[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73023RN8W5GYEXRJ5ZGS7HN":3,"skill-same:01M73023RN8W5GYEXRJ5ZGS7HN":90},{"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":29,"commitSha":30,"skillDirectory":31,"forksCount":14,"repositoryHtmlUrl":32,"repositoryHomepageUrl":6,"ownerHtmlUrl":33,"ownerBlogUrl":6,"ownerDescription":34,"updatedAt":35,"install":36,"resources":49},"01M73023RN8W5GYEXRJ5ZGS7HN","agenthub",null,"Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.","ciciliaETH\u002Fclaude-skills","ciciliaETH","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F94572860?v=4","01M72Z3SEH31MEG7M94G85GE0V","cicilia_eth","01M72Z3SF42XENX7AMJVA7SCQX",0,"MIT","https:\u002F\u002Fgithub.com\u002FciciliaETH\u002Fclaude-skills\u002Fblob\u002Fmain\u002Fengineering\u002Fagenthub\u002Fskills\u002Fagenthub\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: \"agenthub\"\ndescription: \"Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.\"\nlicense: MIT\nmetadata:\n  version: 2.1.2\n  author: Alireza Rezvani\n  category: engineering\n  updated: 2026-03-17\n---\n\n# AgentHub — Multi-Agent Collaboration\n\nSpawn N parallel AI agents that compete on the same task. Each agent works in an isolated git worktree. The coordinator evaluates results and merges the winner.\n\n## Slash Commands\n\n| Command | Description |\n|---------|-------------|\n| `\u002Fhub:init` | Create a new collaboration session — task, agent count, eval criteria |\n| `\u002Fhub:spawn` | Launch N parallel subagents in isolated worktrees |\n| `\u002Fhub:status` | Show DAG state, agent progress, branch status |\n| `\u002Fhub:eval` | Rank agent results by metric or LLM judge |\n| `\u002Fhub:merge` | Merge winning branch, archive losers |\n| `\u002Fhub:board` | Read\u002Fwrite the agent message board |\n| `\u002Fhub:run` | One-shot lifecycle: init → baseline → spawn → eval → merge |\n\n## Agent Templates\n\nWhen spawning with `--template`, agents follow a predefined iteration pattern:\n\n| Template | Pattern | Use Case |\n|----------|---------|----------|\n| `optimizer` | Edit → eval → keep\u002Fdiscard → repeat x10 | Performance, latency, size |\n| `refactorer` | Restructure → test → iterate until green | Code quality, tech debt |\n| `test-writer` | Write tests → measure coverage → repeat | Test coverage gaps |\n| `bug-fixer` | Reproduce → diagnose → fix → verify | Bug fix approaches |\n\nTemplates are defined in `references\u002Fagent-templates.md`.\n\n## When This Skill Activates\n\nTrigger phrases:\n- \"try multiple approaches\"\n- \"have agents compete\"\n- \"parallel optimization\"\n- \"spawn N agents\"\n- \"compare different solutions\"\n- \"fan-out\" or \"tournament\"\n- \"generate content variations\"\n- \"compare different drafts\"\n- \"A\u002FB test copy\"\n- \"explore multiple strategies\"\n\n## Coordinator Protocol\n\nThe main Claude Code session is the coordinator. It follows this lifecycle:\n\n```\nINIT → DISPATCH → MONITOR → EVALUATE → MERGE\n```\n\n### 1. Init\n\nRun `\u002Fhub:init` to create a session. This generates:\n- `.agenthub\u002Fsessions\u002F{session-id}\u002Fconfig.yaml` — task config\n- `.agenthub\u002Fsessions\u002F{session-id}\u002Fstate.json` — state machine\n- `.agenthub\u002Fboard\u002F` — message board channels\n\n### 2. Dispatch\n\nRun `\u002Fhub:spawn` to launch agents. For each agent 1..N:\n- Post task assignment to `.agenthub\u002Fboard\u002Fdispatch\u002F`\n- Spawn via Agent tool with `isolation: \"worktree\"`\n- All agents launched in a single message (parallel)\n\n### 3. Monitor\n\nRun `\u002Fhub:status` to check progress:\n- `dag_analyzer.py --status --session {id}` shows branch state\n- Board `progress\u002F` channel has agent updates\n\n### 4. Evaluate\n\nRun `\u002Fhub:eval` to rank results:\n- **Metric mode**: run eval command in each worktree, parse numeric result\n- **Judge mode**: read diffs, coordinator ranks by quality\n- **Hybrid**: metric first, LLM-judge for ties\n\n### 5. Merge\n\nRun `\u002Fhub:merge` to finalize:\n- `git merge --no-ff` winner into base branch\n- Tag losers: `git tag hub\u002Farchive\u002F{session}\u002Fagent-{i}`\n- Clean up worktrees\n- Post merge summary to board\n\n## Agent Protocol\n\nEach subagent receives this prompt pattern:\n\n```\nYou are agent-{i} in hub session {session-id}.\nYour task: {task description}\n\nInstructions:\n1. Read your assignment at .agenthub\u002Fboard\u002Fdispatch\u002F{seq}-agent-{i}.md\n2. Work in your worktree — make changes, run tests, iterate\n3. Commit all changes with descriptive messages\n4. Write your result summary to .agenthub\u002Fboard\u002Fresults\u002Fagent-{i}-result.md\n5. Exit when done\n```\n\nAgents do NOT see each other's work. They do NOT communicate with each other. They only write to the board for the coordinator to read.\n\n## DAG Model\n\n### Branch Naming\n\n```\nhub\u002F{session-id}\u002Fagent-{N}\u002Fattempt-{M}\n```\n\n- Session ID: timestamp-based (`YYYYMMDD-HHMMSS`)\n- Agent N: sequential (1 to agent-count)\n- Attempt M: increments on retry (usually 1)\n\n### Frontier Detection\n\nFrontier = branch tips with no child branches. Equivalent to AgentHub's \"leaves\" query.\n\n```bash\npython scripts\u002Fdag_analyzer.py --frontier --session {id}\n```\n\n### Immutability\n\nThe DAG is append-only:\n- Never rebase or force-push agent branches\n- Never delete commits (only branch refs after archival)\n- Every approach preserved via git tags\n\n## Message Board\n\nLocation: `.agenthub\u002Fboard\u002F`\n\n### Channels\n\n| Channel | Writer | Reader | Purpose |\n|---------|--------|--------|---------|\n| `dispatch\u002F` | Coordinator | Agents | Task assignments |\n| `progress\u002F` | Agents | Coordinator | Status updates |\n| `results\u002F` | Agents + Coordinator | All | Final results + merge summary |\n\n### Post Format\n\n```markdown\n---\nauthor: agent-1\ntimestamp: 2026-03-17T14:30:22Z\nchannel: results\nparent: null\n---\n\n## Result Summary\n\n- **Approach**: Replaced O(n²) sort with hash map\n- **Files changed**: 3\n- **Metric**: 142ms (baseline: 180ms, delta: -38ms)\n- **Confidence**: High — all tests pass\n```\n\n### Board Rules\n\n- Append-only: never edit or delete posts\n- Unique filenames: `{seq:03d}-{author}-{timestamp}.md`\n- YAML frontmatter required on all posts\n\n## Evaluation Modes\n\n### Metric-Based\n\nBest for: benchmarks, test pass rates, file sizes, response times.\n\n```bash\npython scripts\u002Fresult_ranker.py --session {id} \\\n  --eval-cmd \"pytest bench.py --json\" \\\n  --metric p50_ms --direction lower\n```\n\nThe ranker runs the eval command in each agent's worktree directory and parses the metric from stdout.\n\n### LLM Judge\n\nBest for: code quality, readability, architecture decisions.\n\nThe coordinator reads each agent's diff (`git diff base...agent-branch`) and ranks by:\n1. Correctness (does it solve the task?)\n2. Simplicity (fewer lines changed preferred)\n3. Quality (clean execution, good structure)\n\n### Hybrid\n\nRun metric first. If top agents are within 10% of each other, use LLM judge to break ties.\n\n## Session Lifecycle\n\n```\ninit → running → evaluating → merged\n                            → archived (if no winner)\n```\n\nState transitions managed by `session_manager.py`:\n\n| From | To | Trigger |\n|------|----|---------|\n| `init` | `running` | `\u002Fhub:spawn` completes |\n| `running` | `evaluating` | All agents return |\n| `evaluating` | `merged` | `\u002Fhub:merge` completes |\n| `evaluating` | `archived` | No winner \u002F all failed |\n\n## Proactive Triggers\n\nThe coordinator should act when:\n\n| Signal | Action |\n|--------|--------|\n| All agents crashed | Post failure summary, suggest retry with different constraints |\n| No improvement over baseline | Archive session, suggest different approaches |\n| Orphan worktrees detected | Run `session_manager.py --cleanup {id}` |\n| Session stuck in `running` | Check board for progress, consider timeout |\n\n## Installation\n\n```bash\n# Copy to your Claude Code skills directory\ncp -r engineering\u002Fagenthub ~\u002F.claude\u002Fskills\u002Fagenthub\n\n# Or install via ClawHub\nclawhub install agenthub\n```\n\n## Scripts\n\n| Script | Purpose |\n|--------|---------|\n| `hub_init.py` | Initialize `.agenthub\u002F` structure and session |\n| `dag_analyzer.py` | Frontier detection, DAG graph, branch status |\n| `board_manager.py` | Message board CRUD (channels, posts, threads) |\n| `result_ranker.py` | Rank agents by metric or diff quality |\n| `session_manager.py` | Session state machine and cleanup |\n\n## Related Skills\n\n- **autoresearch-agent** — Single-agent optimization loop (use AgentHub when you want N agents competing)\n- **self-improving-agent** — Self-modifying agent (use AgentHub when you want external competition)\n- **git-worktree-manager** — Git worktree utilities (AgentHub uses worktrees internally)\n","# AgentHub — Multi-Agent Collaboration\n\nSpawn N parallel AI agents that compete on the same task. Each agent works in an isolated git worktree. The coordinator evaluates results and merges the winner.\n\n## Slash Commands\n\n| Command | Description |\n|---------|-------------|\n| `\u002Fhub:init` | Create a new collaboration session — task, agent count, eval criteria |\n| `\u002Fhub:spawn` | Launch N parallel subagents in isolated worktrees |\n| `\u002Fhub:status` | Show DAG state, agent progress, branch status |\n| `\u002Fhub:eval` | Rank agent results by metric or LLM judge |\n| `\u002Fhub:merge` | Merge winning branch, archive losers |\n| `\u002Fhub:board` | Read\u002Fwrite the agent message board |\n| `\u002Fhub:run` | One-shot lifecycle: init → baseline → spawn → eval → merge |\n\n## Agent Templates\n\nWhen spawning with `--template`, agents follow a predefined iteration pattern:\n\n| Template | Pattern | Use Case |\n|----------|---------|----------|\n| `optimizer` | Edit → eval → keep\u002Fdiscard → repeat x10 | Performance, latency, size |\n| `refactorer` | Restructure → test → iterate until green | Code quality, tech debt |\n| `test-writer` | Write tests → measure coverage → repeat | Test coverage gaps |\n| `bug-fixer` | Reproduce → diagnose → fix → verify | Bug fix approaches |\n\nTemplates are defined in `references\u002Fagent-templates.md`.\n\n## When This Skill Activates\n\nTrigger phrases:\n- \"try multiple approaches\"\n- \"have agents compete\"\n- \"parallel optimization\"\n- \"spawn N agents\"\n- \"compare different solutions\"\n- \"fan-out\" or \"tournament\"\n- \"generate content variations\"\n- \"compare different drafts\"\n- \"A\u002FB test copy\"\n- \"explore multiple strategies\"\n\n## Coordinator Protocol\n\nThe main Claude Code session is the coordinator. It follows this lifecycle:\n\n```\nINIT → DISPATCH → MONITOR → EVALUATE → MERGE\n```\n\n### 1. Init\n\nRun `\u002Fhub:init` to create a session. This generates:\n- `.agenthub\u002Fsessions\u002F{session-id}\u002Fconfig.yaml` — task config\n- `.agenthub\u002Fsessions\u002F{session-id}\u002Fstate.json` — state machine\n- `.agenthub\u002Fboard\u002F` — message board channels\n\n### 2. Dispatch\n\nRun `\u002Fhub:spawn` to launch agents. For each agent 1..N:\n- Post task assignment to `.agenthub\u002Fboard\u002Fdispatch\u002F`\n- Spawn via Agent tool with `isolation: \"worktree\"`\n- All agents launched in a single message (parallel)\n\n### 3. Monitor\n\nRun `\u002Fhub:status` to check progress:\n- `dag_analyzer.py --status --session {id}` shows branch state\n- Board `progress\u002F` channel has agent updates\n\n### 4. Evaluate\n\nRun `\u002Fhub:eval` to rank results:\n- **Metric mode**: run eval command in each worktree, parse numeric result\n- **Judge mode**: read diffs, coordinator ranks by quality\n- **Hybrid**: metric first, LLM-judge for ties\n\n### 5. Merge\n\nRun `\u002Fhub:merge` to finalize:\n- `git merge --no-ff` winner into base branch\n- Tag losers: `git tag hub\u002Farchive\u002F{session}\u002Fagent-{i}`\n- Clean up worktrees\n- Post merge summary to board\n\n## Agent Protocol\n\nEach subagent receives this prompt pattern:\n\n```\nYou are agent-{i} in hub session {session-id}.\nYour task: {task description}\n\nInstructions:\n1. Read your assignment at .agenthub\u002Fboard\u002Fdispatch\u002F{seq}-agent-{i}.md\n2. Work in your worktree — make changes, run tests, iterate\n3. Commit all changes with descriptive messages\n4. Write your result summary to .agenthub\u002Fboard\u002Fresults\u002Fagent-{i}-result.md\n5. Exit when done\n```\n\nAgents do NOT see each other's work. They do NOT communicate with each other. They only write to the board for the coordinator to read.\n\n## DAG Model\n\n### Branch Naming\n\n```\nhub\u002F{session-id}\u002Fagent-{N}\u002Fattempt-{M}\n```\n\n- Session ID: timestamp-based (`YYYYMMDD-HHMMSS`)\n- Agent N: sequential (1 to agent-count)\n- Attempt M: increments on retry (usually 1)\n\n### Frontier Detection\n\nFrontier = branch tips with no child branches. Equivalent to AgentHub's \"leaves\" query.\n\n```bash\npython scripts\u002Fdag_analyzer.py --frontier --session {id}\n```\n\n### Immutability\n\nThe DAG is append-only:\n- Never rebase or force-push agent branches\n- Never delete commits (only branch refs after archival)\n- Every approach preserved via git tags\n\n## Message Board\n\nLocation: `.agenthub\u002Fboard\u002F`\n\n### Channels\n\n| Channel | Writer | Reader | Purpose |\n|---------|--------|--------|---------|\n| `dispatch\u002F` | Coordinator | Agents | Task assignments |\n| `progress\u002F` | Agents | Coordinator | Status updates |\n| `results\u002F` | Agents + Coordinator | All | Final results + merge summary |\n\n### Post Format\n\n```markdown\n---\nauthor: agent-1\ntimestamp: 2026-03-17T14:30:22Z\nchannel: results\nparent: null\n---\n\n## Result Summary\n\n- **Approach**: Replaced O(n²) sort with hash map\n- **Files changed**: 3\n- **Metric**: 142ms (baseline: 180ms, delta: -38ms)\n- **Confidence**: High — all tests pass\n```\n\n### Board Rules\n\n- Append-only: never edit or delete posts\n- Unique filenames: `{seq:03d}-{author}-{timestamp}.md`\n- YAML frontmatter required on all posts\n\n## Evaluation Modes\n\n### Metric-Based\n\nBest for: benchmarks, test pass rates, file sizes, response times.\n\n```bash\npython scripts\u002Fresult_ranker.py --session {id} \\\n  --eval-cmd \"pytest bench.py --json\" \\\n  --metric p50_ms --direction lower\n```\n\nThe ranker runs the eval command in each agent's worktree directory and parses the metric from stdout.\n\n### LLM Judge\n\nBest for: code quality, readability, architecture decisions.\n\nThe coordinator reads each agent's diff (`git diff base...agent-branch`) and ranks by:\n1. Correctness (does it solve the task?)\n2. Simplicity (fewer lines changed preferred)\n3. Quality (clean execution, good structure)\n\n### Hybrid\n\nRun metric first. If top agents are within 10% of each other, use LLM judge to break ties.\n\n## Session Lifecycle\n\n```\ninit → running → evaluating → merged\n                            → archived (if no winner)\n```\n\nState transitions managed by `session_manager.py`:\n\n| From | To | Trigger |\n|------|----|---------|\n| `init` | `running` | `\u002Fhub:spawn` completes |\n| `running` | `evaluating` | All agents return |\n| `evaluating` | `merged` | `\u002Fhub:merge` completes |\n| `evaluating` | `archived` | No winner \u002F all failed |\n\n## Proactive Triggers\n\nThe coordinator should act when:\n\n| Signal | Action |\n|--------|--------|\n| All agents crashed | Post failure summary, suggest retry with different constraints |\n| No improvement over baseline | Archive session, suggest different approaches |\n| Orphan worktrees detected | Run `session_manager.py --cleanup {id}` |\n| Session stuck in `running` | Check board for progress, consider timeout |\n\n## Installation\n\n```bash\n# Copy to your Claude Code skills directory\ncp -r engineering\u002Fagenthub ~\u002F.claude\u002Fskills\u002Fagenthub\n\n# Or install via ClawHub\nclawhub install agenthub\n```\n\n## Scripts\n\n| Script | Purpose |\n|--------|---------|\n| `hub_init.py` | Initialize `.agenthub\u002F` structure and session |\n| `dag_analyzer.py` | Frontier detection, DAG graph, branch status |\n| `board_manager.py` | Message board CRUD (channels, posts, threads) |\n| `result_ranker.py` | Rank agents by metric or diff quality |\n| `session_manager.py` | Session state machine and cleanup |\n\n## Related Skills\n\n- **autoresearch-agent** — Single-agent optimization loop (use AgentHub when you want N agents competing)\n- **self-improving-agent** — Self-modifying agent (use AgentHub when you want external competition)\n- **git-worktree-manager** — Git worktree utilities (AgentHub uses worktrees 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ISO 13485 QMS, MDR 2017\u002F745, FDA 510(k)\u002FPMA, ISO 27001 ISMS, GDPR\u002FDSGVO, risk management (ISO 14971), CAPA, document control, auditing. Python tools (stdlib-only).","https:\u002F\u002Fgithub.com\u002FciciliaETH\u002Fclaude-skills\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fskills\u002Fra-qm-skills\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},"01M730QTT31DKCCZC573H22GX6","iso42001-specialist","ISO\u002FIEC 42001:2023 AI Management System (AIMS) specialist for compliance teams running internal audits. Three decisions: (1) Where are the gaps against Clauses 4-10 and what do we close first? (2) What goes in the AI risk register and which Annex A controls treat each risk? (3) What's the 12-month internal audit plan that satisfies Clause 9.2? Use when preparing for certification, scoping internal audit cycles, or onboarding AI systems into an existing ISMS (27001) \u002F QMS (13485) program. NOT an executive AI strategy skill (see chief-ai-officer-advisor). NOT EU AI Act compliance (see compliance-team-eu-ai-act).","https:\u002F\u002Fgithub.com\u002FciciliaETH\u002Fclaude-skills\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fskills\u002Fiso42001-specialist\u002FSKILL.md",[],[],{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":111,"publishedAt":19},"01M730Q40HCJJ97V6HGDF4FPS0","eu-ai-act-specialist","EU AI Act (Regulation (EU) 2024\u002F1689) operational compliance for compliance teams. Three Article-level decisions: (1) What's the risk tier of this AI system — prohibited (Art. 5), high-risk (Art. 6 + Annex III), limited-risk (Art. 50), or minimal-risk? (2) For high-risk systems, what's the Article 43 conformity assessment route (Module A internal control vs Module H full QMS + notified body) and what goes in the Annex IV technical documentation? (3) Per organizational role (provider \u002F deployer \u002F importer \u002F distributor \u002F authorized representative), what are the active obligations and deadlines? Use during AI system intake review, when planning conformity assessment, or when scoping deployer obligations. Cites Articles + Annexes for every output. NOT executive AI strategy (see chief-ai-officer-advisor). NOT a legal substitute.","https:\u002F\u002Fgithub.com\u002FciciliaETH\u002Fclaude-skills\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fskills\u002Feu-ai-act-specialist\u002FSKILL.md",[],[],{"skillNo":113,"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":114,"categories":115,"keywords":116,"publishedAt":19},"01M730PQWQ9N9FWV52Y14A1NJT","https:\u002F\u002Fgithub.com\u002FciciliaETH\u002Fclaude-skills\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fcompliance-team-iso42001\u002Fskills\u002Fiso42001-specialist\u002FSKILL.md",[],[],{"skillNo":118,"name":107,"nameZh":6,"description":108,"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},"01M730PFX71VE2Q2HFHNYRDKQX","https:\u002F\u002Fgithub.com\u002FciciliaETH\u002Fclaude-skills\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fcompliance-team-eu-ai-act\u002Fskills\u002Feu-ai-act-specialist\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},"01M730NW4DAJ4JF9KRXMF35ADB","scrum-master","Advanced Scrum Master skill for data-driven agile team analysis and coaching. Use when the user asks about sprint planning, velocity tracking, retrospectives, standup facilitation, backlog grooming, story points, burndown charts, blocker resolution, or agile team health. Runs Python scripts to analyse sprint JSON exports from Jira or similar tools: velocity_analyzer.py for Monte Carlo sprint forecasting, sprint_health_scorer.py for multi-dimension health scoring, and retrospective_analyzer.py for action-item and theme tracking. Produces confidence-interval forecasts, health grade reports, and improvement-velocity trends for high-performing Scrum teams.","https:\u002F\u002Fgithub.com\u002FciciliaETH\u002Fclaude-skills\u002Fblob\u002Fmain\u002Fproject-management\u002Fskills\u002Fscrum-master\u002FSKILL.md",[],[]]