skill-builder

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:

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.