[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71H8APX57Z1VC10T0K11XR9":3,"skill-same:01M71H8APX57Z1VC10T0K11XR9":140},{"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":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":56,"repositoryHtmlUrl":57,"repositoryHomepageUrl":6,"ownerHtmlUrl":58,"ownerBlogUrl":59,"ownerDescription":60,"updatedAt":61,"install":62,"resources":75},"01M71H8APX57Z1VC10T0K11XR9","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.","iDorgham\u002FWorkspace-Factory","iDorgham","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F136521493?v=4","01M6CNGBDAHE4ARW3YJEF4X2FD","Dorgham","01M71E8H3CXZ7DJX59MTM9JEB2",1,"MIT","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"2026","worktree isolation","subagents","isolation","benefits","multi-agent","metric","judge","git","competition","git worktree isolation","worktree","git worktree","work","code","when","exploration","branch","llm judge","2026-11-10 10:12:30",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 internally)\n",{"name":5,"license":15,"metadata":48,"description":7},{"author":49,"updated":50,"version":51,"category":52},"Alireza Rezvani",1773705600000,"2.1.2","engineering","master","c2e410e11df4d80d885e0ecf0583ef3c926df736","factory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub",0,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory","https:\u002F\u002Fgithub.com\u002FiDorgham","Dorgham.me","hey I'm Dorgham \r\nI code, I create, and AI codes back. \r\nMaybe we can give AI the chance!! now we begin. 🚀🤖\r\n... . . . git commit -m \"Hello, Future\" ","2026-05-04 18:39:09",{"source":8,"skillName":5,"commands":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add iDorgham\u002FWorkspace-Factory --skill agenthub",{"tool":68,"command":69},"bunx","bunx skills add iDorgham\u002FWorkspace-Factory --skill agenthub",{"tool":71,"command":72},"pnpm","pnpm dlx skills add iDorgham\u002FWorkspace-Factory --skill agenthub","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002FSKILL.md",true,[76,82,86,90,94,98,102,106,110,116,120,124,128,132,136],{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"_AIWF_IMPORT.json","REFERENCE","参考文档",314,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002F_AIWF_IMPORT.json",{"path":83,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":84,"sourceHtmlUrl":85},".claude-plugin\u002Fplugin.json",697,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002F.claude-plugin\u002Fplugin.json",{"path":87,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":88,"sourceHtmlUrl":89},"agents\u002Fhub-coordinator.md",3789,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002Fagents\u002Fhub-coordinator.md",{"path":91,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":92,"sourceHtmlUrl":93},"CLAUDE.md",2540,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002FCLAUDE.md",{"path":95,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":96,"sourceHtmlUrl":97},"README.md",11506,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002FREADME.md",{"path":99,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":100,"sourceHtmlUrl":101},"references\u002Fagent-templates.md",7795,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002Freferences\u002Fagent-templates.md",{"path":103,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":104,"sourceHtmlUrl":105},"references\u002Fcoordination-strategies.md",5946,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002Freferences\u002Fcoordination-strategies.md",{"path":107,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":108,"sourceHtmlUrl":109},"references\u002Fdag-patterns.md",4091,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002Freferences\u002Fdag-patterns.md",{"path":111,"resourceType":112,"resourceTypeDesc":113,"sizeBytes":114,"sourceHtmlUrl":115},"scripts\u002Fboard_manager.py","SCRIPT","脚本",8562,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002Fscripts\u002Fboard_manager.py",{"path":117,"resourceType":112,"resourceTypeDesc":113,"sizeBytes":118,"sourceHtmlUrl":119},"scripts\u002Fdag_analyzer.py",8942,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002Fscripts\u002Fdag_analyzer.py",{"path":121,"resourceType":112,"resourceTypeDesc":113,"sizeBytes":122,"sourceHtmlUrl":123},"scripts\u002Fdry_run.py",11415,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002Fscripts\u002Fdry_run.py",{"path":125,"resourceType":112,"resourceTypeDesc":113,"sizeBytes":126,"sourceHtmlUrl":127},"scripts\u002Fhub_init.py",8528,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002Fscripts\u002Fhub_init.py",{"path":129,"resourceType":112,"resourceTypeDesc":113,"sizeBytes":130,"sourceHtmlUrl":131},"scripts\u002Fresult_ranker.py",10557,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002Fscripts\u002Fresult_ranker.py",{"path":133,"resourceType":112,"resourceTypeDesc":113,"sizeBytes":134,"sourceHtmlUrl":135},"scripts\u002Fsession_manager.py",9991,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002Fscripts\u002Fsession_manager.py",{"path":137,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":138,"sourceHtmlUrl":139},"settings.json",854,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fgithub_imports\u002Fagenthub\u002Fsettings.json",[141,171,194,215,235,258],{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":150,"publishedAt":170},"01M71P8WVBMB2X3D24R0YT0B0E","vercel-react-view-transitions","Guide for implementing smooth, native-feeling animations using React's View Transition API (`\u003CViewTransition>` component, `addTransitionType`, and CSS view transition pseudo-elements). Use this skill whenever the user wants to add page transitions, animate route changes, create shared element animations, animate enter\u002Fexit of components, animate list reorder, implement directional (forward\u002Fback) navigation animations, or integrate view transitions in Next.js. Also use when the user mentions view transitions, `startViewTransition`, `ViewTransition`, transition types, or asks about animating between UI states in React without third-party animation libraries.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_react_view_transitions\u002Fskill.md",[147],{"categoryNo":148,"categoryName":149,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[151,152,153,154,155,156,157,158,159,160,161,162,163,164,39,165,166,167,168,169],"react","vercel react view transitions","view","react view transitions","vercel","transitions","shared","back","Animation","css","types","states","element","implementing","components","api","route","create","libraries","2026-11-10 13:02:02",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":178,"publishedAt":170},"01M71P8TDGHZ289TSAC5VTG2EW","vercel-react-native-skills","React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_react_native_skills\u002Fskill.md",[177],{"categoryNo":148,"categoryName":149,"taxonomyType":21,"taxonomyTypeDesc":22},[151,179,180,155,181,182,183,184,164,185,39,186,165,187,188,189,190,191,192,193],"react native","native","vercel react native skills","react native skills","modules","performance","apps","tasks","mobile","expo","list","mobile performance","platform","animations","for",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":198,"categories":199,"keywords":201,"publishedAt":170},"01M71P8QK4JF5BYRGYDDGPZQG9","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React\u002FNext.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_react_best_practices\u002Fskill.md",[200],{"categoryNo":148,"categoryName":149,"taxonomyType":21,"taxonomyTypeDesc":22},[151,202,203,155,204,184,205,39,38,186,165,206,207,208,209,210,211,212,213,214],"react best practices","vercel react best practices","best practices","writing","data fetching","react components","next","refactoring","data","patterns","Optimization","guidelines","triggers",{"skillNo":216,"name":217,"nameZh":6,"description":218,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":219,"categories":220,"keywords":222,"publishedAt":170},"01M71P8HVEA70K5N6ZVJCG2ED3","vercel-composition-patterns","React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_composition_patterns\u002Fskill.md",[221],{"categoryNo":148,"categoryName":149,"taxonomyType":21,"taxonomyTypeDesc":22},[223,155,224,211,225,226,227,228,214,229,230,231,39,232,186,165,233,234,166,151],"Composition","composition patterns","vercel composition patterns","react composition patterns","react 19","render","boolean","component architecture","use","reusable","architecture","react composition",{"skillNo":236,"name":237,"nameZh":6,"description":238,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":239,"categories":240,"keywords":244,"publishedAt":170},"01M71P60XXKKP222ZFJRG7R373","supabase-postgres-best-practices","Postgres performance optimization and best practices from Supabase. Use this skill when writing, reviewing, or optimizing Postgres queries, schema designs, or database configurations.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_supabase_supabase_postgres_best_practices\u002Fskill.md",[241],{"categoryNo":242,"categoryName":243,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[245,246,247,248,204,249,250,251,193,212,184,252,253,205,254,231,255,256,257,39],"postgres best practices","supabase","postgres","supabase postgres best practices","categories","postgres performance","advanced","correct","schema","impact","Rules","critical","database",{"skillNo":259,"name":260,"nameZh":6,"description":261,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":262,"categories":263,"keywords":265,"publishedAt":281},"01M71P2ZCEJD0PKC458YTP1FJW","portable-text-serialization","Render and serialize Portable Text to React, Svelte, Vue, Astro, HTML, Markdown, and plain text. Use when implementing Portable Text rendering in any frontend framework, building custom serializers for non-standard block types, converting Portable Text to HTML strings server-side, converting Portable Text to Markdown, extracting plain text from Portable Text, or troubleshooting rendering issues with marks, blocks, lists, or custom types.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_sanity_io_portable_text_serialization\u002Fskill.md",[264],{"categoryNo":148,"categoryName":149,"taxonomyType":21,"taxonomyTypeDesc":22},[266,267,268,269,270,161,271,228,272,164,273,274,39,151,275,276,277,278,279,280],"text","portable text serialization","portable","serialization","extracting","strings","issues","frontend","svelte","sanity","astro","server","markdown","rendering","vue","2026-11-10 10:56:23"]