[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MM90B2XZ8MH6Z3900DFKKR":3,"skill-same:01M6MM90B2XZ8MH6Z3900DFKKR":97},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":39,"publishedAt":60,"linkOnly":61,"markdownContent":62,"markdownBody":63,"frontMatter":64,"refName":77,"commitSha":78,"skillDirectory":79,"forksCount":13,"repositoryHtmlUrl":80,"repositoryHomepageUrl":6,"ownerHtmlUrl":81,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":82,"install":83,"resources":96},"01M6MM90B2XZ8MH6Z3900DFKKR","research-ops-skills",null,"Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing\u002Fsurveys, or product\u002Fuser research. Triggers on \"design this clinical study\", \"what sample size\", \"R&D budget\", \"burn rate\", \"capitalize or expense\", \"TAM SAM SOM\", \"market sizing\", \"survey design\", \"segment the market\", \"plan user interviews\", \"usability test\", \"synthesize research insights\". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close\u002Fvaluation), research\u002Fgrants (funding discovery), product-team (persona\u002Fjourney\u002Flive experiments), and marketing-skill (campaign analytics).","JiayiV\u002Fquestion_mixing","JiayiV","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54466889?v=4","01M6JFJYYMRZ3QRGP9339JNQ2K","01M6JFJZ35KB0QVT50NAHPJA7G",0,"MIT","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fpublic-02-claude-skills-main-research-ops-skills-research-ops-skills-skill-b5dd8c2e\u002FSKILL.md",[17,22,25,28,33,36],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":26,"categoryName":27,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":29,"categoryName":30,"taxonomyType":31,"taxonomyTypeDesc":32},"01JBSEEDCAT000000000000031","产品经理","PROFESSION","适用职业",{"categoryNo":34,"categoryName":35,"taxonomyType":31,"taxonomyTypeDesc":32},"01JBSEEDCAT000000000000035","市场营销",{"categoryNo":37,"categoryName":38,"taxonomyType":31,"taxonomyTypeDesc":32},"01JBSEEDCAT000000000000040","财务",[40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59],"research","research ops skills","ops","research ops","close","design","synthesize research","synthesize","survey design","campaign","rd","research-finance","usability","product-research","survey","market-research","clinical-research","research-ops","plan","team","2026-11-10 10:12:30",false,"---\nname: research-ops-skills\ndescription: Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing\u002Fsurveys, or product\u002Fuser research. Triggers on \"design this clinical study\", \"what sample size\", \"R&D budget\", \"burn rate\", \"capitalize or expense\", \"TAM SAM SOM\", \"market sizing\", \"survey design\", \"segment the market\", \"plan user interviews\", \"usability test\", \"synthesize research insights\". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close\u002Fvaluation), research\u002Fgrants (funding discovery), product-team (persona\u002Fjourney\u002Flive experiments), and marketing-skill (campaign analytics).\ncontext: fork\nversion: 2.9.0\nauthor: claude-code-skills\nlicense: MIT\ntags: [research-ops, clinical-research, research-finance, market-research, product-research, rd, orchestrator]\ncompatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]\n---\n\n# Research Operations — Domain Orchestrator\n\nThe Research Operations surface is **how the enterprise plans, funds, scopes, and synthesizes research** across four workstreams: clinical R&D, R&D finance, market research, and product research. This orchestrator forks its context, routes your inquiry to one of four sub-skills, then returns a digest. Heavy intake (protocol drafts, program ledgers, survey exports, interview transcripts) stays in the forked context.\n\nThis is the enterprise counterpart to the academic `research\u002F` domain. If your question is about **finding** literature, grants, or patents, use `research\u002F`. If it is about **planning, funding, scoping, or synthesizing** research as an operational discipline, you are in the right place.\n\n## When to invoke\n\n| Symptom | Sub-skill |\n|---|---|\n| \"We're designing a Phase 2 trial — what's the endpoint and sample size?\" | `clinical-research` |\n| \"What's our R&D program burn, and is this cost CapEx or OpEx?\" | `research-finance` |\n| \"What's the TAM for this product, and how do we survey the segment?\" | `market-research` |\n| \"How many users do we interview, and how do we synthesize the findings?\" | `product-research` |\n\n## Routing logic (deterministic)\n\nSame two-signal threshold pattern as `commercial-skills`. Single-signal → clarifying question. Mixed signals → highest-confidence first, chain second in a follow-up turn. Never silently chain.\n\n### Signal table\n\n| Signal class | Keywords | Sub-skill |\n|---|---|---|\n| **CLINICAL** | clinical trial, study design, protocol, endpoint, sample size, power, phase 1\u002F2\u002F3, biostatistics, eligibility, feasibility, estimand | `clinical-research` |\n| **RD_FINANCE** | R&D budget, program budget, burn, runway, F&A, indirect rate, overhead, capitalize vs expense, R&D capex, portfolio ROI, rNPV | `research-finance` |\n| **MARKET** | TAM, SAM, SOM, market sizing, survey design, sampling, margin of error, segmentation, competitive intelligence, market research | `market-research` |\n| **PRODUCT** | user interview, JTBD, usability test, concept test, prototype test, discovery research, research repository, insight synthesis, saturation | `product-research` |\n\n## Workflow (Matt Pocock grill discipline)\n\nDerived from Matt Pocock's `grill-with-docs` pattern: **explore-then-ask, one question per turn with a recommended answer, walk the decision tree depth-first, track dependencies, anchor every challenge in the research canon** (`references\u002F` of each sub-skill).\n\n### Step 1 — Explore before asking\n\nCheck the user's working directory first:\n- Is there a protocol draft, program ledger, TAM model, or interview guide already in the workspace?\n- Does the inquiry already disambiguate the lane (e.g., \"what sample size for a two-arm trial\" — that's `clinical-research`, no question needed)?\n- Is there an artifact filename that resolves the lane (`protocol.json` → clinical; `program-budget.json` → finance; `tam-model.json` → market; `interview-guide.md` → product)?\n\nIf the workspace resolves the lane, **route silently**.\n\n### Step 2 — If still ambiguous, ONE forcing question with a recommended answer\n\nMatt's rule: never bundle. Always recommend.\n\nPattern:\n```\nQ1\u002F1: [precise question naming the two candidate lanes]\nRecommended: [Lane X, because \u003Csignal-table rationale>]\n\n(Confirm, or override?)\n```\n\n### Step 3 — Decision-tree walk for multi-lane inquiries\n\nIf the inquiry legitimately crosses two lanes (e.g., \"design this trial AND budget it\" = CLINICAL + RD_FINANCE), walk depth-first:\n\n1. Highest-confidence lane first → run sub-skill in forked context → digest\n2. Ask: \"Now run [second lane]? Recommended: yes, because [dependency].\"\n3. Confirm before chaining.\n\nNever silently chain.\n\n### Step 4 — Invoke sub-skill in forked context\n\nForward original prompt + structured inputs (protocol JSON, program ledger CSV, market model, observation export).\n\n### Step 5 — Return digest with cited canon challenge\n\n≤ 200 words: analyzed, top 3 findings (anchored to a canon citation), top 3 next actions (named human owner where applicable), artifact path, and **one grill challenge** for the user. Examples:\n\n- \"Your power calc assumes a 0.5 effect size with no published anchor. ICH E9 requires a justified, clinically meaningful difference. Where did 0.5 come from?\"\n- \"Your TAM is a single top-down number (1% of a $40B market). Bessemer market-sizing discipline requires a bottoms-up cross-check. What's units × price × adoption?\"\n\n## Forcing-question library (grill-with-docs pattern)\n\nGrill the user on lane-defining decisions before invoking the sub-skill. One per turn, recommended answer, canon citation:\n\n- **CLINICAL lane**: \"Is your primary endpoint a clinical outcome or a surrogate — and if surrogate, is it validated for this indication? Recommended: clinical outcome unless the surrogate is on FDA's validated table. Canon: FDA Surrogate Endpoint Table; BEST glossary.\"\n- **RD_FINANCE lane**: \"Is this spend in the research phase or the development phase, and can you evidence technical feasibility? Recommended: research = expense; development = capitalize-candidate only with feasibility evidence, routed to a named finance owner. Canon: IAS 38; ASC 730.\"\n- **MARKET lane**: \"Is your TAM top-down or bottoms-up — and have you computed it both ways to triangulate? Recommended: both; reconcile the delta. Canon: Bessemer \u002F a16z market-sizing; Fermi estimation.\"\n- **PRODUCT lane**: \"Is this study generative (discover problems) or evaluative (test a solution)? Recommended: name it first; the method follows. Canon: Rohrer's landscape of UX research methods (NN\u002Fg).\"\n\nNever run a sub-skill until the lane-defining decision is locked.\n\n## Onboarding-first (per sub-skill)\n\nBefore invoking a sub-skill for the first time in a workspace, point the user at that skill's onboarding questionnaire so the tools run pre-configured to their context:\n\n```bash\npython3 skills\u002F\u003Csub-skill>\u002Fscripts\u002Fonboard.py          # interactive Q&A\npython3 skills\u002F\u003Csub-skill>\u002Fscripts\u002Fonboard.py --show    # questions + current config\n```\n\nEach sub-skill has its **own** question set (clinical: area\u002Falpha\u002Fpower\u002Fdropout\u002Fowners · finance: area\u002FF&A\u002Frunway\u002Fstandard\u002Fowner · market: profile\u002Fconfidence\u002FMoE\u002Fmethod · product: profile\u002Finsight-threshold\u002Fmethod\u002Fstakes). Answers persist to `~\u002F.config\u002Fresearch-ops\u002F\u003Csub-skill>.json` (or `.\u002F.research-ops\u002F\u003Csub-skill>.json` with `--scope project`) and are consumed automatically by every tool in that skill. Customization is mandatory discipline here, not decoration — surface the onboarding step when a user starts a fresh research workstream.\n\n## Autoresearch handoff (isolated, opt-in)\n\nEach sub-skill ships its own `skills\u002F\u003Csub-skill>\u002Fscripts\u002Far_evaluator.py` — an **isolated** bridge to `engineering\u002Fautoresearch-agent`. Invoke autoresearch **only when the user explicitly asks** to \"optimize\", \"improve\", or \"run a loop\". The handoff is per-skill (no shared coupling): the loop edits the skill's input file and the evaluator scores it (clinical → `feasibility_composite` higher; finance → `runway_months` higher; market → `tam_divergence` lower; product → `validated_insights` higher). Never auto-start a loop; never let the loop edit the evaluator.\n\n## Assumptions\n\n1. User has research authority OR is preparing analysis for someone who does.\n2. User wants **deterministic decision support**, not the final answer — a clinician approves the protocol, a controller books the entry, the human picks the market number.\n3. Inputs may be partial — every sub-skill ships a templated sample so the user can see the shape before filling in their own.\n\n## Non-goals\n\n- Not an EDC, clinical-trial-management system, accounting system, survey platform, or research repository.\n- Does not give clinical, accounting, or legal advice as fact. Every output is **a recommendation + named human owner**.\n- Does not store research history across sessions.\n\n## Distinct from\n\n- **`research\u002F` (academic)** — that domain **finds** literature, grants, and patents. This domain **plans, funds, scopes, and synthesizes** research.\n- **`ra-qm-team`** — that's **regulatory\u002FQM submission** (ISO 13485\u002F14971, MDR, FDA 510(k)\u002FPMA\u002FQSR). clinical-research designs the **study**; it routes submission out to ra-qm-team.\n- **`finance\u002Ffinancial-analysis`** — that's **corporate close + valuation**. research-finance manages **R&D program\u002Fportfolio spend**.\n- **`research\u002Fgrants`** — that's **funding discovery**. research-finance manages **money already won**.\n- **`product-team`** — that's **persona\u002Fjourney artifacts, discovery sprints, and live A\u002FB experiments**. product-research is the **method + repository discipline**.\n- **`marketing-skill`** — that's **campaign analytics and demand-gen**. market-research is **upstream methodology**.\n\n## Output artifacts\n\n| Sub-skill | Artifact |\n|---|---|\n| clinical-research | `protocol_synopsis.md` + `sample_size.json` |\n| research-finance | `rd_program_budget.md` + `capex_opex_routing.json` |\n| market-research | `market_sizing.md` + `sample_plan.json` |\n| product-research | `research_plan.md` + `insight_synthesis.json` |\n\n## Anti-patterns (do not)\n\n- ❌ Present a clinical power\u002Fendpoint output as fact — it is an **estimate** with a named clinical owner\n- ❌ Auto-decide capitalize-vs-expense — route to a **named finance owner**\n- ❌ Report a market size as a single unsourced number — show **method + both-ways triangulation + assumptions**\n- ❌ Assert a product insight from a single participant — flag it as an **anecdote**\n- ❌ Run all 4 sub-skills \"to be thorough\" — pick one, digest, chain if needed\n\n## References\n\n- Clinical canon: ICH E8(R1)\u002FE9\u002FE9(R1), CONSORT, SPIRIT, FDA Multiple Endpoints\n- R&D finance canon: IAS 38, ASC 730, 2 CFR 200, Cooper stage-gate\n- Market canon: Cochran, Dillman, Kotler, Bessemer market-sizing\n- Product canon: Nielsen, Guest et al., Christensen JTBD, ResearchOps\u002FPolaris\n- Path-B build pattern: `documentation\u002Fimplementation\u002Fresearch-ops-expansion-plan.md`\n","# Research Operations — Domain Orchestrator\n\nThe Research Operations surface is **how the enterprise plans, funds, scopes, and synthesizes research** across four workstreams: clinical R&D, R&D finance, market research, and product research. This orchestrator forks its context, routes your inquiry to one of four sub-skills, then returns a digest. Heavy intake (protocol drafts, program ledgers, survey exports, interview transcripts) stays in the forked context.\n\nThis is the enterprise counterpart to the academic `research\u002F` domain. If your question is about **finding** literature, grants, or patents, use `research\u002F`. If it is about **planning, funding, scoping, or synthesizing** research as an operational discipline, you are in the right place.\n\n## When to invoke\n\n| Symptom | Sub-skill |\n|---|---|\n| \"We're designing a Phase 2 trial — what's the endpoint and sample size?\" | `clinical-research` |\n| \"What's our R&D program burn, and is this cost CapEx or OpEx?\" | `research-finance` |\n| \"What's the TAM for this product, and how do we survey the segment?\" | `market-research` |\n| \"How many users do we interview, and how do we synthesize the findings?\" | `product-research` |\n\n## Routing logic (deterministic)\n\nSame two-signal threshold pattern as `commercial-skills`. Single-signal → clarifying question. Mixed signals → highest-confidence first, chain second in a follow-up turn. Never silently chain.\n\n### Signal table\n\n| Signal class | Keywords | Sub-skill |\n|---|---|---|\n| **CLINICAL** | clinical trial, study design, protocol, endpoint, sample size, power, phase 1\u002F2\u002F3, biostatistics, eligibility, feasibility, estimand | `clinical-research` |\n| **RD_FINANCE** | R&D budget, program budget, burn, runway, F&A, indirect rate, overhead, capitalize vs expense, R&D capex, portfolio ROI, rNPV | `research-finance` |\n| **MARKET** | TAM, SAM, SOM, market sizing, survey design, sampling, margin of error, segmentation, competitive intelligence, market research | `market-research` |\n| **PRODUCT** | user interview, JTBD, usability test, concept test, prototype test, discovery research, research repository, insight synthesis, saturation | `product-research` |\n\n## Workflow (Matt Pocock grill discipline)\n\nDerived from Matt Pocock's `grill-with-docs` pattern: **explore-then-ask, one question per turn with a recommended answer, walk the decision tree depth-first, track dependencies, anchor every challenge in the research canon** (`references\u002F` of each sub-skill).\n\n### Step 1 — Explore before asking\n\nCheck the user's working directory first:\n- Is there a protocol draft, program ledger, TAM model, or interview guide already in the workspace?\n- Does the inquiry already disambiguate the lane (e.g., \"what sample size for a two-arm trial\" — that's `clinical-research`, no question needed)?\n- Is there an artifact filename that resolves the lane (`protocol.json` → clinical; `program-budget.json` → finance; `tam-model.json` → market; `interview-guide.md` → product)?\n\nIf the workspace resolves the lane, **route silently**.\n\n### Step 2 — If still ambiguous, ONE forcing question with a recommended answer\n\nMatt's rule: never bundle. Always recommend.\n\nPattern:\n```\nQ1\u002F1: [precise question naming the two candidate lanes]\nRecommended: [Lane X, because \u003Csignal-table rationale>]\n\n(Confirm, or override?)\n```\n\n### Step 3 — Decision-tree walk for multi-lane inquiries\n\nIf the inquiry legitimately crosses two lanes (e.g., \"design this trial AND budget it\" = CLINICAL + RD_FINANCE), walk depth-first:\n\n1. Highest-confidence lane first → run sub-skill in forked context → digest\n2. Ask: \"Now run [second lane]? Recommended: yes, because [dependency].\"\n3. Confirm before chaining.\n\nNever silently chain.\n\n### Step 4 — Invoke sub-skill in forked context\n\nForward original prompt + structured inputs (protocol JSON, program ledger CSV, market model, observation export).\n\n### Step 5 — Return digest with cited canon challenge\n\n≤ 200 words: analyzed, top 3 findings (anchored to a canon citation), top 3 next actions (named human owner where applicable), artifact path, and **one grill challenge** for the user. Examples:\n\n- \"Your power calc assumes a 0.5 effect size with no published anchor. ICH E9 requires a justified, clinically meaningful difference. Where did 0.5 come from?\"\n- \"Your TAM is a single top-down number (1% of a $40B market). Bessemer market-sizing discipline requires a bottoms-up cross-check. What's units × price × adoption?\"\n\n## Forcing-question library (grill-with-docs pattern)\n\nGrill the user on lane-defining decisions before invoking the sub-skill. One per turn, recommended answer, canon citation:\n\n- **CLINICAL lane**: \"Is your primary endpoint a clinical outcome or a surrogate — and if surrogate, is it validated for this indication? Recommended: clinical outcome unless the surrogate is on FDA's validated table. Canon: FDA Surrogate Endpoint Table; BEST glossary.\"\n- **RD_FINANCE lane**: \"Is this spend in the research phase or the development phase, and can you evidence technical feasibility? Recommended: research = expense; development = capitalize-candidate only with feasibility evidence, routed to a named finance owner. Canon: IAS 38; ASC 730.\"\n- **MARKET lane**: \"Is your TAM top-down or bottoms-up — and have you computed it both ways to triangulate? Recommended: both; reconcile the delta. Canon: Bessemer \u002F a16z market-sizing; Fermi estimation.\"\n- **PRODUCT lane**: \"Is this study generative (discover problems) or evaluative (test a solution)? Recommended: name it first; the method follows. Canon: Rohrer's landscape of UX research methods (NN\u002Fg).\"\n\nNever run a sub-skill until the lane-defining decision is locked.\n\n## Onboarding-first (per sub-skill)\n\nBefore invoking a sub-skill for the first time in a workspace, point the user at that skill's onboarding questionnaire so the tools run pre-configured to their context:\n\n```bash\npython3 skills\u002F\u003Csub-skill>\u002Fscripts\u002Fonboard.py          # interactive Q&A\npython3 skills\u002F\u003Csub-skill>\u002Fscripts\u002Fonboard.py --show    # questions + current config\n```\n\nEach sub-skill has its **own** question set (clinical: area\u002Falpha\u002Fpower\u002Fdropout\u002Fowners · finance: area\u002FF&A\u002Frunway\u002Fstandard\u002Fowner · market: profile\u002Fconfidence\u002FMoE\u002Fmethod · product: profile\u002Finsight-threshold\u002Fmethod\u002Fstakes). Answers persist to `~\u002F.config\u002Fresearch-ops\u002F\u003Csub-skill>.json` (or `.\u002F.research-ops\u002F\u003Csub-skill>.json` with `--scope project`) and are consumed automatically by every tool in that skill. Customization is mandatory discipline here, not decoration — surface the onboarding step when a user starts a fresh research workstream.\n\n## Autoresearch handoff (isolated, opt-in)\n\nEach sub-skill ships its own `skills\u002F\u003Csub-skill>\u002Fscripts\u002Far_evaluator.py` — an **isolated** bridge to `engineering\u002Fautoresearch-agent`. Invoke autoresearch **only when the user explicitly asks** to \"optimize\", \"improve\", or \"run a loop\". The handoff is per-skill (no shared coupling): the loop edits the skill's input file and the evaluator scores it (clinical → `feasibility_composite` higher; finance → `runway_months` higher; market → `tam_divergence` lower; product → `validated_insights` higher). Never auto-start a loop; never let the loop edit the evaluator.\n\n## Assumptions\n\n1. User has research authority OR is preparing analysis for someone who does.\n2. User wants **deterministic decision support**, not the final answer — a clinician approves the protocol, a controller books the entry, the human picks the market number.\n3. Inputs may be partial — every sub-skill ships a templated sample so the user can see the shape before filling in their own.\n\n## Non-goals\n\n- Not an EDC, clinical-trial-management system, accounting system, survey platform, or research repository.\n- Does not give clinical, accounting, or legal advice as fact. Every output is **a recommendation + named human owner**.\n- Does not store research history across sessions.\n\n## Distinct from\n\n- **`research\u002F` (academic)** — that domain **finds** literature, grants, and patents. This domain **plans, funds, scopes, and synthesizes** research.\n- **`ra-qm-team`** — that's **regulatory\u002FQM submission** (ISO 13485\u002F14971, MDR, FDA 510(k)\u002FPMA\u002FQSR). clinical-research designs the **study**; it routes submission out to ra-qm-team.\n- **`finance\u002Ffinancial-analysis`** — that's **corporate close + valuation**. research-finance manages **R&D program\u002Fportfolio spend**.\n- **`research\u002Fgrants`** — that's **funding discovery**. research-finance manages **money already won**.\n- **`product-team`** — that's **persona\u002Fjourney artifacts, discovery sprints, and live A\u002FB experiments**. product-research is the **method + repository discipline**.\n- **`marketing-skill`** — that's **campaign analytics and demand-gen**. market-research is **upstream methodology**.\n\n## Output artifacts\n\n| Sub-skill | Artifact |\n|---|---|\n| clinical-research | `protocol_synopsis.md` + `sample_size.json` |\n| research-finance | `rd_program_budget.md` + `capex_opex_routing.json` |\n| market-research | `market_sizing.md` + `sample_plan.json` |\n| product-research | `research_plan.md` + `insight_synthesis.json` |\n\n## Anti-patterns (do not)\n\n- ❌ Present a clinical power\u002Fendpoint output as fact — it is an **estimate** with a named clinical owner\n- ❌ Auto-decide capitalize-vs-expense — route to a **named finance owner**\n- ❌ Report a market size as a single unsourced number — show **method + both-ways triangulation + assumptions**\n- ❌ Assert a product insight from a single participant — flag it as an **anecdote**\n- ❌ Run all 4 sub-skills \"to be thorough\" — pick one, digest, chain if needed\n\n## References\n\n- Clinical canon: ICH E8(R1)\u002FE9\u002FE9(R1), CONSORT, SPIRIT, FDA Multiple Endpoints\n- R&D finance canon: IAS 38, ASC 730, 2 CFR 200, Cooper stage-gate\n- Market canon: Cochran, Dillman, Kotler, Bessemer market-sizing\n- Product canon: Nielsen, Guest et al., Christensen JTBD, ResearchOps\u002FPolaris\n- Path-B build pattern: `documentation\u002Fimplementation\u002Fresearch-ops-expansion-plan.md`\n",{"name":5,"tags":65,"author":67,"context":68,"license":14,"version":69,"description":7,"compatible_tools":70},[57,56,51,55,53,50,66],"orchestrator","claude-code-skills","fork","2.9.0",[71,72,73,74,75,76],"claude-code","codex-cli","cursor","antigravity","opencode","gemini-cli","main","068c2486ccc0f8d200a3db4d21ec137064eda3b4","skills\u002Fpublic-02-claude-skills-main-research-ops-skills-research-ops-skills-skill-b5dd8c2e","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing","https:\u002F\u002Fgithub.com\u002FJiayiV","2026-08-25 16:30:52",{"source":8,"skillName":5,"commands":84,"prompt":94,"zipAvailable":95},[85,88,91],{"tool":86,"command":87},"npx","npx skills add JiayiV\u002Fquestion_mixing --skill research-ops-skills",{"tool":89,"command":90},"bunx","bunx skills add JiayiV\u002Fquestion_mixing --skill research-ops-skills",{"tool":92,"command":93},"pnpm","pnpm dlx skills add JiayiV\u002Fquestion_mixing --skill research-ops-skills","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fpublic-02-claude-skills-main-research-ops-skills-research-ops-skills-skill-b5dd8c2e\u002FSKILL.md",true,[],[98,124,152,181,209,242],{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":60},"01M6NN9PJS9NHWPDG4QP93BP8F","webmcpify","Make a web app agent-ready — propose a WebMCP tool manifest, integrate, verify in a real browser, heal; unrelated code stays untouched. Use for \"webmcpify\", \"add WebMCP\", or \"expose app actions to AI agents\".","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fwebmcpify\u002FSKILL.md",[],[100,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123],"github","ai agents","web","notes","code","browser","https","actions","manifest","inventory","verify","tool","use","ready","unrelated","app","scope","for","webmcp",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":128,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":131,"publishedAt":60},"01M6NN6KZE5RBN6NGHN2WXV1RE","vardoger-analyze","Use when the user asks to personalize the GitHub Copilot CLI assistant, adapt Copilot to their style, use vardoger, or analyze their Copilot CLI conversation history. Reads the local session directory at `~\u002F.copilot\u002Fsession-state\u002F`, extracts recurring preferences and conventions, and writes a fenced personalization block into `~\u002F.copilot\u002Fcopilot-instructions.md`. Runs entirely on the user's machine via the local `vardoger` CLI (`pipx install vardoger`); no network calls and no uploads. Triggers: 'personalize my copilot', 'analyze my copilot history', 'tailor copilot to me', 'run vardoger', 'update my copilot instructions from history', 'make copilot learn my style'.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fvardoger-analyze\u002FSKILL.md",[],[132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151],"vardoger analyze","analyze","directory","cli","github copilot","copilot","run","update","learn","network","my style","install","conversation","me","local","github copilot cli","pipx","my","at","copilot instructions",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":162,"publishedAt":60},"01M6NN3CZGQP3XAJ04BM1NNYGT","transloadit-media-processing","Process media files (video, audio, images, documents) using Transloadit. Use when asked to encode video to HLS\u002FMP4, generate thumbnails, resize or watermark images, extract audio, concatenate clips, add subtitles, OCR documents, or run any media processing pipeline. Covers 86+ processing robots for file transformation at scale.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Ftransloadit-media-processing\u002FSKILL.md",[158,161],{"categoryNo":159,"categoryName":160,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":26,"categoryName":27,"taxonomyType":20,"taxonomyTypeDesc":21},[163,164,165,166,167,168,169,170,171,172,173,174,175,117,176,177,178,179,122,180],"transloadit media processing","Media","media processing","processing","MCP","mcp server","documents","subtitles","pipeline","ocr","Video","Audio","mp4","images","files","transformation","free","hls",{"skillNo":182,"name":183,"nameZh":6,"description":184,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":185,"categories":186,"keywords":190,"publishedAt":60},"01M6NN28E92F9GH4WFYQ2H1Y15","terraform-azurerm-set-diff-analyzer","Analyze Terraform plan JSON output for AzureRM Provider to distinguish between false-positive diffs (order-only changes in Set-type attributes) and actual resource changes. Use when reviewing terraform plan output for Azure resources like Application Gateway, Load Balancer, Firewall, Front Door, NSG, and other resources with Set-type attributes that cause spurious diffs due to internal ordering changes.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fterraform-azurerm-set-diff-analyzer\u002FSKILL.md",[187],{"categoryNo":188,"categoryName":189,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000011","运维与部署",[191,192,193,194,195,196,197,198,199,117,200,201,202,203,204,205,206,207,208,122],"terraform","SET","terraform azurerm set diff analyzer","diff","analyzer","diffs","azure","gateway","firewall","only","resource changes","provider","resources","load balancer","attributes","front","load","balancer",{"skillNo":210,"name":211,"nameZh":6,"description":212,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":213,"categories":214,"keywords":224,"publishedAt":60},"01M6NN1QMH2TDYWTH3XY3N5XPV","technical-job-search","Use this skill when a software engineer asks for help with job search tasks: parsing or analyzing a job description, tailoring a CV\u002Fresume, writing a cover letter, evaluating a job offer, or drafting a post-interview follow-up email. Do not activate for general career advice unrelated to an active job search action.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Ftechnical-job-search\u002FSKILL.md",[215,218,221],{"categoryNo":216,"categoryName":217,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":219,"categoryName":220,"taxonomyType":31,"taxonomyTypeDesc":32},"01JBSEEDCAT000000000000036","内容创作者",{"categoryNo":222,"categoryName":223,"taxonomyType":31,"taxonomyTypeDesc":32},"01JBSEEDCAT000000000000038","人力资源",[225,226,227,228,229,230,231,232,133,233,117,234,235,236,237,238,239,240,122,241],"search","Job Search","technical job search","technical","writing","tasks","email","parsing","interview","up","engineer","general","activate","do","task","cv","letter",{"skillNo":243,"name":244,"nameZh":6,"description":245,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":246,"categories":247,"keywords":248,"publishedAt":60},"01M6NMZFA67XYS1GS09WT42235","steno-mode","Shorthand-first response compression that cuts ~40% of response tokens while preserving technical precision and exact literals. Use when the user says \"steno mode\", \"shorthand mode\", \"compressed responses\", \"token reduction\", \"brief structured output\", or invokes \u002Fsteno. Supports four compression levels: lite, brief, court, machine. Do not trigger for requests needing polished prose such as onboarding\u002Ftutorial content, stakeholder or customer-facing copy, or teaching-focused explanations.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fsteno-mode\u002FSKILL.md",[],[249,250,251,252,253,228,254,117,238,255,256,257,258,259,260,261,262,263,122,264],"steno mode","mode","customer","brief","first","tutorial","teaching","tokens","as","trigger","structured output","machine","output","court","requests","response compression"]