[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MBWS4AF4A1ETMYSQV9TPYH":3,"skill-same:01M6MBWS4AF4A1ETMYSQV9TPYH":72},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":13,"repositoryHtmlUrl":55,"repositoryHomepageUrl":6,"ownerHtmlUrl":56,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":57,"install":58,"resources":71},"01M6MBWS4AF4A1ETMYSQV9TPYH","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.","JiayiV\u002Fquestion_mixing","JiayiV","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54466889?v=4","01M6JFJYYMRZ3QRGP9339JNQ2K","01M6JFJZ35KB0QVT50NAHPJA7G",0,"MIT","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fpublic-02-claude-skills-main-engineering-agenthub-skills-agenthub-skill-43b2c8da\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[5,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"git worktree isolation","code","metric","worktree isolation","benefits","judge","isolation","competition","exploration","subagents","repo","branch","work","parallel","multi-agent","git worktree","llm judge","worktree","git","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:hub-init` | Create a new collaboration session — task, agent count, eval criteria |\n| `\u002Fhub:spawn` | Launch N parallel subagents in isolated worktrees |\n| `\u002Fhub:hub-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:hub-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:hub-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:hub-init` | Create a new collaboration session — task, agent count, eval criteria |\n| `\u002Fhub:spawn` | Launch N parallel subagents in isolated worktrees |\n| `\u002Fhub:hub-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:hub-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:hub-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":14,"metadata":47,"description":7},{"author":48,"updated":49,"version":50,"category":51},"Alireza Rezvani",1773705600000,"2.1.2","engineering","main","068c2486ccc0f8d200a3db4d21ec137064eda3b4","skills\u002Fpublic-02-claude-skills-main-engineering-agenthub-skills-agenthub-skill-43b2c8da","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing","https:\u002F\u002Fgithub.com\u002FJiayiV","2026-08-25 16:30:52",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add JiayiV\u002Fquestion_mixing --skill agenthub",{"tool":64,"command":65},"bunx","bunx skills add JiayiV\u002Fquestion_mixing --skill agenthub",{"tool":67,"command":68},"pnpm","pnpm dlx skills add JiayiV\u002Fquestion_mixing --skill agenthub","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fpublic-02-claude-skills-main-engineering-agenthub-skills-agenthub-skill-43b2c8da\u002FSKILL.md",true,[],[73,98,126,155,183,218],{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":79,"publishedAt":42},"01M6NN9PJS9NHWPDG4QP93BP8F","webmcpify","Make a web app agent-ready — propose a WebMCP tool manifest, integrate, verify in a real browser, heal; unrelated code stays untouched. Use for \"webmcpify\", \"add WebMCP\", or \"expose app actions to AI agents\".","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fwebmcpify\u002FSKILL.md",[],[75,80,81,82,83,24,84,85,86,87,88,89,90,91,92,93,94,95,96,97],"github","ai agents","web","notes","browser","https","actions","manifest","inventory","verify","tool","use","ready","unrelated","app","scope","for","webmcp",{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":102,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":42},"01M6NN6KZE5RBN6NGHN2WXV1RE","vardoger-analyze","Use when the user asks to personalize the GitHub Copilot CLI assistant, adapt Copilot to their style, use vardoger, or analyze their Copilot CLI conversation history. Reads the local session directory at `~\u002F.copilot\u002Fsession-state\u002F`, extracts recurring preferences and conventions, and writes a fenced personalization block into `~\u002F.copilot\u002Fcopilot-instructions.md`. Runs entirely on the user's machine via the local `vardoger` CLI (`pipx install vardoger`); no network calls and no uploads. Triggers: 'personalize my copilot', 'analyze my copilot history', 'tailor copilot to me', 'run vardoger', 'update my copilot instructions from history', 'make copilot learn my style'.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fvardoger-analyze\u002FSKILL.md",[],[106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125],"vardoger analyze","analyze","directory","cli","github copilot","copilot","run","update","learn","network","my style","install","conversation","me","local","github copilot cli","pipx","my","at","copilot instructions",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":136,"publishedAt":42},"01M6NN3CZGQP3XAJ04BM1NNYGT","transloadit-media-processing","Process media files (video, audio, images, documents) using Transloadit. Use when asked to encode video to HLS\u002FMP4, generate thumbnails, resize or watermark images, extract audio, concatenate clips, add subtitles, OCR documents, or run any media processing pipeline. Covers 86+ processing robots for file transformation at scale.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Ftransloadit-media-processing\u002FSKILL.md",[132,135],{"categoryNo":133,"categoryName":134,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[137,138,139,140,141,142,143,144,145,146,147,148,149,91,150,151,152,153,96,154],"transloadit media processing","Media","media processing","processing","MCP","mcp server","documents","subtitles","pipeline","ocr","Video","Audio","mp4","images","files","transformation","free","hls",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":164,"publishedAt":42},"01M6NN28E92F9GH4WFYQ2H1Y15","terraform-azurerm-set-diff-analyzer","Analyze Terraform plan JSON output for AzureRM Provider to distinguish between false-positive diffs (order-only changes in Set-type attributes) and actual resource changes. Use when reviewing terraform plan output for Azure resources like Application Gateway, Load Balancer, Firewall, Front Door, NSG, and other resources with Set-type attributes that cause spurious diffs due to internal ordering changes.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fterraform-azurerm-set-diff-analyzer\u002FSKILL.md",[161],{"categoryNo":162,"categoryName":163,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000011","运维与部署",[165,166,167,168,169,170,171,172,173,91,174,175,176,177,178,179,180,181,182,96],"terraform","SET","terraform azurerm set diff analyzer","diff","analyzer","diffs","azure","gateway","firewall","only","resource changes","provider","resources","load balancer","attributes","front","load","balancer",{"skillNo":184,"name":185,"nameZh":6,"description":186,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":187,"categories":188,"keywords":200,"publishedAt":42},"01M6NN1QMH2TDYWTH3XY3N5XPV","technical-job-search","Use this skill when a software engineer asks for help with job search tasks: parsing or analyzing a job description, tailoring a CV\u002Fresume, writing a cover letter, evaluating a job offer, or drafting a post-interview follow-up email. Do not activate for general career advice unrelated to an active job search action.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Ftechnical-job-search\u002FSKILL.md",[189,192,197],{"categoryNo":190,"categoryName":191,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":193,"categoryName":194,"taxonomyType":195,"taxonomyTypeDesc":196},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",{"categoryNo":198,"categoryName":199,"taxonomyType":195,"taxonomyTypeDesc":196},"01JBSEEDCAT000000000000038","人力资源",[201,202,203,204,205,206,207,208,107,209,91,210,211,212,213,214,215,216,96,217],"search","Job Search","technical job search","technical","writing","tasks","email","parsing","interview","up","engineer","general","activate","do","task","cv","letter",{"skillNo":219,"name":220,"nameZh":6,"description":221,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":222,"categories":223,"keywords":224,"publishedAt":42},"01M6NMZFA67XYS1GS09WT42235","steno-mode","Shorthand-first response compression that cuts ~40% of response tokens while preserving technical precision and exact literals. Use when the user says \"steno mode\", \"shorthand mode\", \"compressed responses\", \"token reduction\", \"brief structured output\", or invokes \u002Fsteno. Supports four compression levels: lite, brief, court, machine. Do not trigger for requests needing polished prose such as onboarding\u002Ftutorial content, stakeholder or customer-facing copy, or teaching-focused explanations.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fsteno-mode\u002FSKILL.md",[],[225,226,227,228,229,204,230,91,214,231,232,233,234,235,236,237,238,239,96,240],"steno mode","mode","customer","brief","first","tutorial","teaching","tokens","as","trigger","structured output","machine","output","court","requests","response compression"]