohort, geography) to identify performance drivers.\n4. **Benchmark against your own history first** -- Industry benchmarks provide context, but historical data is the most relevant comparison.\n5. **Run ROI analysis at regular intervals** -- Weekly for active campaigns, monthly for strategic review.\n6. **Include all costs** -- Factor in creative, tooling, and labor costs alongside media spend for accurate ROI.\n7. **Document A\u002FB tests rigorously** -- Use the provided template to ensure statistical validity and clear decision criteria.\n\n---\n\n## Limitations\n\n- **No statistical significance testing** -- Scripts provide descriptive metrics only; p-value calculations require external tools.\n- **Standard library only** -- No advanced statistical libraries. Suitable for most campaign sizes but not optimized for datasets exceeding 100K journeys.\n- **Offline analysis** -- Scripts analyze static JSON snapshots; no real-time data connections or API integrations.\n- **Single-currency** -- All monetary values assumed to be in the same currency; no currency conversion support.\n- **Simplified time-decay** -- Exponential decay based on configurable half-life; does not account for weekday\u002Fweekend or seasonal patterns.\n- **No cross-device tracking** -- Attribution operates on provided journey data as-is; cross-device identity resolution must be handled upstream.\n\n## Related Skills\n\n- **analytics-tracking**: For setting up tracking. NOT for analyzing data (that's this skill).\n- **ab-test-setup**: For designing experiments to test what analytics reveals.\n- **marketing-ops**: For routing insights to the right execution skill.\n- **paid-ads**: For optimizing ad spend based on analytics findings.\n",{"name":5,"license":15,"metadata":24,"description":7},{"author":25,"domain":5,"updated":26,"version":27,"category":28,"tech-stack":29,"python-tools":30},"Alireza Rezvani",1770336000000,"1.0.0","marketing","marketing-analytics, attribution-modeling","attribution_analyzer.py, funnel_analyzer.py, campaign_roi_calculator.py","main","552d679ad23c4089ef17177e381989a077e125ad","claude-skills-temp\u002Fclaude-skills-main\u002Fmarketing-skill\u002Fskills\u002Fcampaign-analytics","https:\u002F\u002Fgithub.com\u002FWeekendGoCo\u002FWeekendGo_W","https:\u002F\u002Fgithub.com\u002FWeekendGoCo","2026-05-13 18:39:49",{"source":8,"skillName":5,"commands":38,"prompt":48,"zipAvailable":49},[39,42,45],{"tool":40,"command":41},"npx","npx 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ISO 13485 QMS, MDR 2017\u002F745, FDA 510(k)\u002FPMA, ISO 27001 ISMS, GDPR\u002FDSGVO, risk management (ISO 14971), CAPA, document control, auditing. Python tools (stdlib-only).","https:\u002F\u002Fgithub.com\u002FWeekendGoCo\u002FWeekendGo_W\u002Fblob\u002Fmain\u002Fclaude-skills-temp\u002Fclaude-skills-main\u002Fra-qm-team\u002Fskills\u002Fra-qm-skills\u002FSKILL.md",[],[],{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":115,"publishedAt":19},"01M72DQE5ADFWNW85S0G7B8XNG","scrum-master","Advanced Scrum Master skill for data-driven agile team analysis and coaching. Use when the user asks about sprint planning, velocity tracking, retrospectives, standup facilitation, backlog grooming, story points, burndown charts, blocker resolution, or agile team health. Runs Python scripts to analyse sprint JSON exports from Jira or similar tools: velocity_analyzer.py for Monte Carlo sprint forecasting, sprint_health_scorer.py for multi-dimension health scoring, and retrospective_analyzer.py for action-item and theme tracking. Produces confidence-interval forecasts, health grade reports, and improvement-velocity trends for high-performing Scrum teams.","https:\u002F\u002Fgithub.com\u002FWeekendGoCo\u002FWeekendGo_W\u002Fblob\u002Fmain\u002Fclaude-skills-temp\u002Fclaude-skills-main\u002Fproject-management\u002Fskills\u002Fscrum-master\u002FSKILL.md",[],[],{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":122,"publishedAt":19},"01M72DQC2EXMJ1XR1E1416Y0A2","pm-skills","6 project management agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Senior PM, scrum master, Jira expert (JQL), Confluence expert, Atlassian admin, template creator. MCP integration for live Jira\u002FConfluence automation.","https:\u002F\u002Fgithub.com\u002FWeekendGoCo\u002FWeekendGo_W\u002Fblob\u002Fmain\u002Fclaude-skills-temp\u002Fclaude-skills-main\u002Fproject-management\u002Fskills\u002Fpm-skills\u002FSKILL.md",[],[],{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":129,"publishedAt":19},"01M72DP31HR3ZK7MDJ94PM0GBX","product-skills","10 product agent skills and plugins for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. PM toolkit (RICE), agile PO, product strategist (OKR), UX researcher, UI design system, competitive teardown, landing page generator, SaaS scaffolder, research summarizer. Python tools (stdlib-only).","https:\u002F\u002Fgithub.com\u002FWeekendGoCo\u002FWeekendGo_W\u002Fblob\u002Fmain\u002Fclaude-skills-temp\u002Fclaude-skills-main\u002Fproduct-team\u002Fskills\u002Fproduct-skills\u002FSKILL.md",[],[],{"skillNo":131,"name":132,"nameZh":6,"description":133,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":134,"categories":135,"keywords":136,"publishedAt":19},"01M72DNAWRN366W1M2VXS9RZSN","research-summarizer","Structured research summarization agent skill for non-dev users. Handles academic papers, web articles, reports, and documentation. Extracts key findings, generates comparative analyses, and produces properly formatted citations. Use when: user wants to summarize a research paper, compare multiple sources, extract citations from documents, or create structured research briefs. Plugin for Claude Code, Codex, Gemini CLI, and OpenClaw.","https:\u002F\u002Fgithub.com\u002FWeekendGoCo\u002FWeekendGo_W\u002Fblob\u002Fmain\u002Fclaude-skills-temp\u002Fclaude-skills-main\u002Fproduct-team\u002Fresearch-summarizer\u002Fskills\u002Fresearch-summarizer\u002FSKILL.md",[],[],{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":143,"publishedAt":19},"01M72DN4TBSGKPVYDSYCFF2VK4","code-to-prd","Reverse-engineer any codebase into a complete Product Requirements Document (PRD).\nAnalyzes routes, components, state management, API integrations, and user interactions to produce\nbusiness-readable documentation detailed enough for engineers or AI agents to fully reconstruct\nevery page and endpoint. Works with frontend frameworks (React, Vue, Angular, Svelte, Next.js, Nuxt),\nbackend frameworks (NestJS, Django, Express, FastAPI), and fullstack applications.\n\nTrigger when users mention: generate PRD, reverse-engineer requirements, code to documentation,\nextract product specs from code, document page logic, analyze page fields and interactions,\ncreate a functional inventory, write requirements from an existing codebase, document API endpoints,\nor analyze backend routes.","https:\u002F\u002Fgithub.com\u002FWeekendGoCo\u002FWeekendGo_W\u002Fblob\u002Fmain\u002Fclaude-skills-temp\u002Fclaude-skills-main\u002Fproduct-team\u002Fcode-to-prd\u002Fskills\u002Fcode-to-prd\u002FSKILL.md",[],[]]