skill-builder

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.
domain	core
author	oyi77
license	Apache-2.0
subdomain	core-platform
tags	["ai-infrastructure","automation","parsing","scraping","rag"]
version	1.0.0

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

# 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
2. Implement — Write code following existing conventions, add tests
3. 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