Skill UI

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.

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.

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.

Turn a raw product idea into an evidence-backed build blueprint with researched stack, epics, Given/When/Then criteria, and tests, enabling coding agents to build one epic per session.

Standardizes how coding agents snapshot active state before /clear, /compact, or task switches. Produces a verified handoff document covering goals, decisions, files touched, open loops with owners, and a single next action.

Ten rules that keep coding agents from re-sending bloated context on every call: locate before reading, compute instead of dumping data, bound noisy command output, never re-read, batch parallel calls and answer lean. Cuts token cost ~32% with unchanged pass rates.

A perfectionist mode for focused improvement on specific UI details or performance targets. Runs bounded attempts to find the best version, stops on budget. Works with multiple AI coding agents for targeted refinement and verification.