Skill Builder

---
name: skill-builder
description: Use when 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.
domain: core
author: oyi77
license: Apache-2.0
subdomain: core-platform
tags:
- ai-infrastructure
- automation
- parsing
- scraping
- rag
version: 1.0.0
category: core
---


# Skill Builder

You have access to the Skill Seekers MCP server which provides 40 tools for converting knowledge sources into AI-ready skills.

## Overview

Build new agent skills with proper structure, triggers, and verification.

## When NOT to Use

- You need to edit an existing skill (use the skill's own editing workflow)
- The source is a conversational transcript with no clear structure (use manual writing)
- You're looking for pre-built skills (use `skill_view` or search the 1ai-skills library)

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "I'll figure it out as I go" | A structured approach saves time and reduces errors. Follow the workflow in this skill rather than improvising. |
| "I already know this topic" | Familiarity breeds shortcuts. Use the checklist to verify you haven't missed critical steps. |
| "This doesn't apply to my situation" | The patterns here generalize across contexts. Adapt, don't skip — the underlying principles hold. |
| "One more tool will fix it" | Adding complexity rarely solves process gaps. Master the core workflow first. |

## When to Use

**Trigger phrases:**
- "skill builder"
- "Wants to create an AI skill from a documentation site, GitHub repo, PDF, video,"
- "Needs to convert documentation into a format suitable for LLM consumption"
- "Wants to update or sync existing skills with their source documentation"


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

## Commands

```bash
# Detect source type from input
#   URL → scrape_docs
#   owner/repo → scrape_github
#   file.pdf → scrape_pdf
#   video URL → scrape_video

# Basic workflow pipeline
python3 -c "
from skill_seekers import scrape_docs, enhance_skill, package_skill
# Step 1: Scrape
config = scrape_docs('https://example.com/docs', output_dir='./scraped')
# Step 2: Enhance
enhanced = enhance_skill(config, target_platform='hermes')
# Step 3: Package
package_skill(enhanced, format='SKILL.md', output='./output/')
"
```

## Dependencies

- Python 3.10+
- `skill-seekers` package (tools for scraping and packaging)
- Internet access for documentation scraping
- API keys for vector DB exports (optional)

## Process

1. **Design** — Define interface, identify patterns, plan implementation
1. **Implement** — Write code following existing conventions, add tests
1. **Verify** — Run tests, check integration, validate behavior

## Verification

- [ ] Skill has SKILL.md with frontmatter
- [ ] Has ## When to Use with triggers
- [ ] Has ## Process section
- [ ] Has ## Verification checklist
- [ ] Lint passes with 0 errors