[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7C6TP7MM2E6EE132H73RTWD":3,"skill-same:01M7C6TP7MM2E6EE132H73RTWD":100},{"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},"01M7C6TP7MM2E6EE132H73RTWD","product-skills",null,"Use when coordinating product work across the 12 bundled product sub-skills (RICE, OKRs, UX research, design tokens, competitive teardown, analytics, experiments, discovery, roadmaps, spec-to-repo, landing pages, SaaS scaffolding) or the 4 standalone product-team plugins (user stories, Apple HIG, code-to-PRD, research summarizer). Triggers on 'help me prioritize', 'plan a product experiment', 'we ship features nobody uses', 'run the discovery loop', 'is our OST sound'. Forks context to route to one sub-skill via a deterministic signal router and returns a digest; can also drive a continuous-discovery loop (Torres cadence tracker + OST linter as machine gates) or a full goal→plan→execute→verify→close run through the repo-wide agent-harness. Distinct from project-management (how to deliver vs what to build), marketing\u002Flanding (from-scratch pages), and engineering\u002Fagent-harness (the generic loop engine this orchestrator plugs into).","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\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.codex\u002Fskills\u002Fproduct-skills\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: \"product-skills\"\ndescription: \"Use when coordinating product work across the 12 bundled product sub-skills (RICE, OKRs, UX research, design tokens, competitive teardown, analytics, experiments, discovery, roadmaps, spec-to-repo, landing pages, SaaS scaffolding) or the 4 standalone product-team plugins (user stories, Apple HIG, code-to-PRD, research summarizer). Triggers on 'help me prioritize', 'plan a product experiment', 'we ship features nobody uses', 'run the discovery loop', 'is our OST sound'. Forks context to route to one sub-skill via a deterministic signal router and returns a digest; can also drive a continuous-discovery loop (Torres cadence tracker + OST linter as machine gates) or a full goal→plan→execute→verify→close run through the repo-wide agent-harness. Distinct from project-management (how to deliver vs what to build), marketing\u002Flanding (from-scratch pages), and engineering\u002Fagent-harness (the generic loop engine this orchestrator plugs into).\"\ncontext: fork\nversion: 2.11.1\nauthor: Alireza Rezvani\nlicense: MIT\ntags: [product, product-management, orchestrator, discovery, ux, analytics, agent-harness]\ncompatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]\n---\n\n# Product Team — Domain Orchestrator & Discovery Loop\n\nThis orchestrator does two jobs. **Routing:** fork context, classify a product inquiry\nwith `scripts\u002Fproduct_goal_router.py` across all 16 product-team lanes (12 bundled + 4\nstandalone plugins), run exactly one, return a digest. **Looping:** run product work as\nbounded agentic loops with machine-checkable gates — the continuous-discovery loop\n(weekly cadence scored by `discovery_cadence_tracker.py`, tree structure enforced by\n`ost_linter.py`) and goal-scale runs through the repo-wide agent-harness.\n\n## When to invoke\n\n| Symptom | Sub-skill |\n|---|---|\n| \"Prioritize features \u002F RICE \u002F PRD\" | `product-manager-toolkit` |\n| \"OKRs, strategy cascade\" | `product-strategist` |\n| \"Personas, usability, research synthesis\" | `ux-researcher-designer` |\n| \"Design tokens, WCAG contrast\" | `ui-design-system` |\n| \"Competitor matrix, teardown\" | `competitive-teardown` |\n| \"Retention, cohorts, funnels, KPIs\" | `product-analytics` |\n| \"A\u002FB test, sample size, hypothesis\" | `experiment-designer` |\n| \"Discovery, assumptions, opportunity trees\" | `product-discovery` |\n| \"Roadmap comms, release notes, changelog\" | `roadmap-communicator` |\n| \"Spec → runnable repo\" | `spec-to-repo` |\n| \"Landing page (Next.js\u002FTailwind)\" | `landing-page-generator` |\n| \"SaaS boilerplate\" | `saas-scaffolder` |\n| \"User stories, sprint capacity\" | `agile-product-owner` (standalone) |\n| \"Apple HIG audit\" | `apple-hig-expert` (standalone) |\n| \"PRD from an existing codebase\" | `code-to-prd` (standalone) |\n| \"Summarize papers\u002Farticles\" | `research-summarizer` (standalone) |\n\n## Routing logic (deterministic)\n\n```bash\npython3 scripts\u002Fproduct_goal_router.py --text \"\u003Cthe goal>\" --output json\n```\n\nExit 0 → `route_to` names the skill (with `skill_path`, including the standalone\nplugins): load its SKILL.md and follow its workflow. Exit 2 → ask ONE clarifying question\nnaming the listed candidates, with a recommended answer. Exit 3 → no signal: ask the user\nto restate the goal with the deliverable named. Never guess silently; never silently\nchain — digest first, confirm, then chain.\n\n## The discovery loop (the domain's recurring agentic loop)\n\nModern discovery is a weekly habit, not a project phase (Torres). Run it as a bounded\nloop with two machine gates:\n\n1. **Observe** — maintain `discovery_log.json` (interviews, assumption tests; shape in\n   `assets\u002Fsample_discovery_log.json`) and score the cadence:\n   ```bash\n   python3 scripts\u002Fdiscovery_cadence_tracker.py --input discovery_log.json\n   ```\n   Refuses on \u003C 2 interviews (exit 5) — there is no cadence to measure yet. Output:\n   health 0–100, verdict HEALTHY\u002FAT-RISK\u002FDORMANT, named gaps, and `next_loop_action`.\n2. **Choose** — the tracker's `next_loop_action` IS the choice: book the touchpoint,\n   re-anchor the guide on the outcome, or test the top untested assumption (route to\n   `product-discovery`'s assumption_mapper for prioritization).\n3. **Act** — run the interview \u002F assumption test with the routed sub-skill's tools.\n4. **Verify** — keep the tree structurally sound before it may drive a roadmap:\n   ```bash\n   python3 scripts\u002Fost_linter.py --input ost.json    # exit 2 = NEEDS-REWORK, fix before citing the tree\n   ```\n   Rules: one measurable outcome root (O1), opportunities are needs not features (O2),\n   targeted opportunities compare ≥ 2 solutions (O3), every solution has an assumption\n   test (O4), no orphan solutions (O5 — the feature-factory tell).\n5. **Record \u002F Repeat-or-stop** — update the log, keep the weekly streak alive. Stop\n   states: HEALTHY + validated assumption → graduate to `experiment-designer` (build the\n   A\u002FB gate) or `product-manager-toolkit` (PRD); DORMANT for 4+ weeks → escalate to the\n   product lead by name — do not quietly let discovery die.\n\nFor build-scale goals (\"turn this validated spec into a repo and verify it\"), compile\nthrough the repo-wide harness instead:\n\n```bash\npython3 engineering\u002Fagent-harness\u002Fskills\u002Fagent-harness\u002Fscripts\u002Fgoal_compiler.py \\\n  --goal \"\u003Cgoal>\" --manifest engineering\u002Fagent-harness\u002Fskills\u002Fagent-harness\u002Fassets\u002Fharnesses\u002Fproduct-team.json \\\n  --out .agent-harness\u002Fplan.json\n```\n\nThe domain's three strongest close-out gates plug in as task verifications:\n`..\u002Fspec-to-repo\u002Fscripts\u002Fvalidate_project.py` (exit 0), `code-to-prd`'s golden\n`expected_outputs\u002F`, and `research-summarizer`'s citation-count check.\n\n## Hard rules\n\n1. **Evidence before conviction**: no roadmap item cites the OST unless `ost_linter.py`\n   exits 0; no insight is asserted from a single participant (anecdote, not insight).\n2. **Outcome-first**: every loop hangs from one measurable outcome — the linter's O1 rule\n   is the intake gate.\n3. **Experiments are gated by math**: sample size from\n   `..\u002Fexperiment-designer\u002Fscripts\u002Fsample_size_calculator.py`, never gut feel; report the\n   MDE with the verdict.\n4. **Prioritization shows its framework**: RICE for steady-state, WSJF\u002Fcost-of-delay when\n   time sensitivity dominates, opportunity scoring for underserved needs — name which and\n   why (see [references\u002Fproduct_operating_model.md](references\u002Fproduct_operating_model.md)).\n5. **AI features ship with evals**: a golden set + rubric is the PRD's quality contract\n   for probabilistic features\n   ([references\u002Fai_product_evals.md](references\u002Fai_product_evals.md)).\n6. **Never modify a gate you are judged by**; exhausted budgets escalate to a named human,\n   never report as success.\n\n## Forcing-question library (grill-with-docs pattern)\n\nOne per turn, recommended answer, canon citation. Never run a sub-skill or start a loop\nuntil the lane-defining decision is locked:\n\n- **DISCOVERY lane**: \"What is the single outcome this discovery serves, stated with a\n  number? Recommended: write it as the OST root first — opportunities without an outcome\n  are a feature factory. Canon: Torres, *Continuous Discovery Habits*; opportunity\n  solution trees (producttalk.org).\"\n- **PRIORITIZE lane**: \"Does time sensitivity change this ranking — would delaying any\n  item a quarter erode its value? Recommended: if yes, run WSJF\u002Fcost-of-delay alongside\n  RICE and compare ranks; flag items whose rank flips on a one-step estimate change.\n  Canon: Reinertsen, *Principles of Product Development Flow*; SAFe WSJF false-precision\n  critique.\"\n- **EXPERIMENT lane**: \"What baseline rate and MDE justify this test's runtime?\n  Recommended: compute n first; if you can't reach it in 4 weeks, test a bigger lever.\n  Canon: statistical power analysis (experiment-designer).\"\n- **ANALYTICS lane**: \"Is your North Star a leading indicator of value exchange, or\n  revenue\u002Fvanity? Recommended: leading value metric with an input tree. Canon: Amplitude,\n  *The North Star Playbook*.\"\n- **STRATEGY lane**: \"Are these OKRs outcomes or shipping lists? Recommended: outcomes —\n  output OKRs are the #1 operating-model failure. Canon: Cagan, *Transformed* (SVPG,\n  2024).\"\n- **BUILD lanes (spec-to-repo \u002F saas-scaffolder)**: \"Which validated assumption says this\n  should be built at all? Recommended: link the OST test that survived; building is the\n  most expensive way to test an idea. Canon: Torres; Bland, *Testing Business Ideas*.\"\n\n## Assumptions\n\n1. The user owns (or advises the owner of) the product decision.\n2. Discovery data lives in the workspace as JSON logs — the loop is file-backed and\n   resumable; every tool ships `--sample` so the shape is visible first.\n3. The four standalone plugins are installed alongside the bundle (the router still\n   routes to them by path if not).\n\n## Non-goals\n\n- Not the delivery loop — sprint\u002Fflow\u002FJira work routes to `project-management`.\n- Not the generic loop engine — that is `engineering\u002Fagent-harness`; this orchestrator is\n  the product-domain adapter (router + discovery gates).\n- Not campaign marketing — `marketing\u002Flanding` builds from-scratch marketing pages;\n  `landing-page-generator` here scaffolds product Next.js\u002FTSX pages.\n\n## Output artifacts\n\n| Mode | Artifact |\n|---|---|\n| Route | Sub-skill's own artifact + ≤ 200-word digest with one canon-cited challenge |\n| Discovery loop | `discovery_log.json` + cadence report + linted `ost.json` |\n| Harness run | `.agent-harness\u002Fplan.json` + `state.json` + close handoff |\n\n## Anti-patterns (do not)\n\n- ❌ Run all 16 lanes \"to be thorough\" — route to one, digest, chain on confirmation\n- ❌ Cite an OST that fails the linter, or promote a single-participant anecdote to insight\n- ❌ Ship an AI feature whose PRD has no eval (golden set + rubric)\n- ❌ Let the discovery streak die silently — DORMANT escalates by name\n- ❌ Treat RICE as the only prioritization lens when deadlines dominate\n\n## References\n\n- [references\u002Fcontinuous_discovery_canon.md](references\u002Fcontinuous_discovery_canon.md) —\n  Torres, OST, assumption testing, JTBD switch interviews, story mapping\n- [references\u002Fproduct_operating_model.md](references\u002Fproduct_operating_model.md) — Cagan\n  *Transformed*, North Star framework, PLG benchmarks, WSJF\u002FODI vs RICE\n- [references\u002Fai_product_evals.md](references\u002Fai_product_evals.md) — evals-as-PRD, model\n  cards, evaluator-optimizer loops\n- Loop engine: `engineering\u002Fagent-harness` · Loop vocabulary: `loop-library`\n","# Product Team — Domain Orchestrator & Discovery Loop\n\nThis orchestrator does two jobs. **Routing:** fork context, classify a product inquiry\nwith `scripts\u002Fproduct_goal_router.py` across all 16 product-team lanes (12 bundled + 4\nstandalone plugins), run exactly one, return a digest. **Looping:** run product work as\nbounded agentic loops with machine-checkable gates — the continuous-discovery loop\n(weekly cadence scored by `discovery_cadence_tracker.py`, tree structure enforced by\n`ost_linter.py`) and goal-scale runs through the repo-wide agent-harness.\n\n## When to invoke\n\n| Symptom | Sub-skill |\n|---|---|\n| \"Prioritize features \u002F RICE \u002F PRD\" | `product-manager-toolkit` |\n| \"OKRs, strategy cascade\" | `product-strategist` |\n| \"Personas, usability, research synthesis\" | `ux-researcher-designer` |\n| \"Design tokens, WCAG contrast\" | `ui-design-system` |\n| \"Competitor matrix, teardown\" | `competitive-teardown` |\n| \"Retention, cohorts, funnels, KPIs\" | `product-analytics` |\n| \"A\u002FB test, sample size, hypothesis\" | `experiment-designer` |\n| \"Discovery, assumptions, opportunity trees\" | `product-discovery` |\n| \"Roadmap comms, release notes, changelog\" | `roadmap-communicator` |\n| \"Spec → runnable repo\" | `spec-to-repo` |\n| \"Landing page (Next.js\u002FTailwind)\" | `landing-page-generator` |\n| \"SaaS boilerplate\" | `saas-scaffolder` |\n| \"User stories, sprint capacity\" | `agile-product-owner` (standalone) |\n| \"Apple HIG audit\" | `apple-hig-expert` (standalone) |\n| \"PRD from an existing codebase\" | `code-to-prd` (standalone) |\n| \"Summarize papers\u002Farticles\" | `research-summarizer` (standalone) |\n\n## Routing logic (deterministic)\n\n```bash\npython3 scripts\u002Fproduct_goal_router.py --text \"\u003Cthe goal>\" --output json\n```\n\nExit 0 → `route_to` names the skill (with `skill_path`, including the standalone\nplugins): load its SKILL.md and follow its workflow. Exit 2 → ask ONE clarifying question\nnaming the listed candidates, with a recommended answer. Exit 3 → no signal: ask the user\nto restate the goal with the deliverable named. Never guess silently; never silently\nchain — digest first, confirm, then chain.\n\n## The discovery loop (the domain's recurring agentic loop)\n\nModern discovery is a weekly habit, not a project phase (Torres). Run it as a bounded\nloop with two machine gates:\n\n1. **Observe** — maintain `discovery_log.json` (interviews, assumption tests; shape in\n   `assets\u002Fsample_discovery_log.json`) and score the cadence:\n   ```bash\n   python3 scripts\u002Fdiscovery_cadence_tracker.py --input discovery_log.json\n   ```\n   Refuses on \u003C 2 interviews (exit 5) — there is no cadence to measure yet. Output:\n   health 0–100, verdict HEALTHY\u002FAT-RISK\u002FDORMANT, named gaps, and `next_loop_action`.\n2. **Choose** — the tracker's `next_loop_action` IS the choice: book the touchpoint,\n   re-anchor the guide on the outcome, or test the top untested assumption (route to\n   `product-discovery`'s assumption_mapper for prioritization).\n3. **Act** — run the interview \u002F assumption test with the routed sub-skill's tools.\n4. **Verify** — keep the tree structurally sound before it may drive a roadmap:\n   ```bash\n   python3 scripts\u002Fost_linter.py --input ost.json    # exit 2 = NEEDS-REWORK, fix before citing the tree\n   ```\n   Rules: one measurable outcome root (O1), opportunities are needs not features (O2),\n   targeted opportunities compare ≥ 2 solutions (O3), every solution has an assumption\n   test (O4), no orphan solutions (O5 — the feature-factory tell).\n5. **Record \u002F Repeat-or-stop** — update the log, keep the weekly streak alive. Stop\n   states: HEALTHY + validated assumption → graduate to `experiment-designer` (build the\n   A\u002FB gate) or `product-manager-toolkit` (PRD); DORMANT for 4+ weeks → escalate to the\n   product lead by name — do not quietly let discovery die.\n\nFor build-scale goals (\"turn this validated spec into a repo and verify it\"), compile\nthrough the repo-wide harness instead:\n\n```bash\npython3 engineering\u002Fagent-harness\u002Fskills\u002Fagent-harness\u002Fscripts\u002Fgoal_compiler.py \\\n  --goal \"\u003Cgoal>\" --manifest engineering\u002Fagent-harness\u002Fskills\u002Fagent-harness\u002Fassets\u002Fharnesses\u002Fproduct-team.json \\\n  --out .agent-harness\u002Fplan.json\n```\n\nThe domain's three strongest close-out gates plug in as task verifications:\n`..\u002Fspec-to-repo\u002Fscripts\u002Fvalidate_project.py` (exit 0), `code-to-prd`'s golden\n`expected_outputs\u002F`, and `research-summarizer`'s citation-count check.\n\n## Hard rules\n\n1. **Evidence before conviction**: no roadmap item cites the OST unless `ost_linter.py`\n   exits 0; no insight is asserted from a single participant (anecdote, not insight).\n2. **Outcome-first**: every loop hangs from one measurable outcome — the linter's O1 rule\n   is the intake gate.\n3. **Experiments are gated by math**: sample size from\n   `..\u002Fexperiment-designer\u002Fscripts\u002Fsample_size_calculator.py`, never gut feel; report the\n   MDE with the verdict.\n4. **Prioritization shows its framework**: RICE for steady-state, WSJF\u002Fcost-of-delay when\n   time sensitivity dominates, opportunity scoring for underserved needs — name which and\n   why (see [references\u002Fproduct_operating_model.md](references\u002Fproduct_operating_model.md)).\n5. **AI features ship with evals**: a golden set + rubric is the PRD's quality contract\n   for probabilistic features\n   ([references\u002Fai_product_evals.md](references\u002Fai_product_evals.md)).\n6. **Never modify a gate you are judged by**; exhausted budgets escalate to a named human,\n   never report as success.\n\n## Forcing-question library (grill-with-docs pattern)\n\nOne per turn, recommended answer, canon citation. Never run a sub-skill or start a loop\nuntil the lane-defining decision is locked:\n\n- **DISCOVERY lane**: \"What is the single outcome this discovery serves, stated with a\n  number? Recommended: write it as the OST root first — opportunities without an outcome\n  are a feature factory. Canon: Torres, *Continuous Discovery Habits*; opportunity\n  solution trees (producttalk.org).\"\n- **PRIORITIZE lane**: \"Does time sensitivity change this ranking — would delaying any\n  item a quarter erode its value? Recommended: if yes, run WSJF\u002Fcost-of-delay alongside\n  RICE and compare ranks; flag items whose rank flips on a one-step estimate change.\n  Canon: Reinertsen, *Principles of Product Development Flow*; SAFe WSJF false-precision\n  critique.\"\n- **EXPERIMENT lane**: \"What baseline rate and MDE justify this test's runtime?\n  Recommended: compute n first; if you can't reach it in 4 weeks, test a bigger lever.\n  Canon: statistical power analysis (experiment-designer).\"\n- **ANALYTICS lane**: \"Is your North Star a leading indicator of value exchange, or\n  revenue\u002Fvanity? Recommended: leading value metric with an input tree. Canon: Amplitude,\n  *The North Star Playbook*.\"\n- **STRATEGY lane**: \"Are these OKRs outcomes or shipping lists? Recommended: outcomes —\n  output OKRs are the #1 operating-model failure. Canon: Cagan, *Transformed* (SVPG,\n  2024).\"\n- **BUILD lanes (spec-to-repo \u002F saas-scaffolder)**: \"Which validated assumption says this\n  should be built at all? Recommended: link the OST test that survived; building is the\n  most expensive way to test an idea. Canon: Torres; Bland, *Testing Business Ideas*.\"\n\n## Assumptions\n\n1. The user owns (or advises the owner of) the product decision.\n2. Discovery data lives in the workspace as JSON logs — the loop is file-backed and\n   resumable; every tool ships `--sample` so the shape is visible first.\n3. The four standalone plugins are installed alongside the bundle (the router still\n   routes to them by path if not).\n\n## Non-goals\n\n- Not the delivery loop — sprint\u002Fflow\u002FJira work routes to `project-management`.\n- Not the generic loop engine — that is `engineering\u002Fagent-harness`; this orchestrator is\n  the product-domain adapter (router + discovery gates).\n- Not campaign marketing — `marketing\u002Flanding` builds from-scratch marketing pages;\n  `landing-page-generator` here scaffolds product Next.js\u002FTSX pages.\n\n## Output artifacts\n\n| Mode | Artifact |\n|---|---|\n| Route | Sub-skill's own artifact + ≤ 200-word digest with one canon-cited challenge |\n| Discovery loop | `discovery_log.json` + cadence report + linted `ost.json` |\n| Harness run | `.agent-harness\u002Fplan.json` + `state.json` + close handoff |\n\n## Anti-patterns (do not)\n\n- ❌ Run all 16 lanes \"to be thorough\" — route to one, digest, chain on confirmation\n- ❌ Cite an OST that fails the linter, or promote a single-participant anecdote to insight\n- ❌ Ship an AI feature whose PRD has no eval (golden set + rubric)\n- ❌ Let the discovery streak die silently — DORMANT escalates by name\n- ❌ Treat RICE as the only prioritization lens when deadlines dominate\n\n## References\n\n- [references\u002Fcontinuous_discovery_canon.md](references\u002Fcontinuous_discovery_canon.md) —\n  Torres, OST, assumption testing, JTBD switch interviews, story mapping\n- [references\u002Fproduct_operating_model.md](references\u002Fproduct_operating_model.md) — Cagan\n  *Transformed*, North Star framework, PLG benchmarks, WSJF\u002FODI vs RICE\n- [references\u002Fai_product_evals.md](references\u002Fai_product_evals.md) — evals-as-PRD, model\n  cards, evaluator-optimizer loops\n- Loop engine: `engineering\u002Fagent-harness` · Loop vocabulary: `loop-library`\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],"product","product-management","orchestrator","discovery","ux","analytics","agent-harness","Alireza Rezvani","fork","2.11.1",[36,37,38,39,40,41],"claude-code","codex-cli","cursor","antigravity","opencode","gemini-cli","main","46d43f3edecde97283ac410e9051444d90120cfb","repos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.codex\u002Fskills\u002Fproduct-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 product-skills",{"tool":54,"command":55},"bunx","bunx skills add azhri1990\u002Fjarvis-mega-repo --skill product-skills",{"tool":57,"command":58},"pnpm","pnpm dlx skills add azhri1990\u002Fjarvis-mega-repo --skill product-skills","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.codex\u002Fskills\u002Fproduct-skills\u002FSKILL.md",true,[62,68,72,78,82,86,92,96],{"path":63,"resourceType":64,"resourceTypeDesc":65,"sizeBytes":66,"sourceHtmlUrl":67},"assets\u002Fsample_discovery_log.json","ASSET","资产文件",965,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.codex\u002Fskills\u002Fproduct-skills\u002Fassets\u002Fsample_discovery_log.json",{"path":69,"resourceType":64,"resourceTypeDesc":65,"sizeBytes":70,"sourceHtmlUrl":71},"assets\u002Fsample_ost.json",984,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.codex\u002Fskills\u002Fproduct-skills\u002Fassets\u002Fsample_ost.json",{"path":73,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":76,"sourceHtmlUrl":77},"references\u002Fai_product_evals.md","REFERENCE","参考文档",3711,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.codex\u002Fskills\u002Fproduct-skills\u002Freferences\u002Fai_product_evals.md",{"path":79,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002Fcontinuous_discovery_canon.md",3986,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.codex\u002Fskills\u002Fproduct-skills\u002Freferences\u002Fcontinuous_discovery_canon.md",{"path":83,"resourceType":74,"resourceTypeDesc":75,"sizeBytes":84,"sourceHtmlUrl":85},"references\u002Fproduct_operating_model.md",3956,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.codex\u002Fskills\u002Fproduct-skills\u002Freferences\u002Fproduct_operating_model.md",{"path":87,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":90,"sourceHtmlUrl":91},"scripts\u002Fdiscovery_cadence_tracker.py","SCRIPT","脚本",7976,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.codex\u002Fskills\u002Fproduct-skills\u002Fscripts\u002Fdiscovery_cadence_tracker.py",{"path":93,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":94,"sourceHtmlUrl":95},"scripts\u002Fost_linter.py",7266,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.codex\u002Fskills\u002Fproduct-skills\u002Fscripts\u002Fost_linter.py",{"path":97,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":98,"sourceHtmlUrl":99},"scripts\u002Fproduct_goal_router.py",8743,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.codex\u002Fskills\u002Fproduct-skills\u002Fscripts\u002Fproduct_goal_router.py",[101,108,115,122,129,136],{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":107,"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":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":114,"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":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":121,"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":123,"name":124,"nameZh":6,"description":125,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":126,"categories":127,"keywords":128,"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":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":135,"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":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":142,"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",[],[]]