[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7CCB4CJWMYPWMAT7AGSCA4D":3,"skill-same:01M7CCB4CJWMYPWMAT7AGSCA4D":102},{"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":44,"commitSha":45,"skillDirectory":46,"forksCount":14,"repositoryHtmlUrl":47,"repositoryHomepageUrl":6,"ownerHtmlUrl":48,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":49,"install":50,"resources":63},"01M7CCB4CJWMYPWMAT7AGSCA4D","pm-skills",null,"Use when coordinating project-delivery work across the 8 project-management sub-skills — sprint\u002Fvelocity analytics, portfolio health, Jira\u002FJQL, Confluence, Atlassian admin, templates, meeting analysis, team comms. Triggers on 'our sprints feel off', 'project health report', 'audit our Jira permissions', 'when will it be done', 'run the delivery loop'. Forks context to route to one sub-skill via a deterministic signal router and returns a digest; can also drive a full goal→plan→execute→verify→close delivery loop through the repo-wide agent-harness with Jira MCP data bridged into the domain's analytics tools. Distinct from product-team (what to build vs how to deliver it), business-operations (internal ops), 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.hermes\u002Fskills\u002Fclaude-skills\u002Fproject-management\u002Fpm-skills\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: \"pm-skills\"\ndescription: \"Use when coordinating project-delivery work across the 8 project-management sub-skills — sprint\u002Fvelocity analytics, portfolio health, Jira\u002FJQL, Confluence, Atlassian admin, templates, meeting analysis, team comms. Triggers on 'our sprints feel off', 'project health report', 'audit our Jira permissions', 'when will it be done', 'run the delivery loop'. Forks context to route to one sub-skill via a deterministic signal router and returns a digest; can also drive a full goal→plan→execute→verify→close delivery loop through the repo-wide agent-harness with Jira MCP data bridged into the domain's analytics tools. Distinct from product-team (what to build vs how to deliver it), business-operations (internal ops), and engineering\u002Fagent-harness (the generic loop engine this orchestrator plugs into).\"\ncontext: fork\nversion: 2.11.1\nauthor: Alireza Rezvani\nlicense: MIT\ntags: [project-management, orchestrator, jira, confluence, atlassian, scrum, agile, flow-metrics, agent-harness]\ncompatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]\n---\n\n# Project Management — Domain Orchestrator & Delivery Loop\n\nThis orchestrator does two jobs. **Routing:** fork context, classify a PM inquiry with\n`scripts\u002Fpm_goal_router.py`, run exactly one of the 8 sub-skills, return a digest.\n**Looping:** turn a delivery goal into a bounded agentic loop — pull live Jira data via the\nbundled Atlassian MCP, bridge it into the domain's deterministic analytics tools, verify\nevery step with machine-run gates, and refuse to close until everything is verified or a\nhuman waives it. The bundled `.mcp.json` wires the Atlassian Remote MCP\n(`https:\u002F\u002Fmcp.atlassian.com\u002Fv1\u002Fsse`, OAuth handled by Claude Code).\n\n## When to invoke\n\n| Symptom | Sub-skill |\n|---|---|\n| \"Project\u002Fportfolio health, risk EMV, capacity\" | `senior-pm` |\n| \"Sprint velocity, retro follow-through, ceremony health, when-will-it-be-done\" | `scrum-master` |\n| \"JQL, Jira workflows, boards, automation\" | `jira-expert` |\n| \"Confluence spaces, page trees, content audits\" | `confluence-expert` |\n| \"Users, groups, permissions, SSO\" | `atlassian-admin` |\n| \"Reusable Jira\u002FConfluence templates\" | `atlassian-templates` |\n| \"Meeting transcripts, talk time, action items\" | `meeting-analyzer` |\n| \"Status updates, 3P updates, stakeholder comms\" | `team-communications` |\n\n## Routing logic (deterministic)\n\nRun the router — do not eyeball the table when a script can decide:\n\n```bash\npython3 scripts\u002Fpm_goal_router.py --text \"\u003Cthe goal>\" --output json\n```\n\nExit 0 → `route_to` names the sub-skill: load its SKILL.md and follow its workflow.\nExit 2 → ask ONE clarifying question naming the listed candidates, with a recommended\nanswer. Exit 3 → no signal: ask the user to restate the goal with the deliverable named.\nNever guess silently; never silently chain a second sub-skill — digest first, confirm, then\nchain.\n\n## The delivery loop (agentic)\n\nFor goals (not questions) — \"get sprint 14 to a verified close\", \"produce a portfolio\nhealth report from live Jira\", \"make our flow metrics visible weekly\" — run the\nloop-library contract (Observe → Choose → Act → Verify → Record → Repeat-or-stop):\n\n1. **Observe** — pull fresh state: `mcp__atlassian__searchJiraIssuesUsingJql` (get\n   `cloudId` via `getAccessibleAtlassianResources` first), save the result JSON, then\n   bridge it:\n   ```bash\n   python3 scripts\u002Fjira_snapshot_bridge.py --input snapshot.json --to flow            # WIP, throughput, cycle time p50\u002F85\u002F95, work-item age, SLE, aging alerts\n   python3 scripts\u002Fjira_snapshot_bridge.py --input snapshot.json --to sprint > s.json # scrum-master schema\n   python3 ..\u002Fscrum-master\u002Fscripts\u002Fvelocity_analyzer.py s.json                        # velocity + volatility + forecast\n   ```\n   Add `--forecast N` for a seeded Monte Carlo \"when will N items be done\" answer\n   (refuses on \u003C 10 completed items — thin history forecasts are lies).\n2. **Choose** — route the next task with `pm_goal_router.py`; one task at a time.\n3. **Act** — execute with the routed sub-skill's own tools per its SKILL.md.\n4. **Verify** — gate the plan and every close with:\n   ```bash\n   python3 scripts\u002Fdelivery_loop_gate.py --plan plan.json --mode plan    # exit 2 = blocked\n   python3 scripts\u002Fdelivery_loop_gate.py --plan plan.json --mode close   # exit 4 = close refused\n   ```\n   Plus each sub-skill's own gates (scrum-master's ≥ 3-sprints rule, atlassian-admin's\n   VERIFY steps). Never adjudicate your own verification.\n5. **Record \u002F Repeat-or-stop** — for multi-task goals, run the state through the repo-wide\n   harness (it enforces attempt caps, iteration budgets, and evidence logging):\n   ```bash\n   python3 engineering\u002Fagent-harness\u002Fskills\u002Fagent-harness\u002Fscripts\u002Fgoal_compiler.py \\\n     --goal \"\u003Cgoal>\" --manifest engineering\u002Fagent-harness\u002Fskills\u002Fagent-harness\u002Fassets\u002Fharnesses\u002Fproject-management.json \\\n     --out .agent-harness\u002Fplan.json\n   python3 engineering\u002Fagent-harness\u002Fskills\u002Fagent-harness\u002Fscripts\u002Floop_controller.py init|next|record|verify|close ...\n   ```\n   Terminal states: success, clean no-op, blocked, approval-required, exhausted,\n   stagnated. An exhausted budget is an escalation — never a success report.\n\n## Hard rules (agentic delegation governance)\n\n1. **Agents are contributors, never owners** (Linear model): every loop task carries a\n   named human owner; agent-executed tasks also carry a named human reviewer.\n   `delivery_loop_gate.py` enforces this (G1\u002FG2).\n2. **Acceptance must be machine-checkable** — a command, or a criterion with a threshold.\n   \"Looks good\" is not a gate (G3).\n3. **Every Jira\u002FConfluence write is auditable and reversible-first** (Rovo discipline):\n   never `transitionJiraIssue` to Done without verify evidence; destructive\u002Firreversible\n   actions (deletes, permission changes, org-wide admin) are approval-required terminal\n   states, not loop steps.\n4. **Never modify a gate you are judged by** — same locked-evaluator invariant as\n   autoresearch-agent.\n5. **Forecasts are ranges with confidence, never dates** — Monte Carlo percentiles\n   (p50\u002Fp70\u002Fp85\u002Fp95), per Vacanti. Single-date promises are the anti-pattern.\n6. **Max 3 attempts per task, 12 loop iterations per goal** — then escalate to the named\n   human with the evidence log.\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- **SPRINT lane**: \"Do you want to *measure* flow (cycle time, WIP, throughput, age) or\n  *forecast* delivery? Recommended: measure first — a forecast off unmeasured flow is\n  noise. Canon: Kanban Guide (May 2025) four mandatory flow measures; Vacanti,\n  *Actionable Agile Metrics*.\"\n- **HEALTH lane**: \"Is your project status self-reported RAG or derived from signals?\n  Recommended: derive it (schedule variance, aging WIP, scope churn) and diff against the\n  self-report — that diff finds watermelon projects. Canon: Kanban Guide 2025;\n  DORA 2025 (AI amplifies, doesn't fix, weak signals).\"\n- **JIRA lane**: \"Is this configuration change deployable to a test project first?\n  Recommended: always stage in a test project; jira-expert's workflow validator must exit\n  0 before production. Canon: jira-expert validation workflow.\"\n- **ADMIN lane**: \"Is this action reversible, and who approves it? Recommended: name the\n  approver before touching permissions — admin actions are approval-required terminal\n  states in any loop. Canon: atlassian-admin VERIFY discipline; loop-library stop states.\"\n- **LOOP intake**: \"What single observable outcome means DONE, and which command proves\n  it? Recommended: a named artifact + a command that exits 0 against it. Canon:\n  agent-harness verifier's law; Anthropic, *Building Effective Agents* (evaluator needs\n  clear criteria).\"\n- **MEETINGS\u002FCOMMS lanes**: \"Could this meeting be an async written update? Recommended:\n  status-broadcast meetings convert to async 3P updates; decision meetings keep sync.\n  Canon: GitLab async-first handbook.\"\n\n## Assumptions\n\n1. The user has (or is preparing analysis for someone with) delivery authority.\n2. Jira\u002FConfluence access goes through the bundled MCP; capabilities NOT in\n   `project-management\u002Freferences\u002Fatlassian-mcp-tools.md` (project\u002Fsprint\u002Fboard\u002Fspace\n   creation, admin config) are done in the web UI — never invent tool names.\n3. Inputs may be partial — every tool ships `--sample` so the shape is visible first.\n\n## Non-goals\n\n- Not a replacement for the sub-skills — the orchestrator routes and loops; the\n  sub-skills do the work.\n- Not the generic loop engine — that is `engineering\u002Fagent-harness`; this orchestrator is\n  the PM-domain adapter (data bridge + governance gate + lane router).\n- Does not decide *what* to build — that's `product-team`.\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| Flow report | `flow_metrics.json` (bridge output) with SLE conformance + aging alerts |\n| Delivery loop | `.agent-harness\u002Fplan.json` + `state.json` + gate verdicts + close handoff |\n\n## Anti-patterns (do not)\n\n- ❌ Run all 8 sub-skills \"to be thorough\" — route to one, digest, chain on confirmation\n- ❌ Report sprint health or forecasts from hand-typed numbers when a Jira snapshot is one\n  MCP call away — bridge real data\n- ❌ Close a loop with unverified tasks, or report an exhausted budget as success\n- ❌ Let an agent be the assignee of record — humans own, agents contribute\n- ❌ Auto-transition Jira issues or touch permissions inside a loop without the named\n  approver\n\n## References\n\n- [references\u002Fflow_forecasting_canon.md](references\u002Fflow_forecasting_canon.md) — Kanban\n  Guide 2025, Vacanti Monte Carlo, DORA 2025, EBM, SPACE\n- [references\u002Fagentic_delivery_governance.md](references\u002Fagentic_delivery_governance.md) —\n  Linear\u002FRovo delegation models, Anthropic agent patterns, audit discipline\n- [references\u002Fpm_loop_playbook.md](references\u002Fpm_loop_playbook.md) — the five reusable PM\n  loops (sprint, health, retro-action, RAID-hygiene, comms) mapped to the loop contract\n- Canonical MCP tool list: `project-management\u002Freferences\u002Fatlassian-mcp-tools.md`\n- Loop engine: `engineering\u002Fagent-harness` · Loop vocabulary: `loop-library`\n","# Project Management — Domain Orchestrator & Delivery Loop\n\nThis orchestrator does two jobs. **Routing:** fork context, classify a PM inquiry with\n`scripts\u002Fpm_goal_router.py`, run exactly one of the 8 sub-skills, return a digest.\n**Looping:** turn a delivery goal into a bounded agentic loop — pull live Jira data via the\nbundled Atlassian MCP, bridge it into the domain's deterministic analytics tools, verify\nevery step with machine-run gates, and refuse to close until everything is verified or a\nhuman waives it. The bundled `.mcp.json` wires the Atlassian Remote MCP\n(`https:\u002F\u002Fmcp.atlassian.com\u002Fv1\u002Fsse`, OAuth handled by Claude Code).\n\n## When to invoke\n\n| Symptom | Sub-skill |\n|---|---|\n| \"Project\u002Fportfolio health, risk EMV, capacity\" | `senior-pm` |\n| \"Sprint velocity, retro follow-through, ceremony health, when-will-it-be-done\" | `scrum-master` |\n| \"JQL, Jira workflows, boards, automation\" | `jira-expert` |\n| \"Confluence spaces, page trees, content audits\" | `confluence-expert` |\n| \"Users, groups, permissions, SSO\" | `atlassian-admin` |\n| \"Reusable Jira\u002FConfluence templates\" | `atlassian-templates` |\n| \"Meeting transcripts, talk time, action items\" | `meeting-analyzer` |\n| \"Status updates, 3P updates, stakeholder comms\" | `team-communications` |\n\n## Routing logic (deterministic)\n\nRun the router — do not eyeball the table when a script can decide:\n\n```bash\npython3 scripts\u002Fpm_goal_router.py --text \"\u003Cthe goal>\" --output json\n```\n\nExit 0 → `route_to` names the sub-skill: load its SKILL.md and follow its workflow.\nExit 2 → ask ONE clarifying question naming the listed candidates, with a recommended\nanswer. Exit 3 → no signal: ask the user to restate the goal with the deliverable named.\nNever guess silently; never silently chain a second sub-skill — digest first, confirm, then\nchain.\n\n## The delivery loop (agentic)\n\nFor goals (not questions) — \"get sprint 14 to a verified close\", \"produce a portfolio\nhealth report from live Jira\", \"make our flow metrics visible weekly\" — run the\nloop-library contract (Observe → Choose → Act → Verify → Record → Repeat-or-stop):\n\n1. **Observe** — pull fresh state: `mcp__atlassian__searchJiraIssuesUsingJql` (get\n   `cloudId` via `getAccessibleAtlassianResources` first), save the result JSON, then\n   bridge it:\n   ```bash\n   python3 scripts\u002Fjira_snapshot_bridge.py --input snapshot.json --to flow            # WIP, throughput, cycle time p50\u002F85\u002F95, work-item age, SLE, aging alerts\n   python3 scripts\u002Fjira_snapshot_bridge.py --input snapshot.json --to sprint > s.json # scrum-master schema\n   python3 ..\u002Fscrum-master\u002Fscripts\u002Fvelocity_analyzer.py s.json                        # velocity + volatility + forecast\n   ```\n   Add `--forecast N` for a seeded Monte Carlo \"when will N items be done\" answer\n   (refuses on \u003C 10 completed items — thin history forecasts are lies).\n2. **Choose** — route the next task with `pm_goal_router.py`; one task at a time.\n3. **Act** — execute with the routed sub-skill's own tools per its SKILL.md.\n4. **Verify** — gate the plan and every close with:\n   ```bash\n   python3 scripts\u002Fdelivery_loop_gate.py --plan plan.json --mode plan    # exit 2 = blocked\n   python3 scripts\u002Fdelivery_loop_gate.py --plan plan.json --mode close   # exit 4 = close refused\n   ```\n   Plus each sub-skill's own gates (scrum-master's ≥ 3-sprints rule, atlassian-admin's\n   VERIFY steps). Never adjudicate your own verification.\n5. **Record \u002F Repeat-or-stop** — for multi-task goals, run the state through the repo-wide\n   harness (it enforces attempt caps, iteration budgets, and evidence logging):\n   ```bash\n   python3 engineering\u002Fagent-harness\u002Fskills\u002Fagent-harness\u002Fscripts\u002Fgoal_compiler.py \\\n     --goal \"\u003Cgoal>\" --manifest engineering\u002Fagent-harness\u002Fskills\u002Fagent-harness\u002Fassets\u002Fharnesses\u002Fproject-management.json \\\n     --out .agent-harness\u002Fplan.json\n   python3 engineering\u002Fagent-harness\u002Fskills\u002Fagent-harness\u002Fscripts\u002Floop_controller.py init|next|record|verify|close ...\n   ```\n   Terminal states: success, clean no-op, blocked, approval-required, exhausted,\n   stagnated. An exhausted budget is an escalation — never a success report.\n\n## Hard rules (agentic delegation governance)\n\n1. **Agents are contributors, never owners** (Linear model): every loop task carries a\n   named human owner; agent-executed tasks also carry a named human reviewer.\n   `delivery_loop_gate.py` enforces this (G1\u002FG2).\n2. **Acceptance must be machine-checkable** — a command, or a criterion with a threshold.\n   \"Looks good\" is not a gate (G3).\n3. **Every Jira\u002FConfluence write is auditable and reversible-first** (Rovo discipline):\n   never `transitionJiraIssue` to Done without verify evidence; destructive\u002Firreversible\n   actions (deletes, permission changes, org-wide admin) are approval-required terminal\n   states, not loop steps.\n4. **Never modify a gate you are judged by** — same locked-evaluator invariant as\n   autoresearch-agent.\n5. **Forecasts are ranges with confidence, never dates** — Monte Carlo percentiles\n   (p50\u002Fp70\u002Fp85\u002Fp95), per Vacanti. Single-date promises are the anti-pattern.\n6. **Max 3 attempts per task, 12 loop iterations per goal** — then escalate to the named\n   human with the evidence log.\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- **SPRINT lane**: \"Do you want to *measure* flow (cycle time, WIP, throughput, age) or\n  *forecast* delivery? Recommended: measure first — a forecast off unmeasured flow is\n  noise. Canon: Kanban Guide (May 2025) four mandatory flow measures; Vacanti,\n  *Actionable Agile Metrics*.\"\n- **HEALTH lane**: \"Is your project status self-reported RAG or derived from signals?\n  Recommended: derive it (schedule variance, aging WIP, scope churn) and diff against the\n  self-report — that diff finds watermelon projects. Canon: Kanban Guide 2025;\n  DORA 2025 (AI amplifies, doesn't fix, weak signals).\"\n- **JIRA lane**: \"Is this configuration change deployable to a test project first?\n  Recommended: always stage in a test project; jira-expert's workflow validator must exit\n  0 before production. Canon: jira-expert validation workflow.\"\n- **ADMIN lane**: \"Is this action reversible, and who approves it? Recommended: name the\n  approver before touching permissions — admin actions are approval-required terminal\n  states in any loop. Canon: atlassian-admin VERIFY discipline; loop-library stop states.\"\n- **LOOP intake**: \"What single observable outcome means DONE, and which command proves\n  it? Recommended: a named artifact + a command that exits 0 against it. Canon:\n  agent-harness verifier's law; Anthropic, *Building Effective Agents* (evaluator needs\n  clear criteria).\"\n- **MEETINGS\u002FCOMMS lanes**: \"Could this meeting be an async written update? Recommended:\n  status-broadcast meetings convert to async 3P updates; decision meetings keep sync.\n  Canon: GitLab async-first handbook.\"\n\n## Assumptions\n\n1. The user has (or is preparing analysis for someone with) delivery authority.\n2. Jira\u002FConfluence access goes through the bundled MCP; capabilities NOT in\n   `project-management\u002Freferences\u002Fatlassian-mcp-tools.md` (project\u002Fsprint\u002Fboard\u002Fspace\n   creation, admin config) are done in the web UI — never invent tool names.\n3. Inputs may be partial — every tool ships `--sample` so the shape is visible first.\n\n## Non-goals\n\n- Not a replacement for the sub-skills — the orchestrator routes and loops; the\n  sub-skills do the work.\n- Not the generic loop engine — that is `engineering\u002Fagent-harness`; this orchestrator is\n  the PM-domain adapter (data bridge + governance gate + lane router).\n- Does not decide *what* to build — that's `product-team`.\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| Flow report | `flow_metrics.json` (bridge output) with SLE conformance + aging alerts |\n| Delivery loop | `.agent-harness\u002Fplan.json` + `state.json` + gate verdicts + close handoff |\n\n## Anti-patterns (do not)\n\n- ❌ Run all 8 sub-skills \"to be thorough\" — route to one, digest, chain on confirmation\n- ❌ Report sprint health or forecasts from hand-typed numbers when a Jira snapshot is one\n  MCP call away — bridge real data\n- ❌ Close a loop with unverified tasks, or report an exhausted budget as success\n- ❌ Let an agent be the assignee of record — humans own, agents contribute\n- ❌ Auto-transition Jira issues or touch permissions inside a loop without the named\n  approver\n\n## References\n\n- [references\u002Fflow_forecasting_canon.md](references\u002Fflow_forecasting_canon.md) — Kanban\n  Guide 2025, Vacanti Monte Carlo, DORA 2025, EBM, SPACE\n- [references\u002Fagentic_delivery_governance.md](references\u002Fagentic_delivery_governance.md) —\n  Linear\u002FRovo delegation models, Anthropic agent patterns, audit discipline\n- [references\u002Fpm_loop_playbook.md](references\u002Fpm_loop_playbook.md) — the five reusable PM\n  loops (sprint, health, retro-action, RAID-hygiene, comms) mapped to the loop contract\n- Canonical MCP tool list: `project-management\u002Freferences\u002Fatlassian-mcp-tools.md`\n- Loop engine: `engineering\u002Fagent-harness` · Loop vocabulary: `loop-library`\n",{"name":5,"tags":24,"author":34,"context":35,"license":15,"version":36,"description":7,"compatible_tools":37},[25,26,27,28,29,30,31,32,33],"project-management","orchestrator","jira","confluence","atlassian","scrum","agile","flow-metrics","agent-harness","Alireza Rezvani","fork","2.11.1",[38,39,40,41,42,43],"claude-code","codex-cli","cursor","antigravity","opencode","gemini-cli","main","46d43f3edecde97283ac410e9051444d90120cfb","repos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.hermes\u002Fskills\u002Fclaude-skills\u002Fproject-management\u002Fpm-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":51,"prompt":61,"zipAvailable":62},[52,55,58],{"tool":53,"command":54},"npx","npx skills add azhri1990\u002Fjarvis-mega-repo --skill pm-skills",{"tool":56,"command":57},"bunx","bunx skills add azhri1990\u002Fjarvis-mega-repo --skill pm-skills",{"tool":59,"command":60},"pnpm","pnpm dlx skills add azhri1990\u002Fjarvis-mega-repo --skill pm-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.hermes\u002Fskills\u002Fclaude-skills\u002Fproject-management\u002Fpm-skills\u002FSKILL.md",true,[64,70,74,80,84,88,94,98],{"path":65,"resourceType":66,"resourceTypeDesc":67,"sizeBytes":68,"sourceHtmlUrl":69},"assets\u002Fexpected_flow_metrics.json","ASSET","资产文件",1021,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.hermes\u002Fskills\u002Fclaude-skills\u002Fproject-management\u002Fpm-skills\u002Fassets\u002Fexpected_flow_metrics.json",{"path":71,"resourceType":66,"resourceTypeDesc":67,"sizeBytes":72,"sourceHtmlUrl":73},"assets\u002Fsample_jira_snapshot.json",4586,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.hermes\u002Fskills\u002Fclaude-skills\u002Fproject-management\u002Fpm-skills\u002Fassets\u002Fsample_jira_snapshot.json",{"path":75,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Fagentic_delivery_governance.md","REFERENCE","参考文档",4281,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.hermes\u002Fskills\u002Fclaude-skills\u002Fproject-management\u002Fpm-skills\u002Freferences\u002Fagentic_delivery_governance.md",{"path":81,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fflow_forecasting_canon.md",4029,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.hermes\u002Fskills\u002Fclaude-skills\u002Fproject-management\u002Fpm-skills\u002Freferences\u002Fflow_forecasting_canon.md",{"path":85,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fpm_loop_playbook.md",4777,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.hermes\u002Fskills\u002Fclaude-skills\u002Fproject-management\u002Fpm-skills\u002Freferences\u002Fpm_loop_playbook.md",{"path":89,"resourceType":90,"resourceTypeDesc":91,"sizeBytes":92,"sourceHtmlUrl":93},"scripts\u002Fdelivery_loop_gate.py","SCRIPT","脚本",7478,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.hermes\u002Fskills\u002Fclaude-skills\u002Fproject-management\u002Fpm-skills\u002Fscripts\u002Fdelivery_loop_gate.py",{"path":95,"resourceType":90,"resourceTypeDesc":91,"sizeBytes":96,"sourceHtmlUrl":97},"scripts\u002Fjira_snapshot_bridge.py",12420,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.hermes\u002Fskills\u002Fclaude-skills\u002Fproject-management\u002Fpm-skills\u002Fscripts\u002Fjira_snapshot_bridge.py",{"path":99,"resourceType":90,"resourceTypeDesc":91,"sizeBytes":100,"sourceHtmlUrl":101},"scripts\u002Fpm_goal_router.py",6813,"https:\u002F\u002Fgithub.com\u002Fazhri1990\u002Fjarvis-mega-repo\u002Fblob\u002Fmain\u002Frepos\u002FJ.A.R.V.I.S\u002Fcomponents\u002Fcore\u002Fclaude-skills\u002F.hermes\u002Fskills\u002Fclaude-skills\u002Fproject-management\u002Fpm-skills\u002Fscripts\u002Fpm_goal_router.py",[103,110,117,124,131,138],{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"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":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":116,"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":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":123,"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":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":130,"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":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":137,"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":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":144,"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",[],[]]