Skill_Seekers

Extracts content from documentation websites, GitHub repositories, and PDFs through a five-phase pipeline (scrape → parse → analyze → enhance → package) that normalizes heterogeneous sources into a unified intermediate representation. Uses BFS traversal for HTML scraping, GitHub API with fallback local mode for large repos, and OCR for PDF text extraction, with automatic language detection and code block categorization across all sources.

Detects and resolves conflicts when merging content from multiple sources (e.g., same API documented in both GitHub README and official docs site) using configurable synthesis strategies and formulas. Implements conflict scoring based on content similarity, source authority, and freshness, then applies user-defined resolution rules (prefer newest, prefer authoritative source, merge with deduplication, etc.) to produce a single canonical skill.

Extracts text and structured content from PDF files using OCR (optical character recognition) for scanned documents and native text extraction for digital PDFs. Handles embedded images, tables, and code blocks, preserving document structure and formatting. Supports large PDFs through streaming ingestion and page-by-page processing. Automatically detects and extracts code blocks from PDF content.

Automatically detects and processes llms.txt files in documentation websites (a standard for exposing machine-readable documentation metadata). Extracts structured content hints, API endpoints, and documentation structure from llms.txt, using this information to optimize scraping strategy and improve content extraction. Falls back to standard BFS scraping if llms.txt is not found.

Provides a unified command-line interface for all Skill Seekers operations (scraping, enhancement, distribution, workflow orchestration) with natural language workflow invocation through MCP integration. Supports workflow commands that chain multiple operations (e.g., scrape → enhance → package) in a single invocation. Implements argument parsing, validation, and help system for all commands.

Provides Docker containerization for Skill Seekers with pre-configured images for common use cases (scraping, enhancement, distribution). Includes Kubernetes deployment manifests and Helm charts for production-scale deployments. Integrates with GitHub Actions for automated skill generation workflows triggered by documentation changes. Supports CI/CD pipeline integration for continuous skill updates.

Analyzes local codebases using abstract syntax tree (AST) parsing to extract architectural patterns, design patterns, test examples, configuration patterns, and dependency graphs. Supports multiple languages (Python, JavaScript, Go, Rust, etc.) through language-specific parsers, generates ARCHITECTURE.md documentation, extracts how-to guides from test files, and detects signal flow in game engine code (Godot). Produces structured analysis output that enriches skill content with code-level insights.

Enhances raw scraped content through two pathways: local CLI-based enhancement using local LLM inference, or API-based enhancement using Claude/OpenAI APIs. Applies configurable enhancement presets (improve-clarity, add-examples, generate-summaries, etc.) to enrich skill content with better explanations, additional examples, and structured metadata. Supports streaming ingestion for large documents and checkpoint/resume for interrupted enhancement jobs.

Implements a FastMCP-based server that exposes Skill Seekers capabilities as MCP tools, enabling integration with Claude and other AI agents. Supports multi-agent orchestration with automatic setup/auto-configuration, natural language workflow invocation, and unified CLI commands for scraping, enhancement, and distribution. Agents can invoke scraping, enhancement, and skill packaging workflows through natural language prompts without direct CLI interaction.

Packages enhanced skills into platform-specific formats using a strategy pattern adaptor system. Supports distribution to Claude, Smithery registry, vector databases (for RAG), and custom platforms. Implements quality validation checks (completeness, accuracy, format compliance), chunking strategies for vector database export, and platform-specific metadata generation. Handles large documentation through router skills and hub architecture for modular skill distribution.

Provides a unified configuration schema for all Skill Seekers operations (scraping, enhancement, distribution) with JSON schema validation. Supports analysis presets (predefined configurations for common scenarios), config API service for programmatic configuration management, and private config repositories for team collaboration. Enables users to define custom configurations without code modification through declarative YAML/JSON files.

Implements multi-level caching (scrape cache, parse cache, analysis cache) and checkpoint/resume system enabling interrupted workflows to resume without reprocessing completed phases. Stores intermediate results in a structured cache directory, allowing rapid iteration on enhancement and distribution phases without re-scraping. Supports dry-run mode for testing configurations without side effects.

Implements intelligent rate limit management for GitHub API (60 req/hour unauthenticated, 5000 authenticated) with automatic backoff and retry logic. Handles large files and repositories through streaming ingestion, pagination, and file size detection. Provides rate limit status reporting and proactive warnings when approaching limits. Supports authenticated requests with token management for higher rate limits.

Automatically detects programming languages in code blocks and documentation using heuristic analysis and language-specific syntax patterns. Extracts code examples with context, categorizes them by language and purpose (example, test, configuration, etc.), and enriches skill content with language-tagged code snippets. Supports 40+ programming languages with fallback to generic code handling for unknown languages.