Agent Skills Hub

vitest

Vitest testing patterns for reliable unit and integration tests. Focuses on critical business logic, edge cases, and mocking strategies for high-impact functions.

quality

Expert code reviewer for Rust projects. Performs comprehensive quality, security, performance, and architectural analysis using Bazel and project-specific conventions.

context-driven-testing

Apply context-driven testing principles to adapt testing strategies based on project goals, risks, and constraints rather than relying on universal best practices.

engineering-as-marketing

Implement the 'Engineering as Marketing' growth strategy: build free SEO-driven utility tools to drive organic traffic, capture leads, and convert visitors into customers without ad spend.

skill-template

A standardized template for creating and documenting modular Agent Skills to ensure consistent, efficient context engineering across AI agent systems.

code-quality

Enforce strict code quality, correctness, and Rust design patterns for the Turso database, prioritizing data integrity, performance, and maintainable, idiomatic code.

peer-review

Structured manuscript and grant review assistant utilizing checklist-based evaluation for methodology, statistical validity, and compliance with reporting standards like CONSORT and STROBE.

tools

Unified API for LLM function calling and tool use across OpenAI, Anthropic, Google, and Ollama with standardized schema definitions and execution patterns.

agent-architecture

Implement production-grade AI agents with LangGraph, tool-calling guardrails, SSE streaming, and episodic memory. Includes anti-patterns, fix pairs, and stateful architecture patterns.

claude-copilot

Analyze markdown documentation files to ensure compliance with predefined AI token budgets and optimize content for efficient AI ingestion.

ci

Automated CI/CD incident response, failure analysis, and remediation for GitHub Actions pipelines. Resolves build and test failures with safety guardrails.

rag-implementation

Build production-grade RAG systems using vector databases, semantic search, and LangGraph to ground LLMs in external knowledge.