Skill UI

This skill implements advanced 'Gatekeeper' logic to optimize AI agent performance in long-running workflows. It prevents context window bloat by sharding metadata, allocating token budgets, and enforcing Atomic Precision to deliver purely functional, filler-free responses. Ideal for complex coding, multi-step reasoning, and large document analysis where conciseness is critical.

This skill creates custom Semgrep rules for detecting security vulnerabilities, bug patterns, and code patterns. It guides users through a strict workflow including test-first development, AST analysis, and validation. Ideal for building static analysis detections, enforcing coding standards, and identifying data flow issues using taint mode.

This tool audits the activation metadata of coding skills used by AI agents. It grades skills (A-F) based on description clarity, simulates which skill a prompt will trigger, and identifies metadata collisions where one skill might silently overshadow another. It is essential for ensuring robust, predictable, and reliable AI agent behavior during development and CI processes.

Squirrel is an advanced, full-cycle AI coding skill designed to guide software development from concept to production. It automatically detects the current project state (greenfield, in-progress, or mature) and adapts an 8-phase engineering pipeline. This comprehensive workflow covers planning, building, rigorous testing, bug hunting, code polishing, documentation, and deployment readiness, ensuring high-quality, maintainable results.

This skill provides comprehensive guidelines for writing idiomatic and efficient Swift code. It covers best practices for optionals, collections, value types, error handling, concurrency, and protocol-oriented design to improve code quality and maintainability.

This skill provides a robust system for tracking all code modifications using structured JSON records and enforcing a defined state machine (e.g., planned -> deployed). It is critical for AI coding workflows, ensuring that if a session expires or is abandoned, the next session receives the full context, progress summary, and blockers to resume seamlessly. Ideal for complex, multi-stage development projects and onboarding new codebases.

Master Temporal workflow orchestration using the Python SDK. Covers durable workflows, saga patterns, and distributed transactions. Includes guidance on async/await execution models, worker configuration, activity implementation, error handling, retry policies, and production deployment strategies. Ideal for building reliable, scalable distributed systems with long-running processes and deterministic coding practices.

TextMe is a local daemon that transforms your smartphone into a powerful remote control for Claude Code sessions. By bridging inbound iMessages (from whitelisted numbers) through services like Sendblue, users can interact with their AI session on their laptop, executing code, navigating filesystems, sending voice notes, and receiving real-time updates. Ideal for supervising headless workstations or coding while away from your desk.

Detects which APIs of a Python project's pinned dependencies changed after a coding model's training cutoff, locates the code using them, and writes short evidence-tagged notes into AGENTS.md or CLAUDE.md.

Comprehensive guidelines for writing idiomatic and efficient TypeScript and JavaScript code. Covers array operations, destructuring, async patterns, type safety, React hooks, and common anti-patterns to improve code quality and maintainability.

LWC is a durable memory system for coding agents that preserves project decisions, code structure, and verified context across sessions. It utilizes a physical Wiki document graph and a CodeGraph index to enable source-grounded recall. This tool helps agents search, update, and maintain knowledge without losing critical information during future coding-agent interactions.

Detects data exfiltration via URL path encoding and chained web_fetch navigation, covering fake trusted UI injection, indirect prompt injection, letter-level path exfiltration, and multi-hop navigation hijacking in agent memory/context scenarios.