Skill UI

Turns your Kanban board (Asana, GitHub Projects, Linear) into an autonomous AI development pipeline with multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, cost tracking, and recoverable task state.

Transforms generic AI-generated frontend prototypes into bespoke, production-ready interfaces using curated design tokens, banned anti-patterns, and quality gates.

Quality-gate review tool for reverse-engineering or assessment case packages. Verifies scope readiness, Evidence-to-Finding-to-Path traceability, work-item coverage, timeline consistency, and artifact hashes before report delivery. Ensures defensible handoff for security analysis, forensics, CTF, or authorized security cases without modifying targets. Supports Markdown or JSON output formats for CI integration.

This skill helps transform code reviews from simple gatekeeping into a powerful knowledge-sharing mechanism. It guides users through systematic analysis of code changes, focusing on correctness, security, performance, and maintainability. Use it when reviewing pull requests, establishing best practices, or mentoring developers to ensure high-quality, robust codebase development.

This skill allows you to delegate bounded coding tasks to the GitHub Copilot CLI while retaining review and landing responsibility. You write a brief, dispatch the task via a relay script, wait for completion, review the generated diff against your requirements, and finally commit the changes. It requires the Copilot CLI installed and authenticated, along with Node.js and Git. This approach ensures human oversight in the coding process, preventing silent failures and maintaining control over code quality.

A specialized quality gate skill designed to review complex, multi-source outputs generated by the CrossFrame framework. It rigorously checks reasoning fidelity, evidence boundaries, source anchors, and structural continuity. Use this when advanced verification of logical rigor and source integrity is required, ensuring outputs meet strict internal protocols and avoid common structural failures.

This skill guides users on setting up automated code quality gates directly at the Git level. It covers best practices using frameworks like Husky, lint-staged, and pre-commit. Users can enforce linting, formatting, type-checking, and standardized commit message conventions (e.g., Conventional Commits) before code is committed or pushed, significantly improving code hygiene and development workflow consistency.

Luna is an automated code reviewer focused on correctness, security, and reliability. She evaluates code against blueprints and checklists, identifying critical vulnerabilities like injection flaws, authentication bypasses, and race conditions. Luna acts as a quality gate, ensuring no unresolved high-risk issues proceed to QA or deployment. She excludes style preferences, focusing only on measurable risks to maintainability and safety.

Investigate and remediate data quality alerts from Monte Carlo. Performs root cause analysis, blast radius assessment, tool discovery, and executes fixes or escalates with full context when uncertain.

A robust, production-tested pattern for coordinating multiple specialized AI agents. The orchestrator decomposes complex tasks, routes them to the most suitable specialist, prevents redundant or conflicting work (anti-duplication), and enforces mandatory quality gates (e.g., running tests, checking file diffs) to verify results before marking a task as complete. It also includes heartbeat monitoring for task progress.

RouterBase acts as a unified, OpenAI-compatible API gateway, allowing applications to route requests across diverse large language models (LLMs) like GPT, Claude, and Gemini. It helps developers migrate existing OpenAI SDK calls, implement sophisticated primary/fallback model selection based on cost, latency, and quality, and manage multimodal payloads (image, audio, video) from a single, standardized endpoint.

This skill covers the setup and configuration of Static Application Security Testing (SAST) tools like Semgrep, SonarQube, and CodeQL. It guides users on creating custom security rules, integrating scans into CI/CD pipelines, and managing quality gates. The process includes optimizing scan performance, reducing false positives, and ensuring compliance with security policies across multiple programming languages.