Skill UI

Design task-local harnesses, evaluation gates, and reusable skill extraction for adaptive agent workflows. This skill helps build structured systems for one-off work, shared skills, and observable checkpoints. It defines input boundaries, tool bindings, and safety checks to ensure reliable automation without manual intervention.

A formal framework designed for evaluating AI-assisted code generation sessions. It implements Development Oriented Evals (EDD) principles, allowing users to define capability and regression criteria. The system supports multiple evaluation types—code-based, model-based, and human review—and tracks reliability using advanced metrics like pass@k, ensuring robust quality control for AI agents.

A sophisticated multi-agent development harness inspired by GANs. It separates product planning, code generation, and rigorous evaluation into three distinct agents (Planner, Generator, Evaluator). This creates an adversarial feedback loop, allowing projects to iteratively reach professional, production-grade quality far beyond what a single-agent workflow can achieve. Ideal for complex, full-stack applications.

A utility for scaffolding and verifying fixed evaluation corpora (bench suites). It generates deterministic test suites from existing repositories, decoupling the evaluation process from the repo's natural test suite. This ensures consistent fitness scoring when running evolution pipelines across different commits or forks.

This tool simplifies the process of detecting structural drift by combining audit listing, fresh auditing, and trend diffing into a single command. It finds the most recent audit record, runs a current audit against the repository, and compares the two using structural similarity metrics. It alerts when the structural distance falls below a specified threshold, helping maintain code consistency over time.

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.

Inspect and audit GEPA genomes via the @metaharness/darwin/gepa library. Load, validate, render system prompts, and analyze failure modes in run transcripts. Ideal for adopting evolved policies, CI gates, and debugging evolution runs.

Run a GEPA learning cycle via metaharness learn to optimize a harness genome against a SWE-bench-style slice manifest. Supports dry-run for cost estimation and --run for explicit execution. Requires a metaharness repo checkout.

This tool performs static security scanning of declared MCP surfaces using the `harness mcp-scan` command. It reads configuration files like `.mcp/servers.json` and `.harness/claims.json` without executing any tools. The tool flags policy, permission, and dependency issues, exiting with code 1 if findings meet the specified severity threshold. It supports CI integration and graceful degradation when the harness binary is unavailable.

A powerful tool designed to scaffold sophisticated, custom AI agent harnesses using the metaharness CLI. It supports various templates (e.g., coding, devops, marketing) and AI hosts (e.g., Claude, Codex). Crucially, it operates with maximum safety, defaulting to a dry-run mode and refusing to write to the calling repository root, ensuring controlled and predictable development workflows.

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.