Skill UI

Automates the complex pre-development workflow for large-scale system transformations, such as major rewrites, migrations, or overhauls. This skill performs comprehensive project analysis, task decomposition, planning document generation, and sets up detailed progress tracking across multiple phases, ensuring a structured approach before any coding begins. It supports adaptive control and integrates robust task tracking via GitHub or local files.

AgentFlow transforms your existing Kanban board (Asana, GitHub, Linear) into a self-managing, autonomous AI development pipeline. It orchestrates multiple AI agents through defined stages (Backlog to Done), enforcing deterministic quality gates, performing adversarial reviews, and tracking costs—all visible within your existing project management UI. It provides crash-proof execution and full observability for complex software development lifecycles.

A comprehensive collection of agent skills for rigorously auditing digital interfaces and user flows. It evaluates designs against 168 research-backed UX/UI principles, detects common design antipatterns, and provides specialized review for AI-powered interfaces, ensuring adherence to modern best practices.

A specialized audit skill designed to identify and quantify resource leakage, excessive costs, and billing vulnerabilities within complex development tool workflows (e.g., GitHub Apps). It traces webhook events, queue processing, and model calls to pinpoint where token burn or unauthorized resource usage is occurring, ensuring cost efficiency and clean billing practices.

This skill provides comprehensive automation for managing GitHub workflows across the entire development lifecycle. It covers detailed issue triage, PR review status checks, CI/CD failure debugging, official release management, and security monitoring using the GitHub CLI. Ideal for maintaining open-source project health and streamlining team development processes.

This skill guides users through evidence-based terminal operations. It is designed for scenarios requiring direct command execution, Git status checks, debugging CI/build failures, and pushing narrow, verified fixes. It emphasizes following a structured, minimal-scope workflow and accurately reporting the repository's current state (e.g., local changes vs. pushed code).

Design and implement a unified operational workflow for managing notifications sourced from disparate tools like GitHub, Linear, local hooks, and chat platforms. This skill addresses notification fragmentation, alert noise, and redundancy by establishing clear severity levels, ownership, routing channels, and next operational actions into a single, cohesive pipeline.

Guides disciplined git workflows for AI-assisted teams, covering branch strategies, commit hygiene, descriptive messages, and parallel workstreams to keep changes reviewable and reversible.

Use the gh CLI to inspect PRs, issues, workflow runs, and logs while querying GitHub resources via the API or structured JSON filters.

This comprehensive guide provides detailed procedural steps for configuring and executing CodeQL code scanning. It covers both implementing automated workflows using GitHub Actions (`codeql.yml`) and running scans via the CodeQL CLI. Users will learn how to manage language matrices, set build modes, interpret SARIF outputs, handle monorepo structures, and troubleshoot complex security analysis setups across multiple languages (e.g., Python, Java, C++).

This workflow guides developers through generating standardized, descriptive commit messages using the Conventional Commits specification. It provides a structured XML template (including type, scope, description, body, and footer) and integrates directly into the Git workflow, ensuring project history is clean, traceable, and machine-readable. Ideal for maintaining large, collaborative codebases.

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.