[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7CPTAFAMY3KDM6AE96P1BAT":3,"skill-same:01M7CPTAFAMY3KDM6AE96P1BAT":62},{"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":42,"commitSha":43,"skillDirectory":44,"forksCount":14,"repositoryHtmlUrl":45,"repositoryHomepageUrl":6,"ownerHtmlUrl":46,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":47,"install":48,"resources":61},"01M7CPTAFAMY3KDM6AE96P1BAT","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).","azhri1990\u002Fjarvis-mega-repo","azhri1990","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F281207543?v=4","01M7BMC0WHXB2ZA9SH1PFJDYNS","Nashriq Mokhtar","01M7C4WMQVQ450E0NA99BG7BHA",0,"MIT","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002F.gemini\u002Fskills\u002Fresearch-ops-skills\u002FSKILL.md",[],[],"2026-11-15 14:19:17",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":24,"author":32,"context":33,"license":15,"version":34,"description":7,"compatible_tools":35},[25,26,27,28,29,30,31],"research-ops","clinical-research","research-finance","market-research","product-research","rd","orchestrator","claude-code-skills","fork","2.9.0",[36,37,38,39,40,41],"claude-code","codex-cli","cursor","antigravity","opencode","gemini-cli","main","46d43f3edecde97283ac410e9051444d90120cfb","repos\u002Fproject-genesis\u002Fclaude-skills\u002F.gemini\u002Fskills\u002Fresearch-ops-skills","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo","https:\u002F\u002Fgithub.com\u002Fazhri1990","2026-10-10 07:58:58",{"source":8,"skillName":5,"commands":49,"prompt":59,"zipAvailable":60},[50,53,56],{"tool":51,"command":52},"npx","npx skills add azhri1990\u002Fjarvis-mega-repo --skill research-ops-skills",{"tool":54,"command":55},"bunx","bunx skills add azhri1990\u002Fjarvis-mega-repo --skill research-ops-skills",{"tool":57,"command":58},"pnpm","pnpm dlx skills add azhri1990\u002Fjarvis-mega-repo --skill research-ops-skills","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002F.gemini\u002Fskills\u002Fresearch-ops-skills\u002FSKILL.md",true,[],[63,70,77,84,91,98],{"skillNo":64,"name":65,"nameZh":6,"description":66,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":67,"categories":68,"keywords":69,"publishedAt":19},"01M7CYPF9V9EMA4ZXS1TTCMBFH","syllabus","Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Fsyllabus\u002Fskills\u002Fsyllabus\u002FSKILL.md",[],[],{"skillNo":71,"name":72,"nameZh":6,"description":73,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":19},"01M7CYNRJDTQCRF5KGPMBMSB1S","pulse","Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X\u002FTwitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend\u002Fsentiment\u002Fproblems\u002Fopportunities\u002Fcomparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Fpulse\u002Fskills\u002Fpulse\u002FSKILL.md",[],[],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":19},"01M7CYNAVSE84QD7HAWZWTY2FS","patent","Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search \u002F freedom-to-operate \u002F competitive landscape \u002F acquisition diligence \u002F litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Use when the user asks for patent searching or analysis (e.g., 'prior art search for [invention]', 'freedom to operate analysis for [product]'). Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Fpatent\u002Fskills\u002Fpatent\u002FSKILL.md",[],[],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":19},"01M7CYN0CE8P5ZN7WV2X06HKPC","notebooklm","Browser automation skill for controlling Google's NotebookLM. Use when the user wants anything done in NotebookLM (e.g., 'open NotebookLM', 'check my [name] notebook', 'ask my notebook about X', 'add [source] to NotebookLM', 'generate a Video Overview from my notebook', 'use NotebookLM Studio'). Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio\u002FVideo Overviews, Mind Maps, Reports incl. Briefing Doc\u002FStudy Guide\u002FFAQ, Flashcards, Quiz, slide decks, infographics — discover the exact set from the live Studio panel; the UI evolves fast), and creating new notebooks. Requires browser automation environment — fails gracefully when unavailable.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Fnotebooklm\u002Fskills\u002Fnotebooklm\u002FSKILL.md",[],[],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":97,"publishedAt":19},"01M7CYMHZ3QRJ9BKRB859KW8TQ","litreview","Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER \u002F Decomposition \u002F hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5\u002F10\u002F20 queries) controls coverage vs. speed. Output is a 'launching pad' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'). Do NOT use for single one-off paper searches wanting a quick list — that's a plain PubMed\u002FOpenAlex (or Consensus) query.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Flitreview\u002Fskills\u002Flitreview\u002FSKILL.md",[],[],{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":104,"publishedAt":19},"01M7CYM3D8YKV7R6DT4RNJSF0A","grants","NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance\u002FInnovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget\u002Fscope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.","https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002Fproject-genesis\u002Fclaude-skills\u002Fresearch\u002Fgrants\u002Fskills\u002Fgrants\u002FSKILL.md",[],[]]