[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-langchain-swarm":3,"mdc--vdtv53-key":31,"related-repo-langchain-swarm":6542,"related-org-langchain-swarm":6651},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":21,"repoUrl":22,"updatedAt":23,"license":24,"forks":25,"topics":26,"repo":27,"sourceUrl":29,"mdContent":30},"swarm","dispatch parallel tasks to subagents","Dispatches many independent items in parallel: create a table, fan out to subagents, aggregate results. One row = one unit of work.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"langchain","LangChain","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Flangchain.png","langchain-ai",[13,15,18],{"name":9,"slug":8,"type":14},"tag",{"name":16,"slug":17,"type":14},"Automation","automation",{"name":19,"slug":20,"type":14},"Multi-Agent","multi-agent",877,"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain-skills","2026-06-19T09:12:49.308515",null,77,[],{"repoUrl":22,"stars":21,"forks":25,"topics":28,"description":24},[],"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Flangchain-skills\u002Ftree\u002FHEAD\u002Fconfig\u002Fskills\u002Fswarm","---\nname: swarm\ndescription: >-\n Dispatches many independent items in parallel: create a table, fan out to\n subagents, aggregate results. One row = one unit of work.\ncompatibility: >-\n Requires @langchain\u002Fquickjs code interpreter with swarm_task PTC tool\nmetadata:\n entrypoint: scripts\u002Findex.ts\n required-ptc-tools: swarm_task read_file write_file edit_file glob\n---\n\n# Swarm\n\nProcess many independent items in parallel. `create` builds a table handle;\n`run` fans work out across rows and merges results back. One row = one unit\nof work — swarm handles batching automatically.\n\n## Flow\n\n1. **Create.** Build a table from a source — files, a glob pattern, or\n pre-parsed records. One row per item. Returns a handle.\n2. **Run.** Dispatch an `instruction` template across rows. Results are merged\n back into the table. Returns `{ completed, failed, skipped, failures }`.\n3. **Aggregate.** Use `rows()` and plain JS to count, filter, or summarize.\n Do not spawn additional subagents for aggregation.\n4. **Retry.** Re-run with `filter: { column: \"\u003Ccol>\", exists: false }` to\n reprocess only failed rows.\n\n## Choosing a source\n\n**`glob` \u002F `filePaths`** — one file = one row. Use when each file is an\nindependent unit of work. Each row gets `{ id, file }`; the subagent reads\nthe file itself via the `{file}` placeholder.\n\n**`tasks`** — pass pre-built records directly. Use when the data lives inside\na file (JSONL, CSV, JSON array). Read and parse the file first inside\n`eval`, then pass the records. One record = one row — do not group\nmultiple items into a single row.\n\nFor small files (under ~500 lines), parse and create in one block:\n\n```javascript\nconst { create } = await import(\"@\u002Fskills\u002Fswarm\");\nconst raw = await tools.readFile({ file_path: \"\u002Fdata.jsonl\" });\nconst records = raw.trim().split(\"\\n\").map(l => JSON.parse(l));\nconst table = await create({ tasks: records });\nconsole.log(table);\n```\n\nFor large files, read in chunks of 500 lines to avoid truncation:\n\n```javascript\nconst { create } = await import(\"@\u002Fskills\u002Fswarm\");\nlet records = [];\nlet offset = 0;\nwhile (true) {\n const chunk = await tools.readFile({ file_path: \"\u002Fdata.txt\", offset, limit: 500 });\n const lines = chunk.split(\"\\n\").filter(l => l.trim());\n for (const l of lines) { records.push({ id: `r${records.length}`, text: l }); }\n if (lines.length \u003C 500) break;\n offset += 500;\n}\nconst table = await create({ tasks: records });\nconsole.log(table);\n```\n\nWhen the file is too large to parse and dispatch in one `eval` call, split\nacross two blocks. Only the block that calls swarm functions needs the import:\n\n```javascript\n\u002F\u002F eval 1: parse only — no swarm import needed\nconst raw = await tools.readFile({ file_path: \"\u002Fdata.jsonl\" });\nglobalThis.records = raw.trim().split(\"\\n\").map(l => JSON.parse(l));\nconsole.log(`Parsed ${globalThis.records.length} records`);\n```\n\n```javascript\n\u002F\u002F eval 2: create and dispatch\nconst { create, run } = await import(\"@\u002Fskills\u002Fswarm\");\nconst table = await create({ tasks: globalThis.records });\nconst result = await run(table.id, {\n instruction: \"Classify {text}\",\n responseSchema: {\n type: \"object\",\n properties: { label: { type: \"string\" } },\n required: [\"label\"],\n },\n});\nconsole.log(result);\n```\n\nPassing `filePaths: [\"\u002Fdata.jsonl\"]` would produce a table with **one row**\npointing at the file — not one row per record inside it.\n\n## When to use `subagentType`\n\nOmit `subagentType` for classification, extraction, labeling, and any task\nwhere a single model call with structured output is sufficient. This is the\ndefault and is significantly cheaper and faster — each dispatch is a direct\nmodel call, no tools, no iteration.\n\nSet `subagentType` when the task requires tools, file access, or multi-step\nreasoning. Each dispatch runs a full agentic loop with the named subagent.\n\n```javascript\n\u002F\u002F Direct model call — classification, no tools needed\nawait run(table.id, {\n instruction: \"Classify {text}\",\n responseSchema: { type: \"object\", properties: { label: { type: \"string\" } }, required: [\"label\"] },\n});\n\n\u002F\u002F Subagent — needs to read files and reason over multiple steps\nawait run(table.id, {\n subagentType: \"reviewer\",\n instruction: \"Review {file} for security issues.\",\n responseSchema: { type: \"object\", properties: { finding: { type: \"string\" } }, required: [\"finding\"] },\n});\n```\n\n## Instruction + context\n\n`instruction` is a per-item template with `{column}` placeholders.\nPlaceholders are resolved by the framework — your column names appear in\nprompts as references to the values listed alongside, never as raw\ntemplate syntax. Subagents do the work — do not process items yourself in\nJS and write the results into rows.\n\n`context` is free-form prose prepended to every subagent prompt. Use it for\nshared background: domain terms, classification rules, examples, etc.\n\n```javascript\nconst { create, run } = await import(\"@\u002Fskills\u002Fswarm\");\n\nconst table = await create({ glob: \"src\u002F**\u002F*.ts\" });\nconst r = await run(table.id, {\n subagentType: \"reviewer\",\n instruction: \"Review {file} for security issues. List findings or write 'no issues'.\",\n context: \"TypeScript Express backend using Prisma ORM. Focus on injection, auth bypass, path traversal.\",\n responseSchema: {\n type: \"object\",\n properties: { review: { type: \"string\" } },\n required: [\"review\"],\n },\n});\nconsole.log(r);\n\u002F\u002F → { completed: 45, failed: 2, skipped: 0, failures: [...] }\n```\n\n## Structured output\n\n`responseSchema` is required. Schema properties become top-level columns on\neach row and constrain what subagents can return.\n\n```javascript\nconst { run } = await import(\"@\u002Fskills\u002Fswarm\");\nawait run(table.id, {\n instruction: \"Classify: {text}\",\n responseSchema: {\n type: \"object\",\n properties: {\n sentiment: { type: \"string\", enum: [\"positive\", \"negative\", \"neutral\"] },\n },\n required: [\"sentiment\"],\n },\n});\n\u002F\u002F Row after: { id: \"r1\", text: \"...\", sentiment: \"positive\" }\n```\n\n## Batching\n\nBy default, swarm auto-batches to keep total dispatches under 10. For small\ntables (≤10 rows) each row gets its own subagent call. For larger tables,\nrows are grouped automatically.\n\nSet `batchSize` to control grouping:\n\n- **Number** — uniform batch size for all rows. `batchSize: 1` forces per-row\n dispatch; `batchSize: 20` groups in twenties.\n- **Function** — `(row, rowCount) => number`. Returns the desired batch size\n for each row. Rows with the same batch size are grouped together, then\n chunked. Allows mixed dispatch where some rows go solo and others batch.\n\n```javascript\nconst { create, run } = await import(\"@\u002Fskills\u002Fswarm\");\nconst table = await create({ tasks: items });\n\n\u002F\u002F Complex items get individual attention; simple ones batch together\nawait run(table.id, {\n instruction: \"Analyze {text}\",\n responseSchema: {\n type: \"object\",\n properties: { analysis: { type: \"string\" } },\n required: [\"analysis\"],\n },\n batchSize: (row) => (row.token_count > 1000 ? 1 : 10),\n});\n```\n\nBatch sizes are clamped to [1, 50] after evaluation.\n\n## Aggregation\n\nAfter `run()`, use `rows()` and plain JS — no additional subagents needed.\n\n```javascript\nconst { rows } = await import(\"@\u002Fskills\u002Fswarm\");\nconst data = await rows(table.id, { columns: [\"sentiment\"] });\nconst counts = {};\ndata.forEach(r => { counts[r.sentiment] = (counts[r.sentiment] || 0) + 1 });\nconsole.log(counts);\n\u002F\u002F → { positive: 120, negative: 45, neutral: 35 }\n```\n\n## Chaining passes\n\n`run` updates the table in place — chain calls to accumulate columns.\n\n```javascript\nconst { create, run } = await import(\"@\u002Fskills\u002Fswarm\");\nconst table = await create({ tasks: interviews });\nawait run(table.id, {\n instruction: \"Classify sentiment of {text}\",\n responseSchema: {\n type: \"object\",\n properties: { sentiment: { type: \"string\", enum: [\"positive\", \"negative\", \"neutral\"] } },\n required: [\"sentiment\"],\n },\n});\nawait run(table.id, {\n filter: { column: \"sentiment\", equals: \"negative\" },\n instruction: \"Summarize why {text} had negative sentiment.\",\n responseSchema: {\n type: \"object\",\n properties: { summary: { type: \"string\" } },\n required: [\"summary\"],\n },\n});\n```\n\n## Action-only tasks\n\nWhen subagents perform actions (write a file, apply a fix) rather than return\ndata, use a simple schema with a status or marker field. The `exists: false`\nfilter still works for retries.\n\n```javascript\nconst { create, run } = await import(\"@\u002Fskills\u002Fswarm\");\nconst fixedSchema = {\n type: \"object\",\n properties: { fixed: { type: \"string\" } },\n required: [\"fixed\"],\n};\nconst table = await create({ glob: \"src\u002F**\u002F*.ts\" });\nawait run(table.id, {\n subagentType: \"fixer\",\n instruction: \"Add missing JSDoc to all exported functions in {file}.\",\n responseSchema: fixedSchema,\n});\n\u002F\u002F retry any that failed\nawait run(table.id, {\n subagentType: \"fixer\",\n instruction: \"Add missing JSDoc to all exported functions in {file}.\",\n responseSchema: fixedSchema,\n filter: { column: \"fixed\", exists: false },\n});\n```\n\n## Filtering\n\n```javascript\n{ column: \"status\", equals: \"done\" }\n{ column: \"status\", notEquals: \"done\" }\n{ column: \"category\", in: [\"A\", \"B\"] }\n{ column: \"result\", exists: false } \u002F\u002F not yet processed\n{ and: [filter1, filter2] }\n{ or: [filter1, filter2] }\n```\n\n## Technical notes\n\n- **Only import `@\u002Fskills\u002Fswarm` in blocks where you call swarm functions.**\n Data preparation (reading files, parsing, storing in `globalThis`) does not\n need the import. Destructure only what you use: `{ create }`, `{ run }`,\n `{ create, run }`, etc.\n- **Console output is capped at ~5 KB.** Never log raw file contents —\n log only counts and short samples.\n- **`readFile` inside `eval` returns raw content — no line-number\n prefixes.** Request at most 500 lines per call. For files with more\n than 500 lines, loop with incrementing `offset`.\n- **When building a table from a file, read it inside `eval`.** Data read\n inside the sandbox stays there; it never enters the agent's context window.\n- **Never write to `.swarm\u002F` directly.** Always use `create()`.\n- **Everything the subagent needs must be in `instruction` + `context`.**\n Subagents can't see the agent's context.\n- **Row ids must be unique.** `create()` rejects sources that produce\n duplicate ids. For `tasks`, that's a caller-side responsibility; for\n `glob` \u002F `filePaths`, ids are auto-disambiguated by parent directory.\n- **Unknown columns fail fast.** If `instruction` references `{foo}` and\n no matched row provides `foo`, `run()` throws before any subagent is\n dispatched.\n\n## API Reference\n\n### `create(source)`\n\nCreate a table. Returns a handle `{ id, count, columns }`.\n\n| Source | Description |\n|--------|------------|\n| `{ glob: \"src\u002F**\u002F*.ts\" }` or `{ glob: [\"src\u002F**\u002F*.ts\", \"lib\u002F**\u002F*.ts\"] }` | Match files by one or more patterns. Columns: `id`, `file` |\n| `{ filePaths: [\"a.ts\", \"b.ts\"] }` | Explicit file list. Columns: `id`, `file` |\n| `{ tasks: [{ id: \"t1\", text: \"...\" }] }` | Custom rows. Each must have `id` |\n\n### `run(tableId, options)`\n\nDispatch work across rows. Returns `{ completed, failed, skipped, failures }`.\n\n| Option | Default | Description |\n|--------|---------|------------|\n| `instruction` | (required) | Template with `{column}` placeholders |\n| `responseSchema` | (required) | JSON Schema (`type: \"object\"`) — properties become row columns |\n| `context` | — | Prose prepended to every subagent prompt |\n| `filter` | — | Only dispatch matching rows |\n| `subagentType` | — | Name of subagent to dispatch to. When set, runs a full agentic loop. When omitted, runs a direct model call |\n| `batchSize` | auto | Number or `(row, rowCount) => number`. Auto caps dispatches at 10; `1` = per-row; function = per-row sizing |\n| `concurrency` | `10` | Max concurrent subagent dispatches (clamped to 1–10) |\n\n### `rows(tableId, options?)`\n\nRetrieve rows. 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Covers create_deep_agent(), harness architecture, SKILL.md format, and configuration options.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6550,6553,6556,6557],{"name":6551,"slug":6552,"type":14},"Agents","agents",{"name":6554,"slug":6555,"type":14},"Architecture","architecture",{"name":9,"slug":8,"type":14},{"name":6558,"slug":6559,"type":14},"LLM","llm","2026-04-06T18:26:24.822592",{"slug":6562,"name":6562,"fn":6563,"description":6564,"org":6565,"tags":6566,"stars":21,"repoUrl":22,"updatedAt":6575},"deep-agents-memory","implement memory and persistence for Deep Agents","INVOKE THIS SKILL when your Deep Agent needs memory, persistence, or filesystem access. Covers StateBackend (ephemeral), StoreBackend (persistent), FilesystemMiddleware, and CompositeBackend for routing.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6567,6568,6569,6572],{"name":6551,"slug":6552,"type":14},{"name":9,"slug":8,"type":14},{"name":6570,"slug":6571,"type":14},"Memory","memory",{"name":6573,"slug":6574,"type":14},"Storage","storage","2026-04-06T18:26:26.065654",{"slug":6577,"name":6577,"fn":6578,"description":6579,"org":6580,"tags":6581,"stars":21,"repoUrl":22,"updatedAt":6591},"deep-agents-orchestration","orchestrate subagents and tasks in Deep Agents","INVOKE THIS SKILL when using subagents, task planning, or human approval in Deep Agents. Covers SubAgentMiddleware, TodoList for planning, and HITL interrupts.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6582,6583,6586,6587,6588],{"name":6551,"slug":6552,"type":14},{"name":6584,"slug":6585,"type":14},"Approvals","approvals",{"name":9,"slug":8,"type":14},{"name":19,"slug":20,"type":14},{"name":6589,"slug":6590,"type":14},"Workflow Automation","workflow-automation","2026-04-06T18:26:32.280463",{"slug":6593,"name":6593,"fn":6594,"description":6595,"org":6596,"tags":6597,"stars":21,"repoUrl":22,"updatedAt":6603},"deepagents-python-quickstart","scaffold local Deep Agents","Scaffold a minimal local Deep Agent in Python by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6598,6599,6600],{"name":6551,"slug":6552,"type":14},{"name":9,"slug":8,"type":14},{"name":6601,"slug":6602,"type":14},"Python","python","2026-07-24T06:09:00.291108",{"slug":6605,"name":6605,"fn":6606,"description":6607,"org":6608,"tags":6609,"stars":21,"repoUrl":22,"updatedAt":6615},"deepagents-typescript-quickstart","build Deep Agents in TypeScript","Scaffold a minimal local Deep Agent in TypeScript by following the official quickstart, using provider-native web search instead of Tavily. Use when the user wants to quickly build or try a Deep Agent locally.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6610,6611,6612],{"name":6551,"slug":6552,"type":14},{"name":9,"slug":8,"type":14},{"name":6613,"slug":6614,"type":14},"TypeScript","typescript","2026-07-24T06:09:00.673714",{"slug":6617,"name":6617,"fn":6618,"description":6619,"org":6620,"tags":6621,"stars":21,"repoUrl":22,"updatedAt":6631},"ecosystem-primer","select frameworks for LangChain and LangGraph agents","INVOKE FIRST for any LangChain \u002F LangGraph \u002F Deep Agents agent building project before consulting other skills or writing any agent code. Required starting point for up to date info on framework selection (LangChain vs LangGraph vs Deep Agents vs hybrid composition), agent patterns, install, environment setup, and which skill to load next.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6622,6623,6626,6627,6628],{"name":6551,"slug":6552,"type":14},{"name":6624,"slug":6625,"type":14},"AI Infrastructure","ai-infrastructure",{"name":6554,"slug":6555,"type":14},{"name":9,"slug":8,"type":14},{"name":6629,"slug":6630,"type":14},"LangGraph","langgraph","2026-07-24T05:42:48.348966",{"slug":6633,"name":6633,"fn":6634,"description":6635,"org":6636,"tags":6637,"stars":21,"repoUrl":22,"updatedAt":6649},"eval-engineering","create and audit Harbor agent evals","Iteratively inspect an agent repository and optional user-provided traces, interview the user, and create, run, and audit Harbor evals one at a time. Use for agent evals, Harbor tasks, benchmark cases, verifier design, or controlled agent environments.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6638,6639,6642,6645,6646],{"name":6551,"slug":6552,"type":14},{"name":6640,"slug":6641,"type":14},"Code Analysis","code-analysis",{"name":6643,"slug":6644,"type":14},"Evals","evals",{"name":9,"slug":8,"type":14},{"name":6647,"slug":6648,"type":14},"Testing","testing","2026-07-31T05:53:29.552458",22,{"items":6652,"total":6823},[6653,6674,6685,6702,6715,6730,6743,6756,6770,6780,6791,6810],{"slug":6654,"name":6654,"fn":6655,"description":6656,"org":6657,"tags":6658,"stars":6671,"repoUrl":6672,"updatedAt":6673},"analyze-market","perform market analysis and size estimation","Perform a market analysis for a product category or segment. Trigger on: market analysis, market size, TAM SAM SOM, market opportunity, industry analysis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6659,6662,6665,6668],{"name":6660,"slug":6661,"type":14},"Marketing","marketing",{"name":6663,"slug":6664,"type":14},"Research","research",{"name":6666,"slug":6667,"type":14},"Sales","sales",{"name":6669,"slug":6670,"type":14},"Strategy","strategy",26592,"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fdeepagents","2026-04-18T04:46:54.557115",{"slug":6675,"name":6675,"fn":6676,"description":6677,"org":6678,"tags":6679,"stars":6671,"repoUrl":6672,"updatedAt":6684},"arxiv-search","search arXiv for academic research papers","Searches arXiv for preprints and academic papers, retrieves abstracts, and filters by topic. Use when the user asks to find research papers, search arXiv, look up preprints, find academic articles in physics, math, CS, biology, statistics, or related fields.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6680,6681],{"name":6663,"slug":6664,"type":14},{"name":6682,"slug":6683,"type":14},"Search","search","2026-05-13T06:11:01.203061",{"slug":6686,"name":6686,"fn":6687,"description":6688,"org":6689,"tags":6690,"stars":6671,"repoUrl":6672,"updatedAt":6701},"blog-post","write SEO-optimized blog posts","Write long-form blog posts with SEO optimization and clear structure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6691,6694,6695,6698],{"name":6692,"slug":6693,"type":14},"Content Creation","content-creation",{"name":6660,"slug":6661,"type":14},{"name":6696,"slug":6697,"type":14},"SEO","seo",{"name":6699,"slug":6700,"type":14},"Writing","writing","2026-04-15T05:00:54.149813",{"slug":6703,"name":6703,"fn":6704,"description":6705,"org":6706,"tags":6707,"stars":6671,"repoUrl":6672,"updatedAt":6714},"competitor-analysis","analyze competitors and market positioning","Analyze competitors in a given market segment. Trigger on: competitive landscape, competitor analysis, market comparison, competitive positioning.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6708,6711,6712,6713],{"name":6709,"slug":6710,"type":14},"Competitive Intelligence","competitive-intelligence",{"name":6660,"slug":6661,"type":14},{"name":6663,"slug":6664,"type":14},{"name":6669,"slug":6670,"type":14},"2026-04-18T04:46:55.79306",{"slug":6716,"name":6716,"fn":6717,"description":6718,"org":6719,"tags":6720,"stars":6671,"repoUrl":6672,"updatedAt":6729},"deepagents-thread-inspector","inspect local Deep Agents conversation threads","Inspect and explain conversations in the local Deep Agents Code SQLite session store. Use as a fallback when LangSmith trace tooling is unavailable, for offline or untraced sessions, or when asked to identify or summarize a local dcode thread, inspect checkpoint metadata, list recent local threads, or parse ~\u002F.deepagents\u002F.state\u002Fsessions.db and a thread UUID or prefix.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6721,6722,6725,6726],{"name":6551,"slug":6552,"type":14},{"name":6723,"slug":6724,"type":14},"Debugging","debugging",{"name":9,"slug":8,"type":14},{"name":6727,"slug":6728,"type":14},"SQLite","sqlite","2026-07-24T06:08:57.102689",{"slug":6731,"name":6731,"fn":6732,"description":6733,"org":6734,"tags":6735,"stars":6671,"repoUrl":6672,"updatedAt":6742},"langgraph-docs","build stateful agents with LangGraph","Fetches and references LangGraph Python documentation to build stateful agents, create multi-agent workflows, and implement human-in-the-loop patterns. Use when the user asks about LangGraph, graph agents, state machines, agent orchestration, LangGraph API, or needs LangGraph implementation guidance.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6736,6737,6740,6741],{"name":6551,"slug":6552,"type":14},{"name":6738,"slug":6739,"type":14},"Documentation","documentation",{"name":6629,"slug":6630,"type":14},{"name":19,"slug":20,"type":14},"2026-05-13T06:11:03.650877",{"slug":6744,"name":6744,"fn":6745,"description":6746,"org":6747,"tags":6748,"stars":6671,"repoUrl":6672,"updatedAt":6755},"remember","capture knowledge into persistent memory","Review the current conversation and capture valuable knowledge — best practices, coding conventions, architecture decisions, workflows, and user feedback — into persistent memory (AGENTS.md) or reusable skills. Use when the user says: (1) remember this, (2) save what we learned, (3) update memory, (4) capture learnings.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6749,6750,6751,6754],{"name":6551,"slug":6552,"type":14},{"name":6738,"slug":6739,"type":14},{"name":6752,"slug":6753,"type":14},"Knowledge Management","knowledge-management",{"name":6570,"slug":6571,"type":14},"2026-05-13T06:10:58.510037",{"slug":6757,"name":6757,"fn":6758,"description":6759,"org":6760,"tags":6761,"stars":6671,"repoUrl":6672,"updatedAt":6769},"skill-creator","create agent skills and tool integrations","Guide for creating effective skills that extend agent capabilities with specialized knowledge, workflows, or tool integrations. Use this skill when the user asks to: (1) create a new skill, (2) make a skill, (3) build a skill, (4) set up a skill, (5) initialize a skill, (6) scaffold a skill, (7) update or modify an existing skill, (8) validate a skill, (9) learn about skill structure, (10) understand how skills work, or (11) get guidance on skill design patterns. Trigger on phrases like \"create a skill\", \"new skill\", \"make a skill\", \"skill for X\", \"how do I create a skill\", or \"help me build a skill\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6762,6763,6766],{"name":6551,"slug":6552,"type":14},{"name":6764,"slug":6765,"type":14},"Engineering","engineering",{"name":6767,"slug":6768,"type":14},"Plugin Development","plugin-development","2026-05-13T06:10:59.88449",{"slug":6771,"name":6771,"fn":6772,"description":6773,"org":6774,"tags":6775,"stars":6671,"repoUrl":6672,"updatedAt":6779},"social-media","create optimized social media posts","Create social media posts optimized for engagement across platforms.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6776,6777,6778],{"name":6692,"slug":6693,"type":14},{"name":6660,"slug":6661,"type":14},{"name":6699,"slug":6700,"type":14},"2026-04-15T05:00:55.37452",{"slug":6781,"name":6781,"fn":6782,"description":6783,"org":6784,"tags":6785,"stars":6671,"repoUrl":6672,"updatedAt":6790},"web-research","conduct and synthesize web research","Searches multiple web sources, synthesizes findings, and produces cited research reports using delegated subagents. Use when the user asks to research a topic online, search the web, look something up, find current information, compare options, or produce a research report.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6786,6787,6788,6789],{"name":6551,"slug":6552,"type":14},{"name":19,"slug":20,"type":14},{"name":6663,"slug":6664,"type":14},{"name":6682,"slug":6683,"type":14},"2026-05-13T06:11:04.930044",{"slug":6792,"name":6792,"fn":6793,"description":6794,"org":6795,"tags":6796,"stars":6807,"repoUrl":6808,"updatedAt":6809},"mermaid-diagrams","embed Mermaid diagrams in documentation","Embed Mermaid diagrams in generated wiki pages. Use whenever documenting a runtime or request flow, a call sequence, a state machine or lifecycle, a data model or entity relationships, or non-trivial control flow, since these are clearer as a diagram than as prose. Also use when an update run touches a page that already contains a mermaid fence, or a page that contains a text fence a previous run degraded.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6797,6800,6801,6804],{"name":6798,"slug":6799,"type":14},"Diagrams","diagrams",{"name":6738,"slug":6739,"type":14},{"name":6802,"slug":6803,"type":14},"Markdown","markdown",{"name":6805,"slug":6806,"type":14},"Technical Writing","technical-writing",12181,"https:\u002F\u002Fgithub.com\u002Flangchain-ai\u002Fopenwiki","2026-07-24T06:09:01.089597",{"slug":6811,"name":6811,"fn":6812,"description":6813,"org":6814,"tags":6815,"stars":6807,"repoUrl":6808,"updatedAt":6822},"write-connector","implement OpenWiki source connectors","Add a new built-in OpenWiki source connector. Use when a user asks to create or implement an OpenWiki connector.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},[6816,6819],{"name":6817,"slug":6818,"type":14},"API Development","api-development",{"name":6820,"slug":6821,"type":14},"Integrations","integrations","2026-07-18T05:48:23.961804",41]