Skill_Seekers and pg-aiguide

These are complements: Skill_Seekers converts diverse documentation sources into Claude skills that can be consumed by MCP servers like pg-aiguide, which specializes in making those skills effective for a specific domain (PostgreSQL).

Skill_Seekers
72
Verified
pg-aiguide
57
Established
Maintenance 20/25
Adoption 10/25
Maturity 22/25
Community 20/25
Maintenance 17/25
Adoption 10/25
Maturity 15/25
Community 15/25
Stars: 10,678
Forks: 1,056
Downloads:
Commits (30d): 49
Language: Python
License: MIT
Stars: 1,602
Forks: 79
Downloads:
Commits (30d): 9
Language: Python
License: Apache-2.0
No risk flags
No Package No Dependents

About Skill_Seekers

yusufkaraaslan/Skill_Seekers

Convert documentation websites, GitHub repositories, and PDFs into Claude AI skills with automatic conflict detection

Implements Model Context Protocol (MCP) for direct Claude integration and supports 17+ source types (docs sites, repos, PDFs, videos, notebooks, OpenAPI specs, wikis) with intelligent chunking that preserves code context. Exports unified knowledge assets to Claude Skills, RAG frameworks (LangChain, LlamaIndex, Haystack), vector databases, and coding assistants (Cursor, Windsurf, Cline) through a single preprocessing pipeline. Built on Python 3.10+ with 2,540+ tests and 24+ framework-specific presets for production-ready deployments.

About pg-aiguide

timescale/pg-aiguide

MCP server and Claude plugin for Postgres skills and documentation. Helps AI coding tools generate better PostgreSQL code.

Provides semantic search across version-aware PostgreSQL documentation and curated "skills"—production-tested patterns for constraints, indexing, and modern PG features—that integrate directly into AI agent workflows via HTTP MCP transport. Deploys as a public MCP server or Claude Code plugin, enabling AI assistants across Cursor, VS Code, and other IDEs to reference real PostgreSQL knowledge alongside TimescaleDB and emerging extension ecosystem docs during code generation.

Scores updated daily from GitHub, PyPI, and npm data. How scores work