[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JV40FK6F2P66BY2WNK7JKD":3,"skill-same:01M6JV40FK6F2P66BY2WNK7JKD":63},{"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":27,"linkOnly":28,"markdownContent":29,"markdownBody":30,"frontMatter":31,"refName":33,"commitSha":34,"skillDirectory":35,"forksCount":36,"repositoryHtmlUrl":37,"repositoryHomepageUrl":38,"ownerHtmlUrl":39,"ownerBlogUrl":40,"ownerDescription":41,"updatedAt":42,"install":43,"resources":56},"01M6JV40FK6F2P66BY2WNK7JKD","iterative-retrieval",null,"Pattern for progressively refining context retrieval to solve the subagent context problem","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1566,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FColin4k1024\u002Ftsp\u002Fskills\u002Fiterative-retrieval\u002FSKILL.md",[],[19,20,21,22,23,24,25,26],"iterative retrieval","retrieval","subagent","context","solve","pattern","problem","for","2026-11-10 10:12:30",false,"---\nname: iterative-retrieval\ndescription: Pattern for progressively refining context retrieval to solve the subagent context problem\norigin: ECC\n---\n\n# Iterative Retrieval Pattern\n\nSolves the \"context problem\" in multi-agent workflows where subagents don't know what context they need until they start working.\n\n## When to Activate\n\n- Spawning subagents that need codebase context they cannot predict upfront\n- Building multi-agent workflows where context is progressively refined\n- Encountering \"context too large\" or \"missing context\" failures in agent tasks\n- Designing RAG-like retrieval pipelines for code exploration\n- Optimizing token usage in agent orchestration\n\n## The Problem\n\nSubagents are spawned with limited context. They don't know:\n- Which files contain relevant code\n- What patterns exist in the codebase\n- What terminology the project uses\n\nStandard approaches fail:\n- **Send everything**: Exceeds context limits\n- **Send nothing**: Agent lacks critical information\n- **Guess what's needed**: Often wrong\n\n## The Solution: Iterative Retrieval\n\nA 4-phase loop that progressively refines context:\n\n```\n┌─────────────────────────────────────────────┐\n│                                             │\n│   ┌──────────┐      ┌──────────┐            │\n│   │ DISPATCH │─────│ EVALUATE │            │\n│   └──────────┘      └──────────┘            │\n│        ▲                  │                 │\n│        │                  ▼                 │\n│   ┌──────────┐      ┌──────────┐            │\n│   │   LOOP   │─────│  REFINE  │            │\n│   └──────────┘      └──────────┘            │\n│                                             │\n│        Max 3 cycles, then proceed           │\n└─────────────────────────────────────────────┘\n```\n\n### Phase 1: DISPATCH\n\nInitial broad query to gather candidate files:\n\n```javascript\n\u002F\u002F Start with high-level intent\nconst initialQuery = {\n  patterns: ['src\u002F**\u002F*.ts', 'lib\u002F**\u002F*.ts'],\n  keywords: ['authentication', 'user', 'session'],\n  excludes: ['*.test.ts', '*.spec.ts']\n};\n\n\u002F\u002F Dispatch to retrieval agent\nconst candidates = await retrieveFiles(initialQuery);\n```\n\n### Phase 2: EVALUATE\n\nAssess retrieved content for relevance:\n\n```javascript\nfunction evaluateRelevance(files, task) {\n  return files.map(file => ({\n    path: file.path,\n    relevance: scoreRelevance(file.content, task),\n    reason: explainRelevance(file.content, task),\n    missingContext: identifyGaps(file.content, task)\n  }));\n}\n```\n\nScoring criteria:\n- **High (0.8-1.0)**: Directly implements target functionality\n- **Medium (0.5-0.7)**: Contains related patterns or types\n- **Low (0.2-0.4)**: Tangentially related\n- **None (0-0.2)**: Not relevant, exclude\n\n### Phase 3: REFINE\n\nUpdate search criteria based on evaluation:\n\n```javascript\nfunction refineQuery(evaluation, previousQuery) {\n  return {\n    \u002F\u002F Add new patterns discovered in high-relevance files\n    patterns: [...previousQuery.patterns, ...extractPatterns(evaluation)],\n\n    \u002F\u002F Add terminology found in codebase\n    keywords: [...previousQuery.keywords, ...extractKeywords(evaluation)],\n\n    \u002F\u002F Exclude confirmed irrelevant paths\n    excludes: [...previousQuery.excludes, ...evaluation\n      .filter(e => e.relevance \u003C 0.2)\n      .map(e => e.path)\n    ],\n\n    \u002F\u002F Target specific gaps\n    focusAreas: evaluation\n      .flatMap(e => e.missingContext)\n      .filter(unique)\n  };\n}\n```\n\n### Phase 4: LOOP\n\nRepeat with refined criteria (max 3 cycles):\n\n```javascript\nasync function iterativeRetrieve(task, maxCycles = 3) {\n  let query = createInitialQuery(task);\n  let bestContext = [];\n\n  for (let cycle = 0; cycle \u003C maxCycles; cycle++) {\n    const candidates = await retrieveFiles(query);\n    const evaluation = evaluateRelevance(candidates, task);\n\n    \u002F\u002F Check if we have sufficient context\n    const highRelevance = evaluation.filter(e => e.relevance >= 0.7);\n    if (highRelevance.length >= 3 && !hasCriticalGaps(evaluation)) {\n      return highRelevance;\n    }\n\n    \u002F\u002F Refine and continue\n    query = refineQuery(evaluation, query);\n    bestContext = mergeContext(bestContext, highRelevance);\n  }\n\n  return bestContext;\n}\n```\n\n## Practical Examples\n\n### Example 1: Bug Fix Context\n\n```\nTask: \"Fix the authentication token expiry bug\"\n\nCycle 1:\n  DISPATCH: Search for \"token\", \"auth\", \"expiry\" in src\u002F**\n  EVALUATE: Found auth.ts (0.9), tokens.ts (0.8), user.ts (0.3)\n  REFINE: Add \"refresh\", \"jwt\" keywords; exclude user.ts\n\nCycle 2:\n  DISPATCH: Search refined terms\n  EVALUATE: Found session-manager.ts (0.95), jwt-utils.ts (0.85)\n  REFINE: Sufficient context (2 high-relevance files)\n\nResult: auth.ts, tokens.ts, session-manager.ts, jwt-utils.ts\n```\n\n### Example 2: Feature Implementation\n\n```\nTask: \"Add rate limiting to API endpoints\"\n\nCycle 1:\n  DISPATCH: Search \"rate\", \"limit\", \"api\" in routes\u002F**\n  EVALUATE: No matches - codebase uses \"throttle\" terminology\n  REFINE: Add \"throttle\", \"middleware\" keywords\n\nCycle 2:\n  DISPATCH: Search refined terms\n  EVALUATE: Found throttle.ts (0.9), middleware\u002Findex.ts (0.7)\n  REFINE: Need router patterns\n\nCycle 3:\n  DISPATCH: Search \"router\", \"express\" patterns\n  EVALUATE: Found router-setup.ts (0.8)\n  REFINE: Sufficient context\n\nResult: throttle.ts, middleware\u002Findex.ts, router-setup.ts\n```\n\n## Integration with Agents\n\nUse in agent prompts:\n\n```markdown\nWhen retrieving context for this task:\n1. Start with broad keyword search\n2. Evaluate each file's relevance (0-1 scale)\n3. Identify what context is still missing\n4. Refine search criteria and repeat (max 3 cycles)\n5. Return files with relevance >= 0.7\n```\n\n## Best Practices\n\n1. **Start broad, narrow progressively** - Don't over-specify initial queries\n2. **Learn codebase terminology** - First cycle often reveals naming conventions\n3. **Track what's missing** - Explicit gap identification drives refinement\n4. **Stop at \"good enough\"** - 3 high-relevance files beats 10 mediocre ones\n5. **Exclude confidently** - Low-relevance files won't become relevant\n\n## Related\n\n- [The Longform Guide](https:\u002F\u002Fx.com\u002Faffaanmustafa\u002Fstatus\u002F2014040193557471352) - Subagent orchestration section\n- `continuous-learning` skill - For patterns that improve over time\n- Agent definitions bundled with ECC (manual install path: `agents\u002F`)\n","# Iterative Retrieval Pattern\n\nSolves the \"context problem\" in multi-agent workflows where subagents don't know what context they need until they start working.\n\n## When to Activate\n\n- Spawning subagents that need codebase context they cannot predict upfront\n- Building multi-agent workflows where context is progressively refined\n- Encountering \"context too large\" or \"missing context\" failures in agent tasks\n- Designing RAG-like retrieval pipelines for code exploration\n- Optimizing token usage in agent orchestration\n\n## The Problem\n\nSubagents are spawned with limited context. They don't know:\n- Which files contain relevant code\n- What patterns exist in the codebase\n- What terminology the project uses\n\nStandard approaches fail:\n- **Send everything**: Exceeds context limits\n- **Send nothing**: Agent lacks critical information\n- **Guess what's needed**: Often wrong\n\n## The Solution: Iterative Retrieval\n\nA 4-phase loop that progressively refines context:\n\n```\n┌─────────────────────────────────────────────┐\n│                                             │\n│   ┌──────────┐      ┌──────────┐            │\n│   │ DISPATCH │─────│ EVALUATE │            │\n│   └──────────┘      └──────────┘            │\n│        ▲                  │                 │\n│        │                  ▼                 │\n│   ┌──────────┐      ┌──────────┐            │\n│   │   LOOP   │─────│  REFINE  │            │\n│   └──────────┘      └──────────┘            │\n│                                             │\n│        Max 3 cycles, then proceed           │\n└─────────────────────────────────────────────┘\n```\n\n### Phase 1: DISPATCH\n\nInitial broad query to gather candidate files:\n\n```javascript\n\u002F\u002F Start with high-level intent\nconst initialQuery = {\n  patterns: ['src\u002F**\u002F*.ts', 'lib\u002F**\u002F*.ts'],\n  keywords: ['authentication', 'user', 'session'],\n  excludes: ['*.test.ts', '*.spec.ts']\n};\n\n\u002F\u002F Dispatch to retrieval agent\nconst candidates = await retrieveFiles(initialQuery);\n```\n\n### Phase 2: EVALUATE\n\nAssess retrieved content for relevance:\n\n```javascript\nfunction evaluateRelevance(files, task) {\n  return files.map(file => ({\n    path: file.path,\n    relevance: scoreRelevance(file.content, task),\n    reason: explainRelevance(file.content, task),\n    missingContext: identifyGaps(file.content, task)\n  }));\n}\n```\n\nScoring criteria:\n- **High (0.8-1.0)**: Directly implements target functionality\n- **Medium (0.5-0.7)**: Contains related patterns or types\n- **Low (0.2-0.4)**: Tangentially related\n- **None (0-0.2)**: Not relevant, exclude\n\n### Phase 3: REFINE\n\nUpdate search criteria based on evaluation:\n\n```javascript\nfunction refineQuery(evaluation, previousQuery) {\n  return {\n    \u002F\u002F Add new patterns discovered in high-relevance files\n    patterns: [...previousQuery.patterns, ...extractPatterns(evaluation)],\n\n    \u002F\u002F Add terminology found in codebase\n    keywords: [...previousQuery.keywords, ...extractKeywords(evaluation)],\n\n    \u002F\u002F Exclude confirmed irrelevant paths\n    excludes: [...previousQuery.excludes, ...evaluation\n      .filter(e => e.relevance \u003C 0.2)\n      .map(e => e.path)\n    ],\n\n    \u002F\u002F Target specific gaps\n    focusAreas: evaluation\n      .flatMap(e => e.missingContext)\n      .filter(unique)\n  };\n}\n```\n\n### Phase 4: LOOP\n\nRepeat with refined criteria (max 3 cycles):\n\n```javascript\nasync function iterativeRetrieve(task, maxCycles = 3) {\n  let query = createInitialQuery(task);\n  let bestContext = [];\n\n  for (let cycle = 0; cycle \u003C maxCycles; cycle++) {\n    const candidates = await retrieveFiles(query);\n    const evaluation = evaluateRelevance(candidates, task);\n\n    \u002F\u002F Check if we have sufficient context\n    const highRelevance = evaluation.filter(e => e.relevance >= 0.7);\n    if (highRelevance.length >= 3 && !hasCriticalGaps(evaluation)) {\n      return highRelevance;\n    }\n\n    \u002F\u002F Refine and continue\n    query = refineQuery(evaluation, query);\n    bestContext = mergeContext(bestContext, highRelevance);\n  }\n\n  return bestContext;\n}\n```\n\n## Practical Examples\n\n### Example 1: Bug Fix Context\n\n```\nTask: \"Fix the authentication token expiry bug\"\n\nCycle 1:\n  DISPATCH: Search for \"token\", \"auth\", \"expiry\" in src\u002F**\n  EVALUATE: Found auth.ts (0.9), tokens.ts (0.8), user.ts (0.3)\n  REFINE: Add \"refresh\", \"jwt\" keywords; exclude user.ts\n\nCycle 2:\n  DISPATCH: Search refined terms\n  EVALUATE: Found session-manager.ts (0.95), jwt-utils.ts (0.85)\n  REFINE: Sufficient context (2 high-relevance files)\n\nResult: auth.ts, tokens.ts, session-manager.ts, jwt-utils.ts\n```\n\n### Example 2: Feature Implementation\n\n```\nTask: \"Add rate limiting to API endpoints\"\n\nCycle 1:\n  DISPATCH: Search \"rate\", \"limit\", \"api\" in routes\u002F**\n  EVALUATE: No matches - codebase uses \"throttle\" terminology\n  REFINE: Add \"throttle\", \"middleware\" keywords\n\nCycle 2:\n  DISPATCH: Search refined terms\n  EVALUATE: Found throttle.ts (0.9), middleware\u002Findex.ts (0.7)\n  REFINE: Need router patterns\n\nCycle 3:\n  DISPATCH: Search \"router\", \"express\" patterns\n  EVALUATE: Found router-setup.ts (0.8)\n  REFINE: Sufficient context\n\nResult: throttle.ts, middleware\u002Findex.ts, router-setup.ts\n```\n\n## Integration with Agents\n\nUse in agent prompts:\n\n```markdown\nWhen retrieving context for this task:\n1. Start with broad keyword search\n2. Evaluate each file's relevance (0-1 scale)\n3. Identify what context is still missing\n4. Refine search criteria and repeat (max 3 cycles)\n5. Return files with relevance >= 0.7\n```\n\n## Best Practices\n\n1. **Start broad, narrow progressively** - Don't over-specify initial queries\n2. **Learn codebase terminology** - First cycle often reveals naming conventions\n3. **Track what's missing** - Explicit gap identification drives refinement\n4. **Stop at \"good enough\"** - 3 high-relevance files beats 10 mediocre ones\n5. **Exclude confidently** - Low-relevance files won't become relevant\n\n## Related\n\n- [The Longform Guide](https:\u002F\u002Fx.com\u002Faffaanmustafa\u002Fstatus\u002F2014040193557471352) - Subagent orchestration section\n- `continuous-learning` skill - For patterns that improve over time\n- Agent definitions bundled with ECC (manual install path: `agents\u002F`)\n",{"name":5,"origin":32,"description":7},"ECC","main","b0f0a87593c578a7898eccaf1b363f7f87c1f99b","plugins\u002FColin4k1024\u002Ftsp\u002Fskills\u002Fiterative-retrieval",483,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-13 02:16:32",{"source":8,"skillName":5,"commands":44,"prompt":54,"zipAvailable":55},[45,48,51],{"tool":46,"command":47},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill iterative-retrieval",{"tool":49,"command":50},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill iterative-retrieval",{"tool":52,"command":53},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill iterative-retrieval","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FColin4k1024\u002Ftsp\u002Fskills\u002Fiterative-retrieval\u002FSKILL.md",true,[57],{"path":58,"resourceType":59,"resourceTypeDesc":60,"sizeBytes":61,"sourceHtmlUrl":62},"agents\u002Fopenai.yaml","REFERENCE","参考文档",267,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FColin4k1024\u002Ftsp\u002Fskills\u002Fiterative-retrieval\u002Fagents\u002Fopenai.yaml",[64,72,79,86,113,138],{"skillNo":65,"name":66,"nameZh":6,"description":67,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":68,"categories":69,"keywords":70,"publishedAt":71},"01M746ZWACVKMZ1BJXJZHYJSSX","migrate-to-e2e","Moves an existing Playwright, Cypress, Selenium, Appium, Detox, Maestro, or TesterArmy suite to e2e one slice at a time, with the old suite running beside the new one until each ported test is proven. Keeps a MIGRATION.md progress file so any later session resumes where the last stopped. Optionally plants a defect to show both suites catch it. Use for \u002Fmigrate-to-e2e, \"migrate our tests to e2e\", \"port these Maestro flows\", \"move our TesterArmy tests\", or when a project has an e2e config and an older suite still running.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fmigrate-to-e2e\u002FSKILL.md",[],[],"2026-11-11 15:11:14",{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":78,"publishedAt":71},"01M746ZPTAX1Q0FBKAB1CYND7E","e2e","Writes, runs, and debugs end-to-end tests for web and mobile apps with e2e, the agentic test runner, which mixes agent.act goals with exact locator assertions on a Playwright browser or an iOS or Android device. Also drives a project's running app live over the e2e MCP server to look at a screen, check a UI change, take a screenshot or record a video, and try a locator; and runs bug bashes, parallel e2e explore runs with each bug proven by a repro test. Use when a project depends on e2e or has an e2e.config.ts, when asked for end-to-end, browser, mobile, or agentic UI tests, to check a change in the running app, to QA or bug bash an app or branch, or when an e2e run fails.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fe2e\u002FSKILL.md",[],[],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":83,"categories":84,"keywords":85,"publishedAt":71},"01M746ZJN784HVHMDP9MQGCSGG","create-verification-skill","Generate a project-local verify-\u003Capp> skill on top of e2e, so any coding agent can launch the app, check it is worth driving, exercise a feature the way a user does, keep evidence, and bug bash it. Writes the skill, a feature map with one explore charter per feature, and proves it once. Use for \u002Fcreate-verification-skill, \"make a verification skill for this repo\", or when a project has no scripted way to prove UI behavior.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftester-army\u002Fe2e\u002Fskills\u002Fcreate-verification-skill\u002FSKILL.md",[],[],{"skillNo":87,"name":88,"nameZh":6,"description":89,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":90,"categories":91,"keywords":92,"publishedAt":27},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112],"search","deja search","never","error","ready","memory","re","decide","fix","design","debugging","when","you","done","reviewer","io","change","cli","merge","past",{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":119,"publishedAt":27},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[120,121,122,108,123,124,125,126,127,128,129,130,131,132,98,133,134,135,136,137],"deja reflect","reflect","review","with","record","github","next","https","node","Rules","decisions","brew","remember","project rules","project","session","end","reflection",{"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":155,"publishedAt":27},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[144,149,152],{"categoryNo":145,"categoryName":146,"taxonomyType":147,"taxonomyTypeDesc":148},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":150,"categoryName":151,"taxonomyType":147,"taxonomyTypeDesc":148},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":153,"categoryName":154,"taxonomyType":147,"taxonomyTypeDesc":148},"01JBSEEDCAT000000000000010","后端与接口",[140,156,157,158,159,160,161,134,162,99,163,164,165,166,104,167,168,169,170,171],"URL","Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","image","mcp server","schedule","api","publish"]