Skill UI

Code Health MCP provides structural maintainability feedback and design-level health scores, going beyond standard linting. It is crucial for AI-assisted coding workflows, allowing users to review code quality, verify score deltas after changes, and gate commits or pull requests to prevent codebase regression. Use it when refactoring, updating legacy modules, or ensuring the integrity of large diffs.

This tool manages isolated profiles for Codex CLI and Codex Desktop, ensuring that different accounts, projects, and local states do not interfere with each other. It provides dedicated CODEX_HOME directories, allowing users to securely switch contexts (e.g., work, personal, client) on a single machine without manual environment variable management.

This skill guides the definition of a defensible and decision-relevant competitor set. It is the foundational step before any benchmarking, helping users identify, categorize, and scope competitors (Direct, Adjacent, Aspirational) based on criteria like size, niche, market reach, and positioning. It ensures that strategic analysis focuses only on the most relevant rivals.

This skill assembles scored competitor profiles and market findings into a comprehensive, decision-grade report. It guides the user to answer critical client questions: who to compete with, how to differentiate, and where the defensible white-space is. Ideal for board presentations, strategic planning, and creating highly defensible client deliverables.

This utility provides garbage collection for Claude Code configurations. It periodically scans various directories—including skills, memory files, hooks, permissions, and caches—to identify orphaned, stale, or redundant configuration items. Crucially, the process is designed to be non-destructive, always requiring explicit human confirmation for every deletion to ensure a safe, audited cleanup process.

An advanced system that analyzes coding sessions via hooks to extract atomic, confidence-scored behaviors called "instincts." It tracks patterns, allowing users to evolve these instincts into comprehensive skills, commands, or agents. V2.1 introduces project-scoped learning, preventing knowledge contamination between different codebases and improving development workflow efficiency.

This tool performs robust outlier detection on individual session spending. Utilizing Median and Median Absolute Deviation (MAD), it calculates a modified z-score, making it highly resistant to extreme values, unlike mean+sigma methods. It specifically identifies which singular sessions are anomalous, making it crucial for cost monitoring, auditing, and integrating into CI/CD pipelines to prevent unexpected resource overruns.

A high-performance utility for executing simple, deterministic code transformations (e.g., removing console logs, converting var to const). This engine leverages a WASM core to perform structural refactoring sub-millisecond, without calling any expensive LLM APIs. Ideal for CI/CD pipelines, automated bulk refactoring, and cost-sensitive development workflows where determinism and speed are paramount.

This skill monitors operational costs against defined budgets by reading accumulated spending and computing utilization. It emits a detailed 4-tier alert ladder (INFO, WARNING, CRITICAL, HARD_STOP) based on predefined thresholds (50%, 75%, 90%, 100%). It is essential for cost governance, ensuring resources are managed within budget limits, and preventing system overspending.

Monitors the acceleration trend of production spending (burn rate) over defined time windows. It compares the latest spending bucket against the historical average, providing an early warning system. This is crucial for detecting sudden, unexpected cost spikes, such as a 'hot loop,' independent of overall budget limits.

This skill wraps the `getCompactContext` functionality to efficiently retrieve a compacted ReasoningBank context, specifically optimized for cost-analysis queries. It generates a detailed report summarizing critical metrics such as tokens saved, memories retrieved, and agentic-flow availability. It is essential for monitoring and optimizing resource usage in complex AI workflows, providing deep visibility into the efficiency and cost of the context retrieval mechanism.

This tool detects cost regressions by comparing two cost-summary JSON snapshots (baseline vs. current). It is essential for PR-gate workflows, checking for unexpected spending increases using three orthogonal signals: total percentage change, absolute dollar increase, and disproportionate growth in specific token classes (e.g., cache writes).