Skill UI

This skill helps developers create highly focused, folder-specific documentation (`CLAUDE.md` and `AGENTS.md`). It systematically analyzes all files in a target directory to extract project purpose, operational constraints, naming conventions, and critical decisions. This deep context ensures that future AI agents (like Claude or Codex) operate with localized knowledge, preventing them from relying on global, generalized instructions and improving their accuracy in that specific domain.

Provides deep semantic code intelligence for the Go language, utilizing the official `gopls` language server. Key features include precise go-to-definition, finding references, analyzing call graphs, performing safe refactoring (rename, extract, inline), generating diagnostics, and formatting code. It is essential for navigating, understanding dependencies, and maintaining large Go codebases.

This skill guides the safe, systematic, and at-scale refactoring of existing Go codebases. It ensures that structural changes maintain observable behavior by following a rigorous process: mapping blast radius, strengthening safety nets (tests), applying small tool-driven transforms, and committing changes atomically via staged PRs. Ideal for cleaning up large functions, resolving code smells, renaming packages, or tackling complex architectural improvements in Go.

Drafts high-engagement, long-form LinkedIn posts using 16 reverse-engineered hook formulas. Instead of writing randomly, the skill focuses on the desired outcome (comments, reposts, likes, or saves). It integrates the user's voice, adheres to modern formatting rules, and includes an AI-tell scrub pass to ensure the content reads authentically and humanly.

This skill acts as a critical reality-check layer before code generation. It ensures that every function call, import, method, and variable the agent uses is provably available in the user's specific installed environment and version. It prevents runtime bugs caused by hallucinated or improperly assumed APIs, making the generated code robust and reliable.

This tool manages the formal documentation of significant architectural decisions (ADRs). It automatically assigns sequential numbering, generates structured Markdown files (ADR-NNN), and registers the decision in the AgentDB graph. Use it when documenting technology choices, API designs, or data model changes to maintain a clear, traceable, and centralized history of system evolution.

A lightweight CLI tool for systematically comparing various coding AI agents (such as Claude Code, Aider, and Codex) head-to-head. It measures crucial metrics—pass rate, API cost, execution time, and consistency—on reproducible tasks defined via YAML manifests. Use this to objectively select the best agent for your codebase or to validate model updates before production deployment.

This comprehensive guide outlines best practices for building reliable and robust AI agent harnesses. It covers optimizing action spaces, defining structured tools, managing observation formats, and implementing essential error recovery contracts. Use these principles to dramatically improve agent completion rates, manage context budgets effectively, and transition AI prototypes into stable, production-grade autonomous systems.

This framework guides AI agents to critically assess their own output across five key axes: Accuracy, Completeness, Clarity, Actionability, and Conciseness. It provides a structured, evidence-based scorecard and concrete suggestions for improvement, turning every task completion into a deep learning opportunity.

A comprehensive tool for querying, storing, and managing complex knowledge across multiple agent sessions. AgentDB utilizes advanced techniques including semantic routing, hierarchical recall, causal graph construction, and context synthesis. Use this skill when your application requires robust, multi-faceted knowledge retrieval and reasoning based on structured and unstructured data. It provides a structured workflow for checking system health, starting sessions, storing knowledge in tiers (working, episodic, semantic), and searching learned patterns.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

Automatically generates comprehensive API documentation from existing TypeScript or JavaScript source code. It scans public exports, parses detailed JSDoc annotations (including parameters, returns, and throws), and can generate standardized OpenAPI 3.0 specifications for HTTP endpoints, ensuring robust and up-to-date API references.