Skill UI

This tool provides an end-to-end workflow for managing massive codebase migrations, framework upgrades, and structural refactors across hundreds of files. It uses a specialized CLI to orchestrate issue tracking, create reviewable PR batches, and verify CI status, ensuring complex changes are implemented safely and incrementally.

This meta-skill runs three independent code reviewers—safety, test coverage, and style—in parallel over the current uncommitted diff. It then arbitrates a single, authoritative verdict (BLOCK, BLOCK_WITH_OVERRIDE, or PASS_WITH_NOTES) based on strict priority rules, offering a comprehensive second opinion before committing code.

This meta-skill implements a combinator pattern for deep security analysis. It runs three independent gates—policy governance, secret scanning, and audit logging—over a candidate operation. It enforces a strict arbitration rule (Policy DENY > Scanner WARN > ALLOW) to provide a robust, multi-layered verdict, making it ideal for reviewing critical code changes or environment modifications for safety compliance.

Use the Composio CLI to automate the entire Pull Request review and Continuous Integration (CI) triage process for GitHub and GitLab. This tool fetches code diffs, analyzes failure logs, posts detailed inline review comments, and iteratively patches and re-runs CI checks until the code passes, significantly enhancing code quality and developer efficiency.

A generic entry point for delegating self-contained, multi-step tasks to specialized LLM agents (Codex, Claude Code, Pi). Utilizes background processing tools to manage the full lifecycle of AI-driven sub-tasks, such as policy review, complex report synthesis, document generation, or large-scale code refactoring. Ideal for meta-skill workflows requiring full agent agency.

This workflow automates the AWS Well-Architected Framework (WAF) review. It scans Infrastructure as Code (IaC) files (Terraform, CloudFormation, CDK) and deployed AWS resources against 6 pillars (Security, Reliability, etc.). It identifies risks, generates detailed findings, and automatically creates trackable GitHub issues for remediation, ensuring adherence to industry best practices.

This skill maps engineering changes (code diffs, architecture designs, IaC) to specific financial compliance controls, such as PCI-DSS v4.0 and MAS TRM. It generates an auditable report detailing compliance gaps, required evidence, and concrete remediation steps, helping teams catch violations before formal audits.

Luna serves as the final quality gate, rigorously reviewing code for objective correctness, security vulnerabilities, and overall reliability. It checks for critical flaws such as injection vulnerabilities, authorization bypasses, race conditions, and adherence to the architectural blueprint. It ensures that only high-quality, safe code progresses to QA and deployment.

This skill enforces a high standard for all generated code, prioritizing correctness, clarity, and idiomatic patterns across all languages. It helps minimize code bloat by removing unnecessary abstractions and verbose patterns, while also optimizing the agent's output by using targeted diffs and eliminating excessive conversational overhead. Use this for all code generation, refactoring, or review tasks.

Implements a robust, four-stage coding discipline (Plan, Isolate, Test First, Double Review) to prevent premature code delivery. This skill forces the AI to structure complex tasks by planning, implementing in isolation, writing tests first, and performing deep self-review, significantly improving code correctness and quality.

This skill generates a highly customized code review checklist for any pull request. Instead of using a generic template, it adapts the review depth and specific checks based on the target language (e.g., Python, TypeScript), the nature of the change (e.g., bug fix, refactor), and the associated risk level (low, medium, high, critical). It ensures comprehensive coverage, guiding reviewers to focus on critical areas like security, correctness, and architectural integrity.

This tool conducts structured reviews of various Infrastructure-as-Code (IaC) tools (e.g., Terraform, Pulumi) across multiple cloud providers. It generates an actionable report that categorizes findings by severity (Critical, High, Medium, Low), providing detailed remediation steps and a reusable best-practice checklist for continuous security, compliance, and operational auditing.