with-context-agent
Lints, validates, and auto-fixes AI agent configuration files like SKILL.md, CLAUDE.md, and MCP configs.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
520 skills found
Lints, validates, and auto-fixes AI agent configuration files like SKILL.md, CLAUDE.md, and MCP configs.
Repository implementation guide for local-skills-mcp. Provides technical documentation on MCP tool handlers, skill loading, aggregation logic, and project structure for developers.
Comprehensive code quality validation for LibrAgent, covering TypeScript frontend and Rust/Tauri backend via automated linting, formatting, type checking, and build verification.
Master workflow controller for Lovable-style, AI-driven development. Instantly generates premium, multi-page, animated applications by routing to specialized sub-agents. No prompts needed—just build.
Apply reality-first coding standards: intentional naming, focused functions, guard clauses, and deterministic side effects, with no speculative features.
Completes development branches by verifying tests, managing merge or PR workflows, and cleaning up worktrees to ensure a consistent repository state.
Build and orchestrate end-to-end MLOps pipelines covering data preparation, training, validation, and automated deployment.
Search codebases efficiently using ripgrep for lightning-fast text patterns and ast-grep for precise, syntax-aware structural code analysis.
Master multi-agent orchestration with LangGraph. Build stateful, fault-tolerant AI workflows using supervisor-worker patterns, conditional routing, and advanced state management.
Systematically evaluate scholarly work using the ScholarEval framework, providing structured, quantitative, and qualitative assessment across research quality dimensions with actionable feedback.
A specialized code review agent that performs multi-dimensional analysis covering security vulnerabilities, performance optimization, code quality, and maintainability standards.
Unified API for LLM function calling and tool use across OpenAI, Anthropic, Google, and Ollama with standardized schema definitions and execution patterns.