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skill-builder

Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources.

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来源信息

仓库
yusufkaraaslan/Skill_Seekers
最近来源活动
2026年7月29日 08:17
检测到的 SKILL.md 语言
英语
星标
15,017
分支
1,533

安装方式

默认使用会先检查来源的 Prompt;你也可以切换为直接命令,或下载本地副本。

检查来源文件

决定是否安装前,请先阅读 SKILL.md,以及 SkillsMP 当前展示的配套文件。

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SKILL.md
来源说明 · 只读预览
name
skill-builder
description
Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources.
# Skill Builder This skill uses the Skill Seekers MCP server, which provides 40 tools for converting knowledge sources into AI-ready skills. If the MCP tools are not available, use the CLI fallback at the bottom of this file instead — do not stop. ## Prerequisites The MCP tools below only work when the Skill Seekers MCP server is connected: 1. Install the package: `pip install "skill-seekers[mcp]"` 2. Connect the server: - Installed as the Skill Seekers plugin? Nothing to do — the plugin's bundled `.mcp.json` starts the server automatically (it still needs step 1). - Installed standalone (e.g. copied into `~/.claude/skills/`)? Register the server once: `claude mcp add skill-seekers -- python -m skill_seekers.mcp.server_fastmcp` If tools like `scrape_docs` or `package_skill` are not in your tool list, the server is not connected. Tell the user about the two steps above, and use the CLI fallback in the meantime. ## When to Use This Skill Use this skill when the user: - Wants to create an AI skill from a documentation site, GitHub repo, PDF, video, or other source - Needs to convert documentation into a format suitable for LLM consumption - Wants to update or sync existing skills with their source documentation - Needs to export skills to vector databases (Weaviate, Chroma, FAISS, Qdrant) - Asks about scraping, converting, or packaging documentation for AI ## Source Type Detection Automatically detect the source type from user input: | Input Pattern | Source Type | Tool to Use | |---------------|-------------|-------------| | `https://...` (not GitHub/YouTube) | Documentation | `scrape_docs` | | `owner/repo` or `github.com/...` | GitHub | `scrape_github` | | `*.pdf` | PDF | `scrape_pdf` | | YouTube/Vimeo URL or video file | Video | `scrape_video` | | Local directory path | Codebase | `scrape_codebase` | | `*.ipynb`, `*.html`, `*.yaml` (OpenAPI), `*.adoc`, `*.pptx`, `*.rss`, `*.1`-`.8` | Various | `scrape_generic` | | JSON config file | Unified | Use config with `scrape_docs` | ## Recommended Workflow 1. **Detect source type** from the user's input 2. **Generate or fetch config** using `generate_config` or `fetch_config` if needed 3. **Estimate scope** with `estimate_pages` for documentation sites 4. **Scrape the source** using the appropriate scraping tool 5. **Enhance** with `enhance_skill` if the user wants AI-powered improvements 6. **Package** with `package_skill` for the target platform 7. **Export to vector DB** if requested using `export_to_*` tools ## Available MCP Tools ### Config Management - `generate_config` — Generate a scraping config from a URL - `list_configs` — List available preset configs - `validate_config` — Validate a config file ### Scraping (use based on source type) - `scrape_docs` — Documentation sites - `scrape_github` — GitHub repositories - `scrape_pdf` — PDF files - `scrape_video` — Video transcripts - `scrape_codebase` — Local code analysis - `scrape_generic` — Jupyter, HTML, OpenAPI, AsciiDoc, PPTX, RSS, manpage, Confluence, Notion, chat ### Post-processing - `enhance_skill` — AI-powered skill enhancement - `package_skill` — Package for target platform - `upload_skill` — Upload to platform API - `install_skill` — End-to-end install workflow ### Advanced - `detect_patterns` — Design pattern detection in code - `extract_test_examples` — Extract usage examples from tests - `build_how_to_guides` — Generate how-to guides from tests - `split_config` — Split large configs into focused skills - `export_to_weaviate`, `export_to_chroma`, `export_to_faiss`, `export_to_qdrant` — Vector DB export ## CLI Fallback (MCP server not connected) The same pipeline is available from the command line (requires `pip install skill-seekers`). Run it with the Bash tool: ```bash skill-seekers create <source> # auto-detects: URL, owner/repo, ./path, file.pdf, video URL, ... skill-seekers package <skill_dir> --target claude # or gemini/openai/langchain/chroma/... ``` `create` covers detection, scraping, and building in one step; add `--enhance-level 0` to skip AI enhancement. After it finishes, read the generated `SKILL.md` and summarize what was created.
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