Skill UI

Detects undocumented API endpoints by comparing traffic logs with OpenAPI specs, sweeping codebases, and using discovery tools so teams can inventory shadow APIs, validate monitoring rules, and shore up attack surface controls.

Use 42Crunch to run static API Audit and dynamic conformance scans on OpenAPI specs, integrating with IDEs and CI/CD pipelines to detect OWASP API Top 10 issues before deployment and maintain runtime compliance.

Automates RESTler-based stateful fuzzing of OpenAPI-driven REST services by compiling specs into grammars, configuring authentication, and running test/fuzz modes to expose dependencies, 500 errors, auth bypasses, leaks, and injection flaws.

Reverse-engineers frontend, backend, or fullstack codebases into detailed Product Requirements Documents by scanning routes, components, APIs, data models, and interactions to produce business-friendly specs that engineers or AI agents can use to rebuild every page and endpoint.

Turns messy workflows, docs, transcripts, or existing scripts into production-ready cross-platform agent skills by surfacing hidden requirements, generating specs, and validating exports for Claude Code, Copilot CLI, Cursor, and similar runtimes.

Context Engineering Protocol optimizes agent sessions by curating persistent rules, specs, source files, and feedback so outputs stay focused, accurate, and aligned with conventions whenever you start work or the quality drops.

Creates high-fidelity visuals through structured JSON prompts and a Python generator script, handling subjects, styles, references, and technical specs for characters, scenes, or products.

Explores Feishu/Lark native OpenAPI docs when existing lark-* skills or CLI commands lack coverage, fetching markdown indexes stepwise to surface raw API specs and ready-to-run `lark-cli api` invocations.

Transforms specs or vague assignments into ordered work units with dependency-aware sequencing, vertical slicing, checkpoints, and verification criteria so agents or teams can implement reliably without sprawling scope.

This skill creates high-quality videos from structured JSON prompts and optional reference images, then runs the prescribed Python script to produce MP4 output tailored to the requested scene, style, and technical specs.

A comprehensive workflow designed for Product Managers and technical leads to systematically identify, prioritize, and document missing features in a software project. It guides users through project understanding, gap analysis, applying a scoring matrix for prioritization, developing detailed Minimum Viable Product (MVP) specifications, and structuring the output as actionable development issues (e.g., GitHub Issues) with dependency mapping and workload optimization.

Maintains the repository's llms.txt guidance file by auditing current documentation, specs, and structure to ensure the navigation summary matches reality and stays compliant with the official specification.