Skill UI

This skill provides a systematic pipeline for safely executing large-scale code refactors across multiple files or subsystems. It first performs parallel, dependency-aware analysis to map the entire scope, synthesizing a single, actionable plan. The process then breaks the work into independent 'work packets,' which are executed sequentially or in parallel by specialized worker agents, ensuring strict dependency management and behavior parity throughout the entire process.

A specialized skill designed for agents to proactively identify, analyze, and strategize high-impact contributions within trending Open Source repositories. It systematically performs repository discovery, extracts targeted issues (like 'help wanted' or 'good first issue'), and conducts deep feasibility analysis (including mergeability and complexity). Finally, it generates a structured 'Contribution Dossier' with actionable plans, significantly accelerating the process of becoming a top-tier OSS contributor.

Coordinates multiple specialized agents via Claude Code's native Agent Tool for complex tasks requiring diverse expertise, such as comprehensive code analysis, feature reviews, and security audits. Enables sequential chains, context passing, and resumable work, with a synthesis protocol for unified recommendations.

Coordinate a bounded code review from multiple perspectives including correctness, authorization, and performance. This skill helps review defined diffs or subsystems without installing orchestration engines. It involves reading diffs, choosing relevant perspectives, recording findings, and resolving disagreements through code or tests. Suitable for performance testing reviews where multiple agents or sequential perspectives are authorized.

Register custom Pi Agent model slugs in models.json so saved OpenRouter routing variants and new model ids resolve instead of silently falling back to Pi's bundled per-provider default.

Orchestrate bounded coding tasks by delegating them to the Pi coding agent CLI, then independently review the diff, re-run project gates and commit the verified work yourself.

This blueprint guides the creation of F.R.I.D.A.Y., a tactical, low-latency voice assistant inspired by Iron Man. It utilizes the specialized Pipecat framework to orchestrate a comprehensive, real-time audio pipeline: STT (OpenAI Whisper/GPT-4o) -> LLM (Gemini) -> TTS (OpenAI Nova). Ideal for developers needing to build advanced, multi-provider, conversational voice applications.

Polis Protocol transforms static AI agent workflows into a self-improving, dynamic team environment. It uses a learning router to assign tasks based on an agent's historical track record and effectiveness on specific domains, rather than fixed roles. It is vendor-agnostic, allowing diverse agents (e.g., Claude, Gemini) to collaborate under a unified, auditable governance structure where agents can propose and vote on amending the protocol's core rules.

This skill systematically audits a given project by analyzing its historical memory, rollout summaries, and local skill definitions. It identifies repeated complex workflows, recurring operational patterns, or areas where existing skills are stale. The audit process guides the user to recommend the highest-value skills to either add (if a new pattern emerges) or update (if current skills are outdated or incomplete), ensuring the AI agent operates with the most accurate and context-specific knowledge base for optimal project execution.

This skill reviews git diffs or specific file scopes for code reuse, quality, efficiency, clarity, and standards compliance. It optionally applies safe, behavior-preserving fixes. It uses parallel read-only sub-agents to inspect code while the main agent applies changes. Suitable for auditing changes, simplifying code, or refactoring.

This skill enables parallel read-only multi-agent review of git diffs or specific file scopes. It identifies behavioral regressions, security risks, performance issues, and coverage gaps using four specialized sub-agents. The main agent synthesizes findings to filter and summarize critical issues without modifying code. Ideal for ensuring code quality and safety before merging changes.

Coordinate multi-layer security scanning and hardening across application, infrastructure, and compliance controls using defense-in-depth strategy. Orchestrates specialized security agents for comprehensive assessments, layered controls, and continuous monitoring. Follows DevSecOps principles with shift-left security, automated scanning, and compliance validation to create a resilient security posture addressing vulnerabilities and future threats.