[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7A2Q3SAA4A4N38DCFNMSDWC":3,"skill-same:01M7A2Q3SAA4A4N38DCFNMSDWC":108},{"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":41,"commitSha":42,"skillDirectory":43,"forksCount":44,"repositoryHtmlUrl":45,"repositoryHomepageUrl":6,"ownerHtmlUrl":46,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":47,"install":48,"resources":61},"01M7A2Q3SAA4A4N38DCFNMSDWC","market-research",null,"Use when doing upstream market-research methodology — sizing a market as TAM\u002FSAM\u002FSOM computed BOTH top-down and bottoms-up (never a single unsourced number), planning a survey sample size with finite-population correction and per-segment minimums, or scoring candidate market segments against Kotler's measurable\u002Fsubstantial\u002Faccessible\u002Fdifferentiable\u002Factionable criteria. Outputs always show the method and the assumptions. For market-research analysts and product-marketing at the sizing\u002Fsurvey\u002Fsegmentation moment. Distinct from marketing-skill (campaign analytics, attribution, demand-gen) — this is the evidence-building methodology, not live-campaign optimization.","Heverton-web\u002Fproj_livros","Heverton-web","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F136394064?v=4","01M79STQDWSTA88J5CA9QR184V","Heverton Eduardo Peres","01M79STQEFTTYHD3AC7F2RRW5T",0,"MIT","https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fmarket-research\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: market-research\ndescription: Use when doing upstream market-research methodology — sizing a market as TAM\u002FSAM\u002FSOM computed BOTH top-down and bottoms-up (never a single unsourced number), planning a survey sample size with finite-population correction and per-segment minimums, or scoring candidate market segments against Kotler's measurable\u002Fsubstantial\u002Faccessible\u002Fdifferentiable\u002Factionable criteria. Outputs always show the method and the assumptions. For market-research analysts and product-marketing at the sizing\u002Fsurvey\u002Fsegmentation moment. Distinct from marketing-skill (campaign analytics, attribution, demand-gen) — this is the evidence-building methodology, not live-campaign optimization.\nversion: 2.9.0\nauthor: claude-code-skills\nlicense: MIT\ntags: [research-ops, market-research, tam-sam-som, market-sizing, survey, sampling, segmentation, competitive-intelligence]\ncompatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]\n---\n\n# market-research\n\nUpstream market-research methodology: market sizing, survey\u002Fsampling design, and segmentation. The discipline here is **method + assumptions**: a TAM is never a single number, a survey is never powered only in aggregate, and a segment is never a demographic slice.\n\n## Purpose\n\nMarket-research analysts, product marketers, and strategy teams need rigorous evidence *before* anyone optimizes a campaign or sets a strategy. This skill structures three methodology decisions:\n\nThree deterministic tools:\n\n1. `market_sizer.py` — Computes TAM\u002FSAM\u002FSOM by **both** top-down and bottoms-up methods side-by-side, reports the divergence, and flags failed triangulation. Never returns a single number.\n2. `sample_size_planner.py` — Survey sample size from confidence, margin of error, and expected proportion, with the finite-population correction and **per-segment minimums** (a survey powered overall is not powered per reported segment).\n3. `segmentation_scorer.py` — Scores candidate segments against Kotler's five criteria and enforces a substantiality + accessibility gate; a slice that is too small or unreachable is dropped.\n\n## When to use\n\nInvoke this skill when:\n\n- A board or exec asks \"how big is this market?\" and you need a defensible, triangulated answer.\n- You are fielding a survey and need a sample size that holds up per segment, not just overall.\n- You have a list of candidate segments and need to know which are real markets vs demographic slices.\n- You are synthesizing competitive intelligence and need a methodological backbone.\n\n**Do NOT use this skill to**: measure a live campaign (attribution, ROAS, CPA → `marketing-skill\u002Fcampaign-analytics`), build demand-gen \u002F paid-media plans (`marketing-skill\u002Fmarketing-demand-acquisition`), set positioning \u002F GTM strategy (`marketing-skill\u002Fmarketing-strategy-pmm`), or set pricing (`commercial\u002Fpricing-strategist`).\n\n## Workflow\n\n1. **Write the brief** — Fill `assets\u002Fmarket_research_brief_template.md` (objective, the decision this informs, sizing approach, sampling plan, assumptions register).\n2. **Size the market** — Run `market_sizer.py --input market.json --method both --profile {b2b-saas|consumer|enterprise|marketplace|hardware|services}`. Reconcile the top-down\u002Fbottoms-up delta before quoting anything.\n3. **Plan the survey** — Run `sample_size_planner.py --input survey.json`. Fund the per-segment floors, not just the overall n.\n4. **Score the segments** — Run `segmentation_scorer.py --input segments.json --profile \u003Csame>`. Drop segments failing the substantiality\u002Faccessibility gate.\n5. **Assemble the evidence pack** — Combine into a brief. Every number carries its method + assumptions + confidence.\n\n## Scripts\n\n| Script | Purpose | Profiles |\n|---|---|---|\n| `scripts\u002Fmarket_sizer.py` | TAM\u002FSAM\u002FSOM top-down AND bottoms-up + triangulation flag | b2b-saas, consumer, enterprise, marketplace, hardware, services |\n| `scripts\u002Fsample_size_planner.py` | Survey n + FPC + per-segment minima | n\u002Fa (parameter-driven) |\n| `scripts\u002Fsegmentation_scorer.py` | Kotler 5-criteria scoring + gate | b2b-saas, consumer, enterprise, marketplace, hardware, services |\n\nAll three: stdlib-only, `--help`, `--sample`, `--output {human,json}`.\n\n## Onboarding & customization\n\nRun the onboarding questionnaire **once before you start** — it captures your defaults so every tool in this skill is pre-configured. Customization is the point: the answers actually change tool behavior.\n\n```bash\npython3 scripts\u002Fonboard.py            # interactive (also: --defaults, --set key=value, --reset)\npython3 scripts\u002Fonboard.py --show     # see the questions + current effective config\n```\n\nAnswers are saved to `~\u002F.config\u002Fresearch-ops\u002Fmarket-research.json` (global) or `.\u002F.research-ops\u002Fmarket-research.json` (`--scope project`) and are read automatically by `config_loader.py`. They set the default market **profile**, the default survey **confidence** and **margin of error**, and the default **sizing method**. CLI flags always override saved config; `RESEARCH_OPS_NO_CONFIG=1` ignores it.\n\n**The four questions:** market profile · survey confidence · margin of error · sizing method.\n\n## Optimize with autoresearch (opt-in)\n\nThis skill ships an **isolated, opt-in** bridge to `engineering\u002Fautoresearch-agent`. Only when you ask to \"optimize\" \u002F \"reconcile the sizing\" \u002F \"run a loop\" does an autoresearch experiment iteratively reconcile your market model so top-down and bottoms-up triangulate. `scripts\u002Far_evaluator.py` is the ground-truth evaluator; it prints `tam_divergence: \u003Cfraction>` (**lower** is better).\n\n```bash\n\u002Far:setup --domain custom --name tam-triangulation \\\n  --target market.json \\\n  --eval \"python3 ar_evaluator.py --target market.json\" \\\n  --metric tam_divergence --direction lower\n\u002Far:loop custom\u002Ftam-triangulation\n```\n\nIsolated: no hard dependency — autoresearch runs only on demand, and the loop edits `market.json`, never the evaluator.\n\n## References\n\n- `references\u002Fmarket_sizing_canon.md` — TAM\u002FSAM\u002FSOM frameworks (Bessemer, a16z); top-down vs bottoms-up; Fermi estimation; market-model conventions; common sizing fallacies.\n- `references\u002Fsurvey_methodology.md` — Cochran *Sampling Techniques*; Dillman *Tailored Design Method*; Groves *Survey Methodology*; question-wording bias (Schuman & Presser); AAPOR standards.\n- `references\u002Fsegmentation_and_ci.md` — Kotler segmentation criteria; needs-based vs firmographic; Porter Five Forces; SCIP ethics; Christensen JTBD; conjoint\u002FMaxDiff primer.\n\n## Assumptions\n\n- The sizer reports both methods but cannot validate your inputs — a top-down \"1% of a $40B market\" is only as good as the cited source and the serviceable fraction.\n- Sample-size uses the conservative p=0.5 (maximum variance) unless you supply an expected proportion.\n- Segment scores are inputs you provide; the tool enforces the gates and the weighting, it does not gather the underlying evidence.\n- Competitive intelligence must follow the SCIP code of ethics — no misrepresentation, no protected information.\n\n## Anti-patterns\n\n- **A single TAM number with no method.** Always triangulate top-down against bottoms-up.\n- **Spurious precision.** Size to the decision's tolerance; \"$3.7142B\" implies a confidence you do not have.\n- **Powering only the total.** Each reported segment needs its own sample floor.\n- **Leading or double-barreled survey questions.** Pre-test wording against the bias literature.\n- **Calling a demographic slice a segment.** It must be substantial AND accessible.\n\n## Distinct from\n\n| Neighbor | Scope | Difference |\n|---|---|---|\n| `marketing-skill\u002Fcampaign-analytics` | Attribution, ROAS, CPA, funnel of a live campaign | That **measures spend deployed**; this is **upstream methodology** |\n| `marketing-skill\u002Fmarketing-demand-acquisition` | Demand-gen, paid media, channel mix | That **runs acquisition**; this **builds the evidence** |\n| `marketing-skill\u002Fmarketing-strategy-pmm` | Positioning, GTM, category | That **sets strategy**; this **sizes and segments the market** |\n| `commercial\u002Fpricing-strategist` | Pricing model + WTP + packaging | That **sets price**; this **sizes the market** |\n| `product-research` (sibling) | User\u002Fproduct discovery methods | That studies **users**; this studies **the market** |\n\n## Quick examples\n\n```bash\npython3 scripts\u002Fmarket_sizer.py --sample\npython3 scripts\u002Fsample_size_planner.py --population 62000 --confidence 0.95 --moe 0.05\npython3 scripts\u002Fsegmentation_scorer.py --sample --output json\n```\n\nThe sample market triangulates a ~$1.47B top-down SAM against the bottoms-up figure and flags the divergence; the segmentation sample drops the \"solopreneurs who might want analytics\" slice for failing the substantiality and accessibility gates.\n\n## Forcing-question library (Matt Pocock grill discipline)\n\nWalked one at a time by `\u002Fcs:grill-research-ops` or the orchestrator. Recommended answer + canon citation per question. Never bundled.\n\n1. **\"Is your TAM top-down or bottoms-up — and have you computed it both ways to triangulate?\"**\n   Recommended: both; reconcile the delta before quoting a number.\n   Canon: Bessemer \u002F a16z market-sizing; Fermi estimation.\n\n2. **\"What decision will this market size actually drive — and at what precision does it matter?\"**\n   Recommended: size to the decision's tolerance, not to a spurious-precision number.\n   Canon: market-model conventions (Gartner\u002FForrester); decision-driven analysis.\n\n3. **\"What's your target margin of error and confidence — and does your sample clear it per segment, not just overall?\"**\n   Recommended: power each reported segment, not only the total.\n   Canon: Cochran *Sampling Techniques*; AAPOR standards.\n\n4. **\"Are your survey questions free of leading and double-barreled wording?\"**\n   Recommended: pre-test the wording; cite the bias source.\n   Canon: Schuman & Presser; Dillman *Tailored Design Method*.\n\n5. **\"Do your segments pass measurable \u002F substantial \u002F accessible \u002F actionable — or are they just demographic slices?\"**\n   Recommended: drop segments that fail substantiality or accessibility.\n   Canon: Kotler segmentation criteria.\n\nWalk depth-first. Lock 1-2 before opening 3-5. After all are answered, invoke `market_sizer.py` → `sample_size_planner.py` → `segmentation_scorer.py`.\n","# market-research\n\nUpstream market-research methodology: market sizing, survey\u002Fsampling design, and segmentation. The discipline here is **method + assumptions**: a TAM is never a single number, a survey is never powered only in aggregate, and a segment is never a demographic slice.\n\n## Purpose\n\nMarket-research analysts, product marketers, and strategy teams need rigorous evidence *before* anyone optimizes a campaign or sets a strategy. This skill structures three methodology decisions:\n\nThree deterministic tools:\n\n1. `market_sizer.py` — Computes TAM\u002FSAM\u002FSOM by **both** top-down and bottoms-up methods side-by-side, reports the divergence, and flags failed triangulation. Never returns a single number.\n2. `sample_size_planner.py` — Survey sample size from confidence, margin of error, and expected proportion, with the finite-population correction and **per-segment minimums** (a survey powered overall is not powered per reported segment).\n3. `segmentation_scorer.py` — Scores candidate segments against Kotler's five criteria and enforces a substantiality + accessibility gate; a slice that is too small or unreachable is dropped.\n\n## When to use\n\nInvoke this skill when:\n\n- A board or exec asks \"how big is this market?\" and you need a defensible, triangulated answer.\n- You are fielding a survey and need a sample size that holds up per segment, not just overall.\n- You have a list of candidate segments and need to know which are real markets vs demographic slices.\n- You are synthesizing competitive intelligence and need a methodological backbone.\n\n**Do NOT use this skill to**: measure a live campaign (attribution, ROAS, CPA → `marketing-skill\u002Fcampaign-analytics`), build demand-gen \u002F paid-media plans (`marketing-skill\u002Fmarketing-demand-acquisition`), set positioning \u002F GTM strategy (`marketing-skill\u002Fmarketing-strategy-pmm`), or set pricing (`commercial\u002Fpricing-strategist`).\n\n## Workflow\n\n1. **Write the brief** — Fill `assets\u002Fmarket_research_brief_template.md` (objective, the decision this informs, sizing approach, sampling plan, assumptions register).\n2. **Size the market** — Run `market_sizer.py --input market.json --method both --profile {b2b-saas|consumer|enterprise|marketplace|hardware|services}`. Reconcile the top-down\u002Fbottoms-up delta before quoting anything.\n3. **Plan the survey** — Run `sample_size_planner.py --input survey.json`. Fund the per-segment floors, not just the overall n.\n4. **Score the segments** — Run `segmentation_scorer.py --input segments.json --profile \u003Csame>`. Drop segments failing the substantiality\u002Faccessibility gate.\n5. **Assemble the evidence pack** — Combine into a brief. Every number carries its method + assumptions + confidence.\n\n## Scripts\n\n| Script | Purpose | Profiles |\n|---|---|---|\n| `scripts\u002Fmarket_sizer.py` | TAM\u002FSAM\u002FSOM top-down AND bottoms-up + triangulation flag | b2b-saas, consumer, enterprise, marketplace, hardware, services |\n| `scripts\u002Fsample_size_planner.py` | Survey n + FPC + per-segment minima | n\u002Fa (parameter-driven) |\n| `scripts\u002Fsegmentation_scorer.py` | Kotler 5-criteria scoring + gate | b2b-saas, consumer, enterprise, marketplace, hardware, services |\n\nAll three: stdlib-only, `--help`, `--sample`, `--output {human,json}`.\n\n## Onboarding & customization\n\nRun the onboarding questionnaire **once before you start** — it captures your defaults so every tool in this skill is pre-configured. Customization is the point: the answers actually change tool behavior.\n\n```bash\npython3 scripts\u002Fonboard.py            # interactive (also: --defaults, --set key=value, --reset)\npython3 scripts\u002Fonboard.py --show     # see the questions + current effective config\n```\n\nAnswers are saved to `~\u002F.config\u002Fresearch-ops\u002Fmarket-research.json` (global) or `.\u002F.research-ops\u002Fmarket-research.json` (`--scope project`) and are read automatically by `config_loader.py`. They set the default market **profile**, the default survey **confidence** and **margin of error**, and the default **sizing method**. CLI flags always override saved config; `RESEARCH_OPS_NO_CONFIG=1` ignores it.\n\n**The four questions:** market profile · survey confidence · margin of error · sizing method.\n\n## Optimize with autoresearch (opt-in)\n\nThis skill ships an **isolated, opt-in** bridge to `engineering\u002Fautoresearch-agent`. Only when you ask to \"optimize\" \u002F \"reconcile the sizing\" \u002F \"run a loop\" does an autoresearch experiment iteratively reconcile your market model so top-down and bottoms-up triangulate. `scripts\u002Far_evaluator.py` is the ground-truth evaluator; it prints `tam_divergence: \u003Cfraction>` (**lower** is better).\n\n```bash\n\u002Far:setup --domain custom --name tam-triangulation \\\n  --target market.json \\\n  --eval \"python3 ar_evaluator.py --target market.json\" \\\n  --metric tam_divergence --direction lower\n\u002Far:loop custom\u002Ftam-triangulation\n```\n\nIsolated: no hard dependency — autoresearch runs only on demand, and the loop edits `market.json`, never the evaluator.\n\n## References\n\n- `references\u002Fmarket_sizing_canon.md` — TAM\u002FSAM\u002FSOM frameworks (Bessemer, a16z); top-down vs bottoms-up; Fermi estimation; market-model conventions; common sizing fallacies.\n- `references\u002Fsurvey_methodology.md` — Cochran *Sampling Techniques*; Dillman *Tailored Design Method*; Groves *Survey Methodology*; question-wording bias (Schuman & Presser); AAPOR standards.\n- `references\u002Fsegmentation_and_ci.md` — Kotler segmentation criteria; needs-based vs firmographic; Porter Five Forces; SCIP ethics; Christensen JTBD; conjoint\u002FMaxDiff primer.\n\n## Assumptions\n\n- The sizer reports both methods but cannot validate your inputs — a top-down \"1% of a $40B market\" is only as good as the cited source and the serviceable fraction.\n- Sample-size uses the conservative p=0.5 (maximum variance) unless you supply an expected proportion.\n- Segment scores are inputs you provide; the tool enforces the gates and the weighting, it does not gather the underlying evidence.\n- Competitive intelligence must follow the SCIP code of ethics — no misrepresentation, no protected information.\n\n## Anti-patterns\n\n- **A single TAM number with no method.** Always triangulate top-down against bottoms-up.\n- **Spurious precision.** Size to the decision's tolerance; \"$3.7142B\" implies a confidence you do not have.\n- **Powering only the total.** Each reported segment needs its own sample floor.\n- **Leading or double-barreled survey questions.** Pre-test wording against the bias literature.\n- **Calling a demographic slice a segment.** It must be substantial AND accessible.\n\n## Distinct from\n\n| Neighbor | Scope | Difference |\n|---|---|---|\n| `marketing-skill\u002Fcampaign-analytics` | Attribution, ROAS, CPA, funnel of a live campaign | That **measures spend deployed**; this is **upstream methodology** |\n| `marketing-skill\u002Fmarketing-demand-acquisition` | Demand-gen, paid media, channel mix | That **runs acquisition**; this **builds the evidence** |\n| `marketing-skill\u002Fmarketing-strategy-pmm` | Positioning, GTM, category | That **sets strategy**; this **sizes and segments the market** |\n| `commercial\u002Fpricing-strategist` | Pricing model + WTP + packaging | That **sets price**; this **sizes the market** |\n| `product-research` (sibling) | User\u002Fproduct discovery methods | That studies **users**; this studies **the market** |\n\n## Quick examples\n\n```bash\npython3 scripts\u002Fmarket_sizer.py --sample\npython3 scripts\u002Fsample_size_planner.py --population 62000 --confidence 0.95 --moe 0.05\npython3 scripts\u002Fsegmentation_scorer.py --sample --output json\n```\n\nThe sample market triangulates a ~$1.47B top-down SAM against the bottoms-up figure and flags the divergence; the segmentation sample drops the \"solopreneurs who might want analytics\" slice for failing the substantiality and accessibility gates.\n\n## Forcing-question library (Matt Pocock grill discipline)\n\nWalked one at a time by `\u002Fcs:grill-research-ops` or the orchestrator. Recommended answer + canon citation per question. Never bundled.\n\n1. **\"Is your TAM top-down or bottoms-up — and have you computed it both ways to triangulate?\"**\n   Recommended: both; reconcile the delta before quoting a number.\n   Canon: Bessemer \u002F a16z market-sizing; Fermi estimation.\n\n2. **\"What decision will this market size actually drive — and at what precision does it matter?\"**\n   Recommended: size to the decision's tolerance, not to a spurious-precision number.\n   Canon: market-model conventions (Gartner\u002FForrester); decision-driven analysis.\n\n3. **\"What's your target margin of error and confidence — and does your sample clear it per segment, not just overall?\"**\n   Recommended: power each reported segment, not only the total.\n   Canon: Cochran *Sampling Techniques*; AAPOR standards.\n\n4. **\"Are your survey questions free of leading and double-barreled wording?\"**\n   Recommended: pre-test the wording; cite the bias source.\n   Canon: Schuman & Presser; Dillman *Tailored Design Method*.\n\n5. **\"Do your segments pass measurable \u002F substantial \u002F accessible \u002F actionable — or are they just demographic slices?\"**\n   Recommended: drop segments that fail substantiality or accessibility.\n   Canon: Kotler segmentation criteria.\n\nWalk depth-first. Lock 1-2 before opening 3-5. After all are answered, invoke `market_sizer.py` → `sample_size_planner.py` → `segmentation_scorer.py`.\n",{"name":5,"tags":24,"author":32,"license":15,"version":33,"description":7,"compatible_tools":34},[25,5,26,27,28,29,30,31],"research-ops","tam-sam-som","market-sizing","survey","sampling","segmentation","competitive-intelligence","claude-code-skills","2.9.0",[35,36,37,38,39,40],"claude-code","codex-cli","cursor","antigravity","opencode","gemini-cli","main","5eac686c0e84af1a5dc958c1bfb6c9d00699561d","agentic\u002Fskills\u002Fmarket-research",1,"https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros","https:\u002F\u002Fgithub.com\u002FHeverton-web","2026-07-29 06:58:59",{"source":8,"skillName":5,"commands":49,"prompt":59,"zipAvailable":60},[50,53,56],{"tool":51,"command":52},"npx","npx skills add Heverton-web\u002Fproj_livros --skill market-research",{"tool":54,"command":55},"bunx","bunx skills add Heverton-web\u002Fproj_livros --skill market-research",{"tool":57,"command":58},"pnpm","pnpm dlx skills add Heverton-web\u002Fproj_livros --skill market-research","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fmarket-research\u002FSKILL.md",true,[62,68,74,78,82,88,92,96,100,104],{"path":63,"resourceType":64,"resourceTypeDesc":65,"sizeBytes":66,"sourceHtmlUrl":67},"assets\u002Fmarket_research_brief_template.md","ASSET","资产文件",1638,"https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fmarket-research\u002Fassets\u002Fmarket_research_brief_template.md",{"path":69,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":72,"sourceHtmlUrl":73},"references\u002Fmarket_sizing_canon.md","REFERENCE","参考文档",2650,"https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fmarket-research\u002Freferences\u002Fmarket_sizing_canon.md",{"path":75,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":76,"sourceHtmlUrl":77},"references\u002Fsegmentation_and_ci.md",2888,"https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fmarket-research\u002Freferences\u002Fsegmentation_and_ci.md",{"path":79,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Fsurvey_methodology.md",2631,"https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fmarket-research\u002Freferences\u002Fsurvey_methodology.md",{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"scripts\u002Far_evaluator.py","SCRIPT","脚本",2700,"https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fmarket-research\u002Fscripts\u002Far_evaluator.py",{"path":89,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":90,"sourceHtmlUrl":91},"scripts\u002Fconfig_loader.py",3814,"https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fmarket-research\u002Fscripts\u002Fconfig_loader.py",{"path":93,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":94,"sourceHtmlUrl":95},"scripts\u002Fmarket_sizer.py",6489,"https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fmarket-research\u002Fscripts\u002Fmarket_sizer.py",{"path":97,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":98,"sourceHtmlUrl":99},"scripts\u002Fonboard.py",4057,"https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fmarket-research\u002Fscripts\u002Fonboard.py",{"path":101,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":102,"sourceHtmlUrl":103},"scripts\u002Fsample_size_planner.py",6835,"https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fmarket-research\u002Fscripts\u002Fsample_size_planner.py",{"path":105,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":106,"sourceHtmlUrl":107},"scripts\u002Fsegmentation_scorer.py",5262,"https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fmarket-research\u002Fscripts\u002Fsegmentation_scorer.py",[109,116,123,130,137,144],{"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},"01M7A3PZFSZ0K95BAD6QHJPX97","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fyoutube-full\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},"01M7A3PH26QVPYRTM6EWS45YET","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fx-twitter-growth\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},"01M7A3NVTC4N3NG1TA2SGSFGF6","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fwrite-a-skill\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},"01M7A3NQNAJPSQ24VWSQ679AX8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fworkflow-builder\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},"01M7A3NE3FGSMGDY5XVKJ8297H","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[],{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":150,"publishedAt":19},"01M7A3N4ZG6TVJ1TST9C0T5QAK","webinar-marketing","When the user wants to plan, promote, run, or improve a webinar or virtual event to generate and convert demand. Use when the user mentions 'webinar,' 'virtual event,' 'online event,' 'live demo,' 'virtual summit,' 'workshop,' 'masterclass,' 'fireside chat,' 'roundtable,' 'registration funnel,' 'show-up rate,' 'attendance rate,' 'webinar promotion,' 'webinar follow-up,' or 'on-demand webinar.' Also use when they have a webinar that isn't converting — low registrations, low show-up, or attendees who don't buy — and want to diagnose and fix it. Covers the full funnel: registration, promotion, show-up, live engagement, live-to-close, and post-event nurture. Distinct from launch-strategy (full product launches) and email-sequence (lifecycle nurture) — this is the end-to-end webinar\u002Fevent motion. NOT for in-person field events logistics, and NOT for generic lifecycle email (use email-sequence).","https:\u002F\u002Fgithub.com\u002FHeverton-web\u002Fproj_livros\u002Fblob\u002Fmain\u002Fagentic\u002Fskills\u002Fwebinar-marketing\u002FSKILL.md",[],[]]