Chroma Vector Database
Orchestra-Research/AI-Research-SKILLs
Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.
本地向量知识库,支持按项目管理文档(docx/doc/pdf/md),语义检索。默认用硅基流动免费 API,零模型安装即可使用。支持多项目隔离、中文制度文档专用切片、Embedding+Rerank 两阶段检索。触发词:知识库、向量检索、RAG、制度检索、文档入库、语义搜索、local…
$ npx skills add nigo81/nigo-skills --skill local-rag -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install nigo81/nigo-skills local-rag --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/nigo81/nigo-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/local-rag .claude/skills/local-rag && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "local-rag" agent skill from https://github.com/nigo81/nigo-skills/tree/main/local-rag into .claude/skills/local-rag/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-rag", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/nigo81/nigo-skills/tree/main/local-ragType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add nigo81/nigo-skills --skill local-rag -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install nigo81/nigo-skills local-rag --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nigo81/nigo-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/local-rag .agents/skills/local-rag && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "local-rag" agent skill from https://github.com/nigo81/nigo-skills/tree/main/local-rag into .agents/skills/local-rag/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-rag", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add nigo81/nigo-skills --skill local-rag -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install nigo81/nigo-skills local-rag --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nigo81/nigo-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/local-rag .cursor/skills/local-rag && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "local-rag" agent skill from https://github.com/nigo81/nigo-skills/tree/main/local-rag into .cursor/skills/local-rag/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-rag", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/nigo81/nigo-skills.git --path local-rag--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add nigo81/nigo-skills --skill local-rag -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install nigo81/nigo-skills local-rag --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nigo81/nigo-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/local-rag .gemini/skills/local-rag && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "local-rag" agent skill from https://github.com/nigo81/nigo-skills/tree/main/local-rag into .gemini/skills/local-rag/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-rag", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install nigo81/nigo-skills local-ragInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add nigo81/nigo-skills --skill local-rag -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/nigo81/nigo-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/local-rag .github/skills/local-rag && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "local-rag" agent skill from https://github.com/nigo81/nigo-skills/tree/main/local-rag into .github/skills/local-rag/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-rag", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add nigo81/nigo-skills --skill local-rag -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install nigo81/nigo-skills local-rag --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/nigo81/nigo-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/local-rag .opencode/skills/local-rag && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "local-rag" agent skill from https://github.com/nigo81/nigo-skills/tree/main/local-rag into .opencode/skills/local-rag/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "local-rag", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
local-rag本地向量知识库,支持按项目管理文档(docx/doc/pdf/md),语义检索。默认用硅基流动免费 API,零模型安装即可使用。支持多项目隔离、中文制度文档专用切片、Embedding+Rerank 两阶段检索。触发词:知识库、向量检索、RAG、制度检索、文档入库、语义搜索、local…
Local RAG is an agent skill from nigo81/nigo-skills. 本地向量知识库,支持按项目管理文档(docx/doc/pdf/md),语义检索。默认用硅基流动免费 API,零模型安装即可使用。支持多项目隔离、中文制度文档专用切片、Embedding+Rerank 两阶段检索。触发词:知识库、向量检索、RAG、制度检索、文档入库、语义搜索、local rag、搜制度、查条款、文档检索、从文档中搜、语义匹配。即使用户只是说"帮我从这些文件里找到关于XX的规定"或"对比两份制度的差异",也应使用本 skill。
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 19 other files (for example `README.md`, `config.example.yaml` and `mcp_server.py`).
It sits in AI & LLM Engineering, covering Retrieval-augmented generation, Embeddings and Word documents. It works with Microsoft Word, OpenAI, Ollama and macOS. The repository describes itself as: 审计师专属 AI 技能包 —— 让 AI 拥有资深审计专家的思维方式. The licence is MIT.
3 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 6468211. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
pippython3From the folder's file list and the shell code blocks in SKILL.md.
Links to these hosts (documentation or services it may open):
cloud.siliconflow.cnFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
SILICONFLOW_API_KEYOPENAI_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Local RAG loads about 1.4k tokens when it runs. Until then it costs about 58 tokens; SKILL.md has 343 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from nigo81/nigo-skills at commit 6468211, republished under its MIT licence (© nigo81). 343 words, ~1,441 tokens.
.claude/skills/local-rag/SKILL.md (or your agent's skills folder). This skill also uses 17 other files; get the full folder from GitHub.轻量级本地向量知识库,支持按项目管理文档,语义检索制度条款。
作者: nigo(公众号「逆行的狗」) 面向财务/审计从业者的 AI 效率工具,有任何问题可关注公众号反馈。
bin/local-rag 是独立的 CLI 入口脚本,自动定位 skill 目录,可在任意位置运行。
# 设置快捷方式(一次性,加到 PATH)
export PATH="$HOME/.claude/skills/local-rag/bin:$PATH"
# 或创建别名:alias local-rag="$HOME/.claude/skills/local-rag/bin/local-rag"
# 首次配置(交互式向导)
local-rag setup
# 项目管理
local-rag create my-project
local-rag delete my-project
local-rag list
# 入库
local-rag ingest my-project /path/to/docs
local-rag ingest my-project /path/to/file.docx --label "财务制度"
# 检索
local-rag search my-project "消防安全管理"
local-rag search my-project "消防安全管理" --rerank --top-k 20
# 工具
local-rag chunk-test /path/to/file.doc
local-rag info也可以直接用完整路径:~/.claude/skills/local-rag/bin/local-rag
import sys, os
# 添加 skill 目录(根据实际安装位置调整)
sys.path.insert(0, os.path.expanduser("~/.claude/skills/local-rag"))
from src.pipeline import Pipeline
pipeline = Pipeline()
# 项目管理
pipeline.create_project("my-project")
pipeline.list_projects()
pipeline.delete_project("my-project")
# 入库(文件或文件夹)
result = pipeline.ingest("my-project", "/path/to/docs")
# 检索
results = pipeline.search("my-project", "消防安全管理", top_k=15)
# 检索 + Rerank
results = pipeline.search_with_rerank("my-project", "消防安全管理", final_k=10)
# 测试切片
result = pipeline.chunk_test("/path/to/file.doc")pip install fastmcp
python3 ~/.claude/skills/local-rag/mcp_server.py6 个 tool:create_project / delete_project / list_projects / ingest / search / rerank_search
| Provider | 适用场景 | 需要什么 |
|---|---|---|
| siliconflow(默认) | 大多数场景,免费额度无限量 | API Key |
| ollama | 离线/内网环境 | Ollama + 模型(~2.5GB) |
| openai | 已有 OpenAI 账号 | API Key |
⚠️ API Key 安全: 不要把 API Key 直接写在 config.yaml 里!请设置环境变量
SILICONFLOW_API_KEY,config.yaml 中用${SILICONFLOW_API_KEY}引用。
环境变量 SILICONFLOW_API_KEY 设置硅基流动 Key 即可使用默认配置。如果没有 Key,请先到 硅基流动 免费注册并创建 API Key。
当用户没有设置 API Key 时,按以下顺序查找,不要 grep 搜索:
SILICONFLOW_API_KEY(echo $SILICONFLOW_API_KEY / Windows: echo %SILICONFLOW_API_KEY%)~/Library/Application Support/local-rag/config.yaml%LOCALAPPDATA%\local-rag\config.yaml~/.local/share/local-rag/config.yaml请先设置硅基流动 API Key(免费,注册地址:https://cloud.siliconflow.cn):
macOS/Linux:
echo 'export SILICONFLOW_API_KEY="sk-xxx"' >> ~/.zshrc && source ~/.zshrc
Windows (PowerShell):
[Environment]::SetEnvironmentVariable("SILICONFLOW_API_KEY", "sk-xxx", "User")
或运行:local-rag setupChonkie RecursiveChunker 三级递归:
chunking.strategy: generic配置文件路径(跨平台自动选择):
~/Library/Application Support/local-rag/config.yaml%LOCALAPPDATA%\local-rag\config.yaml~/.local/share/local-rag/config.yaml环境变量优先:SILICONFLOW_API_KEY / OPENAI_API_KEY / RAG_DATA_DIR / RAG_EMBEDDING_MODEL
默认路径(跨平台自动选择):
~/Library/Application Support/local-rag/%LOCALAPPDATA%\local-rag\~/.local/share/local-rag/可通过环境变量 RAG_DATA_DIR 或 config.yaml 的 storage.data_dir 修改。
qwen3-embedding:4blocal-rag setup 或手动设置 SILICONFLOW_API_KEY 环境变量local-rag info 检查配置是否就绪local-rag info 报错"API Key 未设置",直接引导用户(见上方"API Key 查找顺序"),不要到处 greppip install -r ~/.claude/skills/local-rag/requirements.txt,如果命令报 ImportError,引导用户安装RAG_EMBEDDING_MODEL 可切换模型list_collections() 返回 Collection 对象,需要 .name/api/embed 返回 {"embeddings": [[...]]} 不是 {"embedding": [...]}© nigo81, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 17 other files in local-rag of nigo81/nigo-skills.
Open the folder on GitHubat commit 6468211
Local RAG next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Local RAG this skillnigo81/nigo-skills | 133 | — | ~1.4k | Automated safety check: Pass | MIT | |
| Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Knowledgeguaardvark/guaardvark | 257 | — | ~642 | Automated safety check: Pass | MIT | |
| Embeddings via 9Routerdecolua/9router | 31k | — | ~604 | Automated safety check: Pass | MIT | |
| Perfupraullenchai/Rapid-MLX | 4k | — | ~1.6k | Automated safety check: Notes | Custom licence | |
| AI SDK Developmenttrypostit/trypost | 691 | 1 repos | ~3.5k | Automated safety check: Pass | MIT |
Orchestra-Research/AI-Research-SKILLs
Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.
guaardvark/guaardvark
Answer from the user's own indexed documents with Guaardvark's local RAG, browse and read indexed files section by section, remember facts across sessions, and process or fetch new material.
decolua/9router
Generates vector embeddings through the 9Router /v1/embeddings endpoint, using models from providers such as OpenAI, Gemini, Mistral and Voyage for RAG and semantic search.
raullenchai/Rapid-MLX
Autonomous performance optimization: research, PoC, benchmark, implement, review, PR
trypostit/trypost
TRIGGER when working with ai-sdk which is Laravel official first-party AI SDK.
ddalcu/mlx-serve
Hook an app, game or script up to the local mlx-serve server for LLM chat, embeddings, image, speech, music, sound effect, video and 3D generation, and Laya/Kev/Clef typed decisions.
nigo81/nigo-skills
Looks up Chinese company registration data in the CICPA industry knowledge base, from a quick search to a full 61-dimension export, subsidiary discovery and Excel output.
nigo81/nigo-skills
Checks audit reports (financial statements and notes) for cross-reference, summation and text errors, with arithmetic done by script and results exported to Excel and Markdown.
nigo81/nigo-skills
Merges bank statements from several banks and formats into one standard Excel layout, then reconciles them against the general ledger with a layered matching engine.
nigo81/nigo-skills
A Chinese-language persona that answers accounting-standards and audit questions in the style of forum moderator Chen Yiwei, checking standards text before it replies.
nigo81/nigo-skills
Chinese-language tool that turns a business process description into a swimlane flowchart for internal control and audit work, exported as .drawio, PNG, SVG or VSDX.
nigo81/nigo-skills
Cross-checks a client's own registry data against its customers' and suppliers' to surface undisclosed related-party relationships for an audit.
Works with
Categories
本地向量知识库,支持按项目管理文档(docx/doc/pdf/md),语义检索。默认用硅基流动免费 API,零模型安装即可使用。支持多项目隔离、中文制度文档专用切片、Embedding+Rerank 两阶段检索。触发词:知识库、向量检索、RAG、制度检索、文档入库、语义搜索、local…. Local RAG is an agent skill from nigo81/nigo-skills.
Local RAG fits situations like: tasks that involve Retrieval-augmented generation; tasks that involve Embeddings; tasks that involve Word documents.
Run `npx skills add nigo81/nigo-skills --skill local-rag -a claude-code`. Or copy the skill folder (local-rag in nigo81/nigo-skills) into .claude/skills/local-rag in your project. Claude Code loads it when a task matches its description.
Run `npx skills add nigo81/nigo-skills --skill local-rag -a codex`. Or copy the skill folder (local-rag in nigo81/nigo-skills) into .agents/skills/local-rag in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add nigo81/nigo-skills --skill local-rag -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/local-rag, .gemini/skills/local-rag, .github/skills/local-rag and .opencode/skills/local-rag in your project.
Going by SKILL.md and its folder, Local RAG needs Python for the scripts in its folder, the command-line tools its instructions call (pip and python3) and credentials named SILICONFLOW_API_KEY and OPENAI_API_KEY. Our summary lists: Python 3; A credential in SILICONFLOW_API_KEY; A credential in OPENAI_API_KEY.
SKILL.md names 1 domain. As links in the text: cloud.siliconflow.cn. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Local RAG is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.4k tokens (SKILL.md is roughly 5.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Local RAG: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), Knowledge (guaardvark/guaardvark, 257 stars), Embeddings via 9Router (decolua/9router, 31k stars) and Perfup (raullenchai/Rapid-MLX, 4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
nigo81 (a GitHub user) maintains it in nigo81/nigo-skills, which has 133 GitHub stars. The repository holds 13 skills in this directory. The repository was last updated on August 23, 2026.
Source: nigo81/nigo-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.