[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6N227VQ5ABJXVZXGDPRJDEV":3,"skill-same:01M6N227VQ5ABJXVZXGDPRJDEV":91},{"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":17,"publishedAt":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":55,"updatedAt":56,"install":57,"resources":70},"01M6N227VQ5ABJXVZXGDPRJDEV","raglite",null,"Local-first RAG cache: distill docs into structured Markdown, then index\u002Fquery with Chroma + hybrid search (vector + keyword).","Kernel8901\u002Fai-agent-skills-classification","Kernel8901","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F72647553?v=4","01M6KV388J2H5PNC8D8P940BJD","01M6KV38CS5B3AY63RKZB410YM",7,"MIT","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fraglite-local-rag-cache\u002FSKILL.md",[],[5,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"distill","hybrid","darwin","pip","with","Hybrid Search","local","query","chroma","docs","python3","markdown","first","cache","vector","search","index","rag","linux","2026-11-10 10:12:30",false,"---\nname: raglite\nversion: 1.0.0\ndescription: \"Local-first RAG cache: distill docs into structured Markdown, then index\u002Fquery with Chroma + hybrid search (vector + keyword).\"\nmetadata:\n  {\n    \"openclaw\": {\n      \"emoji\": \"?\",\n      \"os\": [\"darwin\", \"linux\"],\n      \"requires\": { \"bins\": [\"python3\", \"pip\"] }\n    }\n  }\n---\n\n# RAGLite — a local RAG cache (not a memory replacement)\n\nRAGLite is a **local-first RAG cache**.\n\nIt does **not** replace model memory or chat context. It gives your agent a durable place to store and retrieve information the model wasn’t trained on — especially useful for **local\u002Fprivate knowledge** (school work, personal notes, medical records, internal runbooks).\n\n## Why it’s better than “paid RAG” \u002F knowledge bases (for many use cases)\n\n- **Local-first privacy:** keep sensitive data on your machine\u002Fnetwork.\n- **Open-source building blocks:** **Chroma** ? + **ripgrep** ⚡ — no managed vector DB required.\n- **Compression-before-embeddings:** distill first → less fluff\u002Fduplication → cheaper prompts + more reliable retrieval.\n- **Auditable artifacts:** the distilled Markdown is human-readable and version-controllable.\n\nIf you later outgrow local, you can swap in a hosted DB — but you often don’t need to.\n\n## What it does\n\n### 1) Condense ✍️\nTurns docs into structured Markdown outputs (low fluff, more “what matters”).\n\n### 2) Index ?\nEmbeds the distilled outputs into a **Chroma** collection (one DB, many collections).\n\n### 3) Query ?\nHybrid retrieval:\n- vector similarity via Chroma\n- keyword matches via ripgrep (`rg`)\n\n## Default engine\n\nThis skill defaults to **OpenClaw** ? for condensation unless you pass `--engine` explicitly.\n\n## Prereqs\n\n- **Python 3.11+**\n- For indexing\u002Fquery:\n  - Chroma server reachable (default `http:\u002F\u002F127.0.0.1:8100`)\n- For hybrid keyword search:\n  - `rg` installed (`brew install ripgrep`)\n- For OpenClaw engine:\n  - OpenClaw Gateway `\u002Fv1\u002Fresponses` reachable\n  - `OPENCLAW_GATEWAY_TOKEN` set if your gateway requires auth\n\n## Install (skill runtime)\n\nThis skill installs RAGLite into a skill-local venv:\n\n```bash\n.\u002Fscripts\u002Finstall.sh\n```\n\nIt installs from GitHub:\n- `git+https:\u002F\u002Fgithub.com\u002FVirajSanghvi1\u002Fraglite.git@main`\n\n## Usage\n\n### One-command pipeline (recommended)\n\n```bash\n.\u002Fscripts\u002Fraglite.sh run \u002Fpath\u002Fto\u002Fdocs \\\n  --out .\u002Fraglite_out \\\n  --collection my-docs \\\n  --chroma-url http:\u002F\u002F127.0.0.1:8100 \\\n  --skip-existing \\\n  --skip-indexed \\\n  --nodes\n```\n\n### Query\n\n```bash\n.\u002Fscripts\u002Fraglite.sh query .\u002Fraglite_out \\\n  --collection my-docs \\\n  --top-k 5 \\\n  --keyword-top-k 5 \\\n  \"rollback procedure\"\n```\n\n## Outputs (what gets written)\n\nIn `--out` you’ll see:\n- `*.tool-summary.md`\n- `*.execution-notes.md`\n- optional: `*.outline.md`\n- optional: `*\u002Fnodes\u002F*.md` plus per-doc `*.index.md` and a root `index.md`\n- metadata in `.raglite\u002F` (cache, run stats, errors)\n\n## Troubleshooting\n\n- **Chroma not reachable** → check `--chroma-url`, and that Chroma is running.\n- **No keyword results** → install ripgrep (`rg --version`).\n- **OpenClaw engine errors** → ensure gateway is up and token env var is set.\n\n## Pitch (for ClawHub listing)\n\nRAGLite is a **local RAG cache** for repeated lookups.\n\nWhen you (or your agent) keep re-searching for the same non-training data — local notes, school work, medical records, internal docs — RAGLite gives you a private, auditable library:\n\n1) **Distill** to structured Markdown (compression-before-embeddings)\n2) **Index** locally into Chroma\n3) **Query** with hybrid retrieval (vector + keyword)\n\nIt doesn’t replace memory\u002Fcontext — it’s the place to store what you need again.\n","# RAGLite — a local RAG cache (not a memory replacement)\n\nRAGLite is a **local-first RAG cache**.\n\nIt does **not** replace model memory or chat context. It gives your agent a durable place to store and retrieve information the model wasn’t trained on — especially useful for **local\u002Fprivate knowledge** (school work, personal notes, medical records, internal runbooks).\n\n## Why it’s better than “paid RAG” \u002F knowledge bases (for many use cases)\n\n- **Local-first privacy:** keep sensitive data on your machine\u002Fnetwork.\n- **Open-source building blocks:** **Chroma** ? + **ripgrep** ⚡ — no managed vector DB required.\n- **Compression-before-embeddings:** distill first → less fluff\u002Fduplication → cheaper prompts + more reliable retrieval.\n- **Auditable artifacts:** the distilled Markdown is human-readable and version-controllable.\n\nIf you later outgrow local, you can swap in a hosted DB — but you often don’t need to.\n\n## What it does\n\n### 1) Condense ✍️\nTurns docs into structured Markdown outputs (low fluff, more “what matters”).\n\n### 2) Index ?\nEmbeds the distilled outputs into a **Chroma** collection (one DB, many collections).\n\n### 3) Query ?\nHybrid retrieval:\n- vector similarity via Chroma\n- keyword matches via ripgrep (`rg`)\n\n## Default engine\n\nThis skill defaults to **OpenClaw** ? for condensation unless you pass `--engine` explicitly.\n\n## Prereqs\n\n- **Python 3.11+**\n- For indexing\u002Fquery:\n  - Chroma server reachable (default `http:\u002F\u002F127.0.0.1:8100`)\n- For hybrid keyword search:\n  - `rg` installed (`brew install ripgrep`)\n- For OpenClaw engine:\n  - OpenClaw Gateway `\u002Fv1\u002Fresponses` reachable\n  - `OPENCLAW_GATEWAY_TOKEN` set if your gateway requires auth\n\n## Install (skill runtime)\n\nThis skill installs RAGLite into a skill-local venv:\n\n```bash\n.\u002Fscripts\u002Finstall.sh\n```\n\nIt installs from GitHub:\n- `git+https:\u002F\u002Fgithub.com\u002FVirajSanghvi1\u002Fraglite.git@main`\n\n## Usage\n\n### One-command pipeline (recommended)\n\n```bash\n.\u002Fscripts\u002Fraglite.sh run \u002Fpath\u002Fto\u002Fdocs \\\n  --out .\u002Fraglite_out \\\n  --collection my-docs \\\n  --chroma-url http:\u002F\u002F127.0.0.1:8100 \\\n  --skip-existing \\\n  --skip-indexed \\\n  --nodes\n```\n\n### Query\n\n```bash\n.\u002Fscripts\u002Fraglite.sh query .\u002Fraglite_out \\\n  --collection my-docs \\\n  --top-k 5 \\\n  --keyword-top-k 5 \\\n  \"rollback procedure\"\n```\n\n## Outputs (what gets written)\n\nIn `--out` you’ll see:\n- `*.tool-summary.md`\n- `*.execution-notes.md`\n- optional: `*.outline.md`\n- optional: `*\u002Fnodes\u002F*.md` plus per-doc `*.index.md` and a root `index.md`\n- metadata in `.raglite\u002F` (cache, run stats, errors)\n\n## Troubleshooting\n\n- **Chroma not reachable** → check `--chroma-url`, and that Chroma is running.\n- **No keyword results** → install ripgrep (`rg --version`).\n- **OpenClaw engine errors** → ensure gateway is up and token env var is set.\n\n## Pitch (for ClawHub listing)\n\nRAGLite is a **local RAG cache** for repeated lookups.\n\nWhen you (or your agent) keep re-searching for the same non-training data — local notes, school work, medical records, internal docs — RAGLite gives you a private, auditable library:\n\n1) **Distill** to structured Markdown (compression-before-embeddings)\n2) **Index** locally into Chroma\n3) **Query** with hybrid retrieval (vector + keyword)\n\nIt doesn’t replace memory\u002Fcontext — it’s the place to store what you need again.\n",{"name":5,"version":42,"metadata":43,"description":7},"1.0.0",{"openclaw":44},{"os":45,"emoji":46,"requires":47},[20,36],"?",{"bins":48},[28,21],"main","f4cb2d49f7667e79ef7804f50396d5aaffdf1f56","人工智能与大型语言模型\u002Fraglite-local-rag-cache",1,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification","https:\u002F\u002Fgithub.com\u002FKernel8901","a little little little dobe in life but science geek","2026-04-05 16:43:56",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add Kernel8901\u002Fai-agent-skills-classification --skill raglite",{"tool":63,"command":64},"bunx","bunx skills add Kernel8901\u002Fai-agent-skills-classification --skill raglite",{"tool":66,"command":67},"pnpm","pnpm dlx skills add Kernel8901\u002Fai-agent-skills-classification --skill raglite","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fraglite-local-rag-cache\u002FSKILL.md",true,[71,77,81,87],{"path":72,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":75,"sourceHtmlUrl":76},"_meta.json","REFERENCE","参考文档",289,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fraglite-local-rag-cache\u002F_meta.json",{"path":78,"resourceType":73,"resourceTypeDesc":74,"sizeBytes":79,"sourceHtmlUrl":80},"openclaw.plugin.json",374,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fraglite-local-rag-cache\u002Fopenclaw.plugin.json",{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"scripts\u002Finstall.sh","SCRIPT","脚本",592,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fraglite-local-rag-cache\u002Fscripts\u002Finstall.sh",{"path":88,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":89,"sourceHtmlUrl":90},"scripts\u002Fraglite.sh",551,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fraglite-local-rag-cache\u002Fscripts\u002Fraglite.sh",[92,116,151,162,172,200],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":37},"01M6Q5R6WZ06RRB9RQTW54B34B","zyfai","Earn yield on any Ethereum wallet on Base, Arbitrum, and Plasma. Use when a user wants passive DeFi yield on their funds. 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