Skill UI

This skill executes the DeepSWE benchmark, a comprehensive coding-agent evaluation suite. It uses OpenRouter and mini-swe-agent to score various Large Language Models (LLMs) on 113 tasks, providing reproducible results for model comparison, development assessment, and research benchmarking. Use this when a standardized, deep evaluation of LLM coding capabilities is required.

This framework guides AI agents to critically assess their own output across five key axes: Accuracy, Completeness, Clarity, Actionability, and Conciseness. It provides a structured, evidence-based scorecard and concrete suggestions for improvement, turning every task completion into a deep learning opportunity.

This skill scores a scoped competitor set into comparable profile cards using nine weighted dimensions including positioning, voice, and visual craft. It provides evidence-anchored rubrics from 1-5 and a tension 2x2 plot. Use this when benchmarking competitors, building comparison matrices, or grading rival positioning before assembling reports. It ensures consistency by requiring the same evidence to earn the same score across all competitors.

Compare two recorded browser sessions step-by-step using screenshots and DOM snapshots. Identifies visual regressions, verifies replay accuracy, and compares A/B variants. Reports divergence scores and accessibility changes for quality assurance.

Expertise in managing carrier portfolios, negotiating freight rates, tracking carrier performance, allocating freight, and maintaining strategic carrier relationships. Informed by a transportation manager with 15+ years of experience. Includes scorecarding frameworks, RFP processes, market intelligence, and compliance screening. Use when managing carriers, negotiating rates, evaluating carrier performance, or building freight strategies.

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.

Assemble scored competitor profiles into a decision-grade report featuring landscape maps, benchmarking matrices, and strategic recommendations. Ideal for presenting findings to leadership or boards, this skill structures competitive analysis pipelines to identify white-space opportunities and drive actionable business decisions based on auditable data.

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.

Run @metaharness/darwin evolve to mutate a harness's seven policy surfaces, sandbox-score each variant, and promote only measured wins. Use when readiness scores are flat and you need empirical configuration discovery.

A comprehensive 7-section report providing a deep assessment of a repository's health and readiness level. It calculates key metrics including risk score, agent topology recommendations, MCP surface area, test confidence, and publish readiness. This tool is vital for pre-mint reviews, detecting architectural drift, and enforcing strict CI/CD quality gates.

A comprehensive audit worker that integrates Open Infrastructure Architecture (OIA) manifest analysis, threat modeling, and MCP security scanning. It computes a composite worst-case severity score and persists time-stamped audit records in a memory namespace. Primarily designed for scheduled CI/CD pipelines to detect architectural drift and security regressions over time.