Skill UI

Diagnoses full-stack failure modes in agent and LLM applications, including wrapper regressions, memory contamination, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings and code-first fixes for production agent systems.

This skill provides an end-to-end solution to transform existing REST APIs, defined by OpenAPI or Swagger specifications, into functional MCP servers. It maps every API operation to a dedicated, callable LLM tool, automatically generating robust parameter schemas (Zod) and handling authentication wiring. Use this to expose internal or third-party APIs to LLMs, providing a complete workflow from spec ingestion to local testing and cloud deployment.

This skill provides a direct, fast shell invocation to capture the current git diff (staged, working-tree, or specific file list) as raw text. It is designed to replace slower LLM agent steps that merely execute 'git diff' commands, significantly speeding up CI/CD workflows and meta-steps that require repository state analysis.

A lightweight tool designed to fetch data from a single URL via HTTP/HTTPS using various methods (GET, POST, PUT, DELETE). It serves as a fast, direct shell wrapper, ideal for replacing complex agent steps that only require simple data retrieval. Supports configurable timeouts and response body capping, making it perfect for quick data fetching in meta-skill workflows.

An internal, non-user-invocable agent used by the meta-skill creator process. It executes positive (G3) and negative (G4) smoke tests against a candidate meta-skill definition (SKILL.md). It simulates meta-resolution using LLM-generated fixtures and reports the results in a structured JSON format.

This skill provides a deterministic, git-only method to verify claims made by AI agents (e.g., 'done', 'shipped', 'fixed'). Instead of trusting the agent's self-report, it audits the commit's diff and git history using the DOS kernel (`dos verify`, `dos commit-audit`) to confirm if the claimed change genuinely occurred and is backed by non-forgeable evidence. It acts as a critical quality gate.

This skill generates comprehensive, academic-style survey papers as a single, self-contained HTML file. By reading an anchor resource and curating research data, it structures the content and utilizes advanced LLMs (like Kimi K2.6 via Fireworks) to generate all prose, figures, and bibliographies, suitable for rapid academic literature review.