Skill UI

This infrastructure provides a robust solution for running powerful AI agents in remote, sandboxed environments. It overcomes the limitations of local execution by ensuring unlimited concurrency, consistent environments, and collaborative features. Use this to build complex background coding agents, multi-client interfaces (e.g., Slack, Web), or scale agent workloads far beyond local machine constraints.

A deep expert system for Anthropic's Claude Code CLI. This skill maximizes productivity by managing advanced features like hooks, persistent context via CLAUDE.md, memory management, sub-agent orchestration, and integrating with local development ecosystems. Ideal for complex coding tasks, debugging, and advanced workflow automation.

A forensic tool for performing post-mortem analysis on AI-assisted coding sessions. It goes beyond simply stating what was done, determining *why* work deviated (e.g., spec ambiguity, repo friction, agent error), identifying scope creep, and classifying rework patterns. It provides evidence-backed recommendations to improve prompts, codebase health, and future development workflows.

A robust framework for orchestrating complex, multi-step coding tasks using multiple AI agents. It functions by first planning the entire project into a mission dependency graph (DAG), then dispatching specialized agents to isolated Git worktrees for parallel development. The system automatically monitors progress, handles merges, and generates structured, comprehensive reports detailing changes, completed tasks, and next steps.

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 skill acts as an anti-over-engineering guardrail, instructing AI coding agents to maintain strict scope control and minimalism. It guides the AI to only modify the files and lines explicitly requested by the user, avoiding unnecessary abstractions, unrequested refactors, or scope creep. Use it when you prefer the simplest, most direct solution and want to prevent the agent from 'grinding' the code base.

Blueprint turns a single objective into an executable, multi-step construction plan tailored for coding agents and multi-session teams, complete with context briefs, review gates, dependency awareness, and installable via `/blueprint <project> <objective>`.

This skill enforces a rigorous, 5-phase debugging protocol (Reproduce, Isolate, Root Cause, Fix, Verify) for AI coding agents. It forces a disciplined approach, preventing agents from guessing fixes or making premature edits. Use this when debugging complex, intermittent, or hard-to-isolate bugs to ensure the root cause is fully confirmed before any code changes are applied, maximizing debugging accuracy.

This tool assesses whether coding agents adhere to defined skills, rules, or agent definitions. It automatically generates scenarios with varying prompt strictness, executes the agent, and generates a comprehensive report detailing the compliance rate of tool calls against predefined workflow specifications using advanced LLM classification.

OpenViking Memory enables AI agents to recall and persist long-term semantic knowledge using MCP tools. It supports retrieving prior context at task start via find/search/read and storing durable facts, preferences, and lessons via remember. Ideal for coding, debugging, and multi-step workflows, ensuring continuity across sessions without automatic lifecycle hooks.

This comprehensive guide provides a detailed framework for authoring effective and structured SKILL.md files for AI coding agents. It covers best practices for structuring content, including frontmatter fields, defining natural language triggers, establishing core principles, and incorporating advanced elements like decision trees and verification checklists, ensuring agents receive clear, actionable, and consistent technical guidance.

This guide outlines the structure and content guidelines for an AGENTS.md file. This file serves as a comprehensive 'README for agents,' providing detailed, technical context and operational instructions (such as setup, development workflow, testing procedures, and code style) that enable advanced AI coding agents to interact with and understand a codebase effectively. It greatly complements traditional READMEs by focusing purely on machine-readable technical requirements.