[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QMJK7K33YNHAF8VT1C9KSA":3,"skill-same:01M6QMJK7K33YNHAF8VT1C9KSA":89},{"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":31,"publishedAt":51,"linkOnly":52,"markdownContent":53,"markdownBody":54,"frontMatter":55,"refName":66,"commitSha":67,"skillDirectory":68,"forksCount":14,"repositoryHtmlUrl":69,"repositoryHomepageUrl":70,"ownerHtmlUrl":71,"ownerBlogUrl":72,"ownerDescription":73,"updatedAt":74,"install":75,"resources":88},"01M6QMJK7K33YNHAF8VT1C9KSA","product-research",null,"Use when planning and synthesizing product\u002Fuser research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation\u002Fsample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team\u002Fux-researcher-designer (persona\u002Fjourney artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A\u002FB) — this is the research-ops method + insight-repository layer.","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fproduct-research\u002FSKILL.md",[18,23,28],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000031","产品经理","PROFESSION","适用职业",{"categoryNo":29,"categoryName":30,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000032","设计师",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,5,47,48,49,50],"research","product","product research","generative","gemini","for","concept","as","live","research-repository","insight-synthesis","saturation","usability","jtbd","ux-research","research-ops","discipline","team","discovery","2026-11-10 10:12:30",false,"---\nname: product-research\ndescription: Use when planning and synthesizing product\u002Fuser research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation\u002Fsample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team\u002Fux-researcher-designer (persona\u002Fjourney artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A\u002FB) — this is the research-ops method + insight-repository layer.\nversion: 2.9.0\nauthor: claude-code-skills\nlicense: MIT\ntags: [research-ops, product-research, ux-research, jtbd, usability, saturation, insight-synthesis, research-repository]\ncompatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]\n---\n\n# product-research\n\nProduct \u002F user research as an operational discipline: choosing the right method, sizing it honestly, and synthesizing findings into governed insights. The core rule: **method must match the goal**, and **an insight requires recurrence across independent participants** — a single quote is an anecdote.\n\n## Purpose\n\nProduct researchers, ResearchOps teams, and PMs running discovery need method rigor and an insight repository they can trust. This skill structures three decisions:\n\nThree deterministic tools:\n\n1. `study_designer.py` — Maps (research goal × product stage) to an appropriate method and emits a method-matched plan skeleton (objective, participant criteria, guide structure, success criteria). Redirects live A\u002FB to `product-team\u002Fexperiment-designer`.\n2. `saturation_planner.py` — Method-based sample guidance with an explicit **confidence label**: Nielsen problem-discovery (5\u002Fsegment), Guest et al. thematic saturation (~12), and evaluative coverage. Never claims a prevalence rate from a small-n usability test.\n3. `insight_synthesizer.py` — Clusters coded observations by tag, counts distinct participants, ranks by cross-participant recurrence, and flags any candidate below the source threshold as an **ANECDOTE**, never promoting it to an insight.\n\n## When to use\n\nInvoke this skill when:\n\n- You are planning a study and need the method to match the goal (generative vs evaluative vs validation).\n- You need a defensible sample size \u002F saturation rationale with a stated confidence.\n- You have raw coded observations and need to synthesize insights without over-claiming.\n- You are setting up or auditing a research repository and need the insight-vs-observation discipline.\n\n**Do NOT use this skill to**: generate personas \u002F journey maps (use `product-team\u002Fux-researcher-designer`), plan a discovery sprint or validate an opportunity (use `product-team\u002Fproduct-discovery`), design or analyze a live product A\u002FB experiment (use `product-team\u002Fexperiment-designer`), or do market sizing \u002F surveys (use the `market-research` sibling).\n\n## Workflow\n\n1. **Frame the study** — Fill `assets\u002Fresearch_plan_template.md` (research questions, method rationale, participant criteria, analysis plan, repository tagging scheme).\n2. **Pick the method** — Run `study_designer.py --goal {discovery|evaluative|validation} --stage {concept|prototype|beta|live} --profile {b2b-saas|consumer-app|enterprise|marketplace|hardware|platform}`. Honor the redirect if it routes to experiment-designer.\n3. **Size it** — Run `saturation_planner.py --method {usability|thematic|evaluative-coverage} --segments N`. Record the confidence label and limits.\n4. **Synthesize** — After fielding, code observations and run `insight_synthesizer.py --input observations.json --min-sources 3`. Treat ANECDOTE-flagged clusters as signals to probe, not findings to ship.\n5. **File in the repository** — Tag insights to the atomic schema at synthesis time, with their evidence and confidence.\n\n## Scripts\n\n| Script | Purpose | Profiles |\n|---|---|---|\n| `scripts\u002Fstudy_designer.py` | (goal × stage) → method + plan skeleton | b2b-saas, consumer-app, enterprise, marketplace, hardware, platform |\n| `scripts\u002Fsaturation_planner.py` | Method-based sample guidance + confidence | n\u002Fa (method-driven) |\n| `scripts\u002Finsight_synthesizer.py` | Cluster observations, flag anecdotes | n\u002Fa (evidence-driven) |\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 (e.g. the insight source-threshold).\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\u002Fproduct-research.json` (global) or `.\u002F.research-ops\u002Fproduct-research.json` (`--scope project`) and are read automatically by `config_loader.py`. They set the default product **profile**, the **insight source-threshold** (how many independent participants make a finding an insight, not an anecdote), the default **saturation method**, and the **high-stakes** flag. CLI flags always override saved config; `RESEARCH_OPS_NO_CONFIG=1` ignores it.\n\n**The four questions:** product profile · insight source-threshold · saturation method · high-stakes flag.\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 the synthesis\" \u002F \"run a loop\" does an autoresearch experiment iteratively refine the coding\u002Fclustering of a fixed evidence set so more cross-participant patterns surface. `scripts\u002Far_evaluator.py` is the ground-truth evaluator; it prints `validated_insights: \u003Cint>` (higher is better). It optimizes the **coding**, never fabricates evidence.\n\n```bash\n\u002Far:setup --domain custom --name insight-synthesis \\\n  --target observations.json \\\n  --eval \"python3 ar_evaluator.py --target observations.json\" \\\n  --metric validated_insights --direction higher\n\u002Far:loop custom\u002Finsight-synthesis\n```\n\nIsolated: no hard dependency — autoresearch runs only on demand, and the loop edits `observations.json`, never the evaluator.\n\n## References\n\n- `references\u002Fresearch_methods_canon.md` — Portigal *Interviewing Users*; Christensen\u002FUlwick JTBD; Rohrer's UX-research methods landscape (NN\u002Fg); Sauro & Lewis *Quantifying the User Experience*; Goodman\u002FKuniavsky.\n- `references\u002Fsampling_and_saturation.md` — Nielsen \"test with 5 users\"; Guest, Bunce & Johnson saturation; Faulkner on more-than-5; Sauro usability sample size; Braun & Clarke thematic analysis.\n- `references\u002Frepository_and_synthesis.md` — ResearchOps \u002F atomic research (Tomer Sharon \"Polaris\"); insight-vs-observation discipline; repository governance; affinity mapping; democratization guardrails.\n\n## Assumptions\n\n- Method selection assumes you can name the goal honestly; if the goal is fuzzy, grill it first (the goal drives everything).\n- Saturation guidance is method-based, not a power calculation — usability tests find problems, not prevalence rates.\n- The synthesizer counts evidence you provide; coding quality is upstream of it. Garbage tags → garbage clusters.\n- The insight threshold (`--min-sources`) defaults to 3; raise it for high-stakes or heterogeneous populations.\n\n## Anti-patterns\n\n- **Mismatching method to goal.** A usability test cannot discover unmet needs; an interview cannot measure task success.\n- **Reporting usability problems as percentages.** Small-n tests surface problems, not population rates.\n- **Promoting an anecdote to an insight.** One participant is a signal to probe, not a finding.\n- **Framing interview questions as feature reactions.** Probe the job-to-be-done and recent real behavior, not hypothetical opinions.\n- **Synthesizing without a repository scheme.** Tag at synthesis time, or insights rot unfindable.\n\n## Distinct from\n\n| Neighbor | Scope | Difference |\n|---|---|---|\n| `product-team\u002Fux-researcher-designer` | Personas, journey maps, usability frameworks tied to design output | That produces **artifacts**; this is **method + repository discipline** |\n| `product-team\u002Fproduct-discovery` | Opportunity validation, discovery-sprint planning | That plans **discovery sprints**; this designs and synthesizes the **research** |\n| `product-team\u002Fexperiment-designer` | Live product A\u002FB hypothesis + sample size | That runs **live experiments**; this runs **qualitative\u002Fevaluative research** |\n| `market-research` (sibling) | Market sizing, surveys, segmentation | That studies **the market**; this studies **users** |\n\n## Quick examples\n\n```bash\npython3 scripts\u002Fstudy_designer.py --sample\npython3 scripts\u002Fsaturation_planner.py --method thematic --segments 3\npython3 scripts\u002Finsight_synthesizer.py --sample --min-sources 3\n```\n\nThe synthesizer sample correctly promotes \"import-confusion\" (3 independent participants) to INSIGHT and flags \"wants-slack\" (1 participant) as an ANECDOTE.\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 this study generative (discover problems) or evaluative (test a solution)?\"**\n   Recommended: name it first — the method follows from the goal.\n   Canon: Rohrer, *When to Use Which User-Experience Research Methods* (NN\u002Fg).\n\n2. **\"What's your sample size and saturation rationale — and at what confidence?\"**\n   Recommended: method-based n (5\u002Fsegment usability; ~12 for thematic saturation), state the confidence.\n   Canon: Nielsen; Guest, Bunce & Johnson (2006); Faulkner (2003).\n\n3. **\"How many independent participants support each insight — or is it a single-source anecdote?\"**\n   Recommended: require recurrence across ≥3 sources before calling it an insight; flag singletons.\n   Canon: atomic research \u002F ResearchOps; Braun & Clarke thematic analysis.\n\n4. **\"Are your interview \u002F usability tasks framed as outcomes (jobs) or as feature reactions?\"**\n   Recommended: frame around the job-to-be-done and recent real behavior, not hypothetical opinion.\n   Canon: Christensen\u002FUlwick Jobs-to-be-Done; Portigal *Interviewing Users*.\n\n5. **\"Where does this land in the repository, and how is it tagged for reuse?\"**\n   Recommended: tag to the atomic schema at synthesis time, not later.\n   Canon: Tomer Sharon, *Polaris* \u002F ResearchOps repository practice.\n\nWalk depth-first. Lock 1-2 before opening 3-5. After all are answered, invoke `study_designer.py` → `saturation_planner.py` → (after fielding) `insight_synthesizer.py`.\n","# product-research\n\nProduct \u002F user research as an operational discipline: choosing the right method, sizing it honestly, and synthesizing findings into governed insights. The core rule: **method must match the goal**, and **an insight requires recurrence across independent participants** — a single quote is an anecdote.\n\n## Purpose\n\nProduct researchers, ResearchOps teams, and PMs running discovery need method rigor and an insight repository they can trust. This skill structures three decisions:\n\nThree deterministic tools:\n\n1. `study_designer.py` — Maps (research goal × product stage) to an appropriate method and emits a method-matched plan skeleton (objective, participant criteria, guide structure, success criteria). Redirects live A\u002FB to `product-team\u002Fexperiment-designer`.\n2. `saturation_planner.py` — Method-based sample guidance with an explicit **confidence label**: Nielsen problem-discovery (5\u002Fsegment), Guest et al. thematic saturation (~12), and evaluative coverage. Never claims a prevalence rate from a small-n usability test.\n3. `insight_synthesizer.py` — Clusters coded observations by tag, counts distinct participants, ranks by cross-participant recurrence, and flags any candidate below the source threshold as an **ANECDOTE**, never promoting it to an insight.\n\n## When to use\n\nInvoke this skill when:\n\n- You are planning a study and need the method to match the goal (generative vs evaluative vs validation).\n- You need a defensible sample size \u002F saturation rationale with a stated confidence.\n- You have raw coded observations and need to synthesize insights without over-claiming.\n- You are setting up or auditing a research repository and need the insight-vs-observation discipline.\n\n**Do NOT use this skill to**: generate personas \u002F journey maps (use `product-team\u002Fux-researcher-designer`), plan a discovery sprint or validate an opportunity (use `product-team\u002Fproduct-discovery`), design or analyze a live product A\u002FB experiment (use `product-team\u002Fexperiment-designer`), or do market sizing \u002F surveys (use the `market-research` sibling).\n\n## Workflow\n\n1. **Frame the study** — Fill `assets\u002Fresearch_plan_template.md` (research questions, method rationale, participant criteria, analysis plan, repository tagging scheme).\n2. **Pick the method** — Run `study_designer.py --goal {discovery|evaluative|validation} --stage {concept|prototype|beta|live} --profile {b2b-saas|consumer-app|enterprise|marketplace|hardware|platform}`. Honor the redirect if it routes to experiment-designer.\n3. **Size it** — Run `saturation_planner.py --method {usability|thematic|evaluative-coverage} --segments N`. Record the confidence label and limits.\n4. **Synthesize** — After fielding, code observations and run `insight_synthesizer.py --input observations.json --min-sources 3`. Treat ANECDOTE-flagged clusters as signals to probe, not findings to ship.\n5. **File in the repository** — Tag insights to the atomic schema at synthesis time, with their evidence and confidence.\n\n## Scripts\n\n| Script | Purpose | Profiles |\n|---|---|---|\n| `scripts\u002Fstudy_designer.py` | (goal × stage) → method + plan skeleton | b2b-saas, consumer-app, enterprise, marketplace, hardware, platform |\n| `scripts\u002Fsaturation_planner.py` | Method-based sample guidance + confidence | n\u002Fa (method-driven) |\n| `scripts\u002Finsight_synthesizer.py` | Cluster observations, flag anecdotes | n\u002Fa (evidence-driven) |\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 (e.g. the insight source-threshold).\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\u002Fproduct-research.json` (global) or `.\u002F.research-ops\u002Fproduct-research.json` (`--scope project`) and are read automatically by `config_loader.py`. They set the default product **profile**, the **insight source-threshold** (how many independent participants make a finding an insight, not an anecdote), the default **saturation method**, and the **high-stakes** flag. CLI flags always override saved config; `RESEARCH_OPS_NO_CONFIG=1` ignores it.\n\n**The four questions:** product profile · insight source-threshold · saturation method · high-stakes flag.\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 the synthesis\" \u002F \"run a loop\" does an autoresearch experiment iteratively refine the coding\u002Fclustering of a fixed evidence set so more cross-participant patterns surface. `scripts\u002Far_evaluator.py` is the ground-truth evaluator; it prints `validated_insights: \u003Cint>` (higher is better). It optimizes the **coding**, never fabricates evidence.\n\n```bash\n\u002Far:setup --domain custom --name insight-synthesis \\\n  --target observations.json \\\n  --eval \"python3 ar_evaluator.py --target observations.json\" \\\n  --metric validated_insights --direction higher\n\u002Far:loop custom\u002Finsight-synthesis\n```\n\nIsolated: no hard dependency — autoresearch runs only on demand, and the loop edits `observations.json`, never the evaluator.\n\n## References\n\n- `references\u002Fresearch_methods_canon.md` — Portigal *Interviewing Users*; Christensen\u002FUlwick JTBD; Rohrer's UX-research methods landscape (NN\u002Fg); Sauro & Lewis *Quantifying the User Experience*; Goodman\u002FKuniavsky.\n- `references\u002Fsampling_and_saturation.md` — Nielsen \"test with 5 users\"; Guest, Bunce & Johnson saturation; Faulkner on more-than-5; Sauro usability sample size; Braun & Clarke thematic analysis.\n- `references\u002Frepository_and_synthesis.md` — ResearchOps \u002F atomic research (Tomer Sharon \"Polaris\"); insight-vs-observation discipline; repository governance; affinity mapping; democratization guardrails.\n\n## Assumptions\n\n- Method selection assumes you can name the goal honestly; if the goal is fuzzy, grill it first (the goal drives everything).\n- Saturation guidance is method-based, not a power calculation — usability tests find problems, not prevalence rates.\n- The synthesizer counts evidence you provide; coding quality is upstream of it. Garbage tags → garbage clusters.\n- The insight threshold (`--min-sources`) defaults to 3; raise it for high-stakes or heterogeneous populations.\n\n## Anti-patterns\n\n- **Mismatching method to goal.** A usability test cannot discover unmet needs; an interview cannot measure task success.\n- **Reporting usability problems as percentages.** Small-n tests surface problems, not population rates.\n- **Promoting an anecdote to an insight.** One participant is a signal to probe, not a finding.\n- **Framing interview questions as feature reactions.** Probe the job-to-be-done and recent real behavior, not hypothetical opinions.\n- **Synthesizing without a repository scheme.** Tag at synthesis time, or insights rot unfindable.\n\n## Distinct from\n\n| Neighbor | Scope | Difference |\n|---|---|---|\n| `product-team\u002Fux-researcher-designer` | Personas, journey maps, usability frameworks tied to design output | That produces **artifacts**; this is **method + repository discipline** |\n| `product-team\u002Fproduct-discovery` | Opportunity validation, discovery-sprint planning | That plans **discovery sprints**; this designs and synthesizes the **research** |\n| `product-team\u002Fexperiment-designer` | Live product A\u002FB hypothesis + sample size | That runs **live experiments**; this runs **qualitative\u002Fevaluative research** |\n| `market-research` (sibling) | Market sizing, surveys, segmentation | That studies **the market**; this studies **users** |\n\n## Quick examples\n\n```bash\npython3 scripts\u002Fstudy_designer.py --sample\npython3 scripts\u002Fsaturation_planner.py --method thematic --segments 3\npython3 scripts\u002Finsight_synthesizer.py --sample --min-sources 3\n```\n\nThe synthesizer sample correctly promotes \"import-confusion\" (3 independent participants) to INSIGHT and flags \"wants-slack\" (1 participant) as an ANECDOTE.\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 this study generative (discover problems) or evaluative (test a solution)?\"**\n   Recommended: name it first — the method follows from the goal.\n   Canon: Rohrer, *When to Use Which User-Experience Research Methods* (NN\u002Fg).\n\n2. **\"What's your sample size and saturation rationale — and at what confidence?\"**\n   Recommended: method-based n (5\u002Fsegment usability; ~12 for thematic saturation), state the confidence.\n   Canon: Nielsen; Guest, Bunce & Johnson (2006); Faulkner (2003).\n\n3. **\"How many independent participants support each insight — or is it a single-source anecdote?\"**\n   Recommended: require recurrence across ≥3 sources before calling it an insight; flag singletons.\n   Canon: atomic research \u002F ResearchOps; Braun & Clarke thematic analysis.\n\n4. **\"Are your interview \u002F usability tasks framed as outcomes (jobs) or as feature reactions?\"**\n   Recommended: frame around the job-to-be-done and recent real behavior, not hypothetical opinion.\n   Canon: Christensen\u002FUlwick Jobs-to-be-Done; Portigal *Interviewing Users*.\n\n5. **\"Where does this land in the repository, and how is it tagged for reuse?\"**\n   Recommended: tag to the atomic schema at synthesis time, not later.\n   Canon: Tomer Sharon, *Polaris* \u002F ResearchOps repository practice.\n\nWalk depth-first. Lock 1-2 before opening 3-5. After all are answered, invoke `study_designer.py` → `saturation_planner.py` → (after fielding) `insight_synthesizer.py`.\n",{"name":5,"tags":56,"author":57,"license":15,"version":58,"description":7,"compatible_tools":59},[47,5,46,45,44,43,42,41],"claude-code-skills","2.9.0",[60,61,62,63,64,65],"claude-code","codex-cli","cursor","antigravity","opencode","gemini-cli","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Fproduct-research","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6","https:\u002F\u002Fdisha6.6.thenitishkr.in\u002F","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh","www.thenitishkr","thenitishkr | AI • Cyber-security • Research\r\n","2026-11-08 03:33:25",{"source":8,"skillName":5,"commands":76,"prompt":86,"zipAvailable":87},[77,80,83],{"tool":78,"command":79},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill product-research",{"tool":81,"command":82},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill product-research",{"tool":84,"command":85},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill product-research","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fproduct-research\u002FSKILL.md",true,[],[90,119,143,170,194,218],{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":99,"publishedAt":51},"01M6QRD07D4TGNDK6VJ4EHF8BV","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\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[96],{"categoryNo":97,"categoryName":98,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,37,116,117,118],"youtube full","youtube","full","transcript","search","monitoring","data","Video","Audio","list","files","do","get","age","trigger","URL","browsing","request","youtube transcripts",{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":125,"publishedAt":51},"01M6QRCJ3112VYPA97YG7KGMC2","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\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[126,127,128,32,104,129,130,131,132,133,134,135,136,137,138,139,140,141,142,37],"twitter","x twitter growth","growth","content","engineering","plan","web search","competitors","analyze","engine","write","strategy","use","platform","grow","posting","competitive",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":152,"publishedAt":51},"01M6QRBN3EHQNJY42RERP7A149","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\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[149],{"categoryNo":150,"categoryName":151,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[153,136,154,155,156,157,158,159,138,160,161,66,162,163,164,165,166,167,168,169],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":176,"publishedAt":51},"01M6QRB6G8P33PNGAF9Y69EJ9T","wrap-up","Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says \"wrap up\", \"close this out\", \"what do I own now\", \"give me the summary\", \"recap the agent\", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained .\u002Fmy-agent\u002Fagent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[177,178,179,36,180,181,182,183,184,138,185,111,186,187,188,189,190,191,192,193],"wrap up","up","wrap","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","directions","phase",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":198,"categories":199,"keywords":201,"publishedAt":51},"01M6QRA5AYC1RBFZ39GTWGS3N8","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\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[200],{"categoryNo":150,"categoryName":151,"taxonomyType":21,"taxonomyTypeDesc":22},[202,203,204,205,206,155,181,207,208,209,210,211,136,212,110,213,214,215,216,217],"workflow","workflow builder","builder","design","workflows","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":219,"name":220,"nameZh":6,"description":221,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":222,"categories":223,"keywords":224,"publishedAt":51},"01M6QR7QMCZESKQA0ERW1E657M","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\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[225,226,227,228,229,230,231,232,49,210,233,234,235,236,237,238,112,239,240,193],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","projects","project-management","scanner","conversation","back","project","sprint","cadence"]