Skill UI

This meta-skill automatically inspects open GitHub Pull Requests, failing CI runs, and new issues. It utilizes the GitHub API to gather critical updates and summarizes the findings into three actionable buckets (To Review, Awaiting Me, CI Red). Finally, it persists follow-up tasks to long-term memory, helping developers efficiently prioritize their daily workflow.

This meta-skill automates the complex process of modernizing legacy PDF documents (e.g., historical contracts, scanned manuals, legal documents). It follows a structured four-step pipeline: structural extraction, natural-language rewriting of problem pages, generating an audit summary, and finally re-merging the content into a single, refined PDF. This ensures content is modernized while maintaining fidelity to the original meaning, providing a human review summary before finalization.

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 powerful DI extension point for Website Builders, allowing developers to inject custom query parameters into all live preview URLs. This is essential for integrating complex features like signed tokens, tenant identifiers, or feature flags directly into the preview experience. It supports asynchronous operations, such as fetching remote authorization tokens, ensuring robust customization across the editor iframe and generated links.

This automated workflow performs a comprehensive review against the AWS Well-Architected Framework (WAF). It scans IaC definitions (Terraform, CloudFormation, CDK) and deployed resources to identify compliance gaps, security risks, and optimization opportunities across six core pillars. Findings are structured and automatically generated as actionable GitHub issues with remediation code examples.

Atlas Ledger acts as a crucial memory companion to atlas-contract. It automatically triggers when a deviation ('drift') is caught during audits or reviews. Its core function is to distill the observed error or mistake into structured, actionable IF/WHEN/DON'T/INSTEAD clauses. These clauses are written to a permanent ledger (Atlas.md), ensuring that subsequent interactions are governed by learned lessons, thereby strengthening the project's overall governance and reliability.

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 simulates a world-class Senior System Design Engineer with 20+ years of experience at top tech companies. It helps users design, review, and scale complex, highly available, and resilient distributed systems. It covers every aspect from database sharding and microservices decomposition to caching strategies, observability stacks, and CI/CD pipelines, providing detailed blueprints and mathematical scaling estimates.

This skill provides deep security review for GitHub Actions workflow files. It focuses on the unique Actions threat model—identifying vulnerabilities like script injection via ${{ }} interpolation, privilege escalation from triggers (e.g., pull_request_target), and ensuring least-privilege permissions for CI/CD pipelines. Use it to audit, secure, or harden existing workflows, preventing attacks from untrusted inputs and supply chain risks.

This技能 is used when analyzing complex propositions, debating, or reviewing arguments within the CrossFrame Suite. It helps users deconstruct claims into testable arguments, identifying hidden premises, strongest counterarguments, evidence requirements, and conditions for withdrawal, ensuring conclusions are robust and non-judgmental.