Skill: er6y/py-mnn-kb (version 1.0.0)

--- name: py_mnn_kb description: > Local vector knowledge base with GraphRAG retrieval (vector + BM25 + knowledge graph). Use this skill when the user mentions: "查知识库", "加入知识库", "记住这个", "save to KB", "add to knowledge base", "query knowledge base", "记录一下", or similar intent to store or retrieve private knowledge. license: MIT allowed-tools: - kb_build - kb_note - kb_query - kb_status disable: false --- # py_mnn_kb — MNN Knowledge Base Skill Local GraphRAG knowledge base backed by SQLite + MNN embeddings. Fully compatible with Android OfflineAI RAG database format. --- ## Setup ### 1. Install dependencies ```bash pip install -r requirements.txt ``` ### 2. Configure ```bash cp config.example.json config.json # Edit config.json: set llm_api.api_key, optionally change default_name ``` Key fields in `config.json`: | Field | Default | Description | |---|---|---| | `knowledge_base.default_name` | `default` | KB used when `--kb` is omitted | | `knowledge_base.storage_dir` | `assets/knowledge_bases` | Where DB files are stored | | `llm_api.api_key` | *(required for query+LLM)* | OpenAI-compatible API key | | `graph_ner.custom_dict_path` | `assets/example_terms.json` | Domain terminology for NER | ### 3. First run (auto-downloads embedding model) ```bash python scripts/py_mnn_kb.py status ``` On first use, `Qwen3-Embedding-0.6B-MNN-int4` (~400 MB) is auto-downloaded into `assets/`. --- ## Tools ### `kb_build` — Build / append knowledge base from files Indexes a directory of documents. **Runs in background; returns immediately.** Check progress with `kb_status`. **Parameters:** | Name | Type | Required | Description | |---|---|---|---| | `dir_path` | string | yes | Directory path to index (recursive) | | `kb_name` | string | no | KB name (default: value of `default_name` in config.json) | **Returns:** `{ status, command, kb_name, pid, files, message }` **Supported formats:** `.txt` `.md` `.pdf` `.docx` `.pptx` `.xlsx` `.csv` `.html` `.json` `.jsonl` **CLI:** ```bash python scripts/py_mnn_kb.py build ./my_docs/ --kb my_kb python scripts/py_mnn_kb.py build ./my_docs/ # uses default KB name ``` **Trigger phrases:** "加入知识库", "索引这个目录", "build KB", "index these files" --- ### `kb_note` — Insert a text note directly into the knowledge base Embeds and stores a free-form text snippet. **Synchronous. Refused while build is running.** **Parameters:** | Name | Type | Required | Description | |---|---|---|---| | `text` | string | yes | Text content to store | | `kb_name` | string | no | KB name (default: `default_name`) | | `title` | string | no | Optional title, stored as source label | **Returns:** `{ status, kb_name, chunks_added, elapsed_sec }` **CLI:** ```bash python scripts/py_mnn_kb.py note "Q1 roadmap: focus on modules A and B" --kb my_kb python scripts/py_mnn_kb.py note "$(cat meeting.txt)" --kb my_kb --title "Weekly meeting" ``` **Trigger phrases:** "记住这个", "记录一下", "加个笔记", "save this", "remember this" --- ### `kb_query` — Retrieve relevant chunks (RAG retrieval) Runs vector + BM25 + GraphRAG fusion and returns the top-N context string. **The agent appends this context to its prompt — no LLM call is made inside this tool.** **Synchronous. Refused while build is running.** **Parameters:** | Name | Type | Required | Description | |---|---|---|---| | `prompt` | string | yes | Query question or keywords | | `kb_name` | string | no | KB name (default: `default_name`) | **Returns:** Multi-document context string, e.g.: ``` Document1 [ID:42 source:manual.pdf]: Deployment has three steps... Document2 [ID:55 source:notes.md]: ... ``` **CLI:** ```bash python scripts/py_mnn_kb.py query "NAND筛选核心流程" --kb my_kb --no-llm python scripts/py_mnn_kb.py --output json query "产品路线图" --kb my_kb ``` **Agent usage pattern:** ``` context = kb_query("用户的问题", kb_name="my_kb") # Then: f"Based on the following context:\n{context}\n\nQuestion: {user_question}" ``` **Trigger phrases:** "查知识库", "查一下", "知识库里有没有", "search KB" --- ### `kb_status` — Check build progress or last build result **No KB initialization needed. Always returns instantly.** **Parameters:** | Name | Type | Required | Description | |---|---|---|---| | `kb_name` | string | no | (informational only, does not affect result) | **Returns:** - While building: `{ status: "building", progress: 0-100, message }` - After success: `{ status: "ok", message, stats: { chunks_added, elapsed_sec, ... } }` - After failure: `{ status: "error", error }` - Not yet run: `{ status: "idle", message }` **CLI:** ```bash python scripts/py_mnn_kb.py status ``` **Trigger phrases:** "构建进度", "build status", "知识库建好了吗" --- ## Workflow Examples **A · User uploads files → auto-index** ``` User: "把这些文档加入知识库" Agent → save files to temp dir → kb_build(dir_path=tmp_dir, kb_name="my_kb") # returns immediately → "已开始后台构建，用 kb_status 检查进度" ``` **B · User dictates a note → insert** ``` User: "记住：STAR2000 低温写性能提升 8%" Agent → kb_note(text="STAR2000 低温写性能提升 8%", kb_name="my_kb", title="技术发现") → "已保存到知识库 my_kb" ``` **C · User asks a question → KB-assisted answer** ``` User: "NAND 筛选核心流程是什么？" Agent → context = kb_query("NAND 筛选核心流程", kb_name="my_kb") → append context to LLM prompt → generate answer ``` **D · Check if build finished before querying** ``` Agent → st = kb_status() → if st["status"] == "building": tell user to wait → else: proceed with kb_query(...) ``` --- ## Notes - `kb_build` is **incremental append** — re-running on the same directory adds only new content - `kb_note` and `kb_query` are **blocked** (return `status: building`) while a build is running - `--output json` on any CLI command returns machine-parseable JSON on stdout - KB name `default` is used when `--kb` is omitted; configure `default_name` in `config.json`

path: skill.json
content: {
  "name": "py_mnn_kb",
  "display_name": "MNN Local Knowledge Base",
  "description": "Lightweight local knowledge base retrieval skill based on MNN inference. Offline, low power, privacy-first, no GPU required.",
  "author": "er6y",
  "version": "1.0.0",
  "language": "python",
  "type": "knowledge",
  "tags": [
    "mnn",
    "knowledge",
    "retrieval",
    "local",
    "offline",
    "embedding"
  ],
  "entry": "main:PyMNNKBSkill",
  "config": "config.example.json",
  "repo": "https://github.com/er6y/py_mnn_kb",
  "license": "MIT"
}


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)