AI Agent Skills

A Deep Thinking RAG Pipeline to Solve Complex Queries

Lightpanda browser, drop-in replacement for Chrome-based browsing in any AI agent - faster and lighter for tasks without graphical rendering like data retrieval. Use it…

Open-source generalized AI agent for everyday task automations.

Smoothly Manage Multiple LLMs (OpenAI, Anthropic, Azure) and Image Models (Dall-E, SDXL), Speed Up Responses, and Ensure Non-Stop Reliability.

LLM-powered injection of project context into installed agent templates via `aspens customize agents`

Context health analysis — freshness, domain coverage, hub surfacing, drift detection, LLM-powered interpretation, and auto-repair for generated agent context

LLM-powered generation pipeline for Claude Code skills and AGENTS.md — doc-init command, prompt system, context building, and output parsing

A robust methodology for LLM-based requirements gathering and high-fidelity artifact generation. Employs 'Divide, Conquer, and Verify' tactics using specialized subagent…

The single source of truth for how TFW commands survey their runtime, choose an execution mode (teams / subagents / sequential), route models, and run the TOH LOOP — the…

MindSQL: A Python Text-to-SQL RAG Library simplifying database interactions. Seamlessly integrates with PostgreSQL, MySQL, SQLite, Snowflake, and BigQuery. Powered by GP…

Advanced document extraction and chunking techniques for retrieval augmented generation that is aware of the layout of documents. Increases knowledge retrieval accuracy…

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building M…

AI Agent Work shop include Agent from Corepython, using CrewAI and Using SmolAgent

A python library for creating AI assistants with Vectara, using Agentic RAG

Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when desig…

Chooses among AI/ML approaches: classical ML, LLM, RAG, fine-tuning, agents, multimodal, embeddings/recsys, dense/MoE/SSM/diffusion. Use when picking or scaling an archi…