learning-output-style

Full-lifecycle development orchestration — design, plan, implement with TDD, review, respond to feedback, and ship with engineering discipline. Jira ticket triage, Sentry error-driven workflows, and interactive QA sessions with headless Chrome reproduction. 13 commands, 17 agents, 11 skills. Flags: --auto (full autopilot), --team (collaborative agents), --worktree (parallel isolation), --repo (multi-repo support), --jira-sync (Jira progress tracking).

11 skills · 13 commands · 17 agents · hooks

Analyze codebases to discover bugs, security vulnerabilities, architectural problems, and improvements, then create GitHub issues

2 commands · 7 agents

Comprehensive PR review agents specializing in comments, tests, error handling, type design, code quality, and code simplification

1 command · 6 agents

Streamline your git workflow with simple commands for committing, pushing, and creating pull requests

3 commands

Adds educational insights about implementation choices and codebase patterns (mimics the deprecated Explanatory output style)

hooks

Frontend design skill for UI/UX implementation

1 skill

Collection of example skills demonstrating various capabilities including skill creation, MCP building, visual design, algorithmic art, internal communications, web testing, artifact building, Slack GIFs, and theme styling

19 skills

Interactive self-referential AI loops for iterative development. Claude works on the same task repeatedly, seeing its previous work, until completion.

3 commands · hooks

Professional UI/UX design intelligence for AI coding assistants. Includes searchable databases of styles, colors, typography, charts, and UX guidelines for React, Next.js, Astro, Vue, Nuxt.js, Nuxt UI, Svelte, SwiftUI, React Native, Flutter, Tailwind, shadcn/ui, Jetpack Compose, Angular, Laravel, JavaFX, and Three.js.

7 skills

Agentic QE (external: maintained in proffesor-for-testing/agentic-qe, pinned to v3.15.17): a quality-engineering fleet with 11 QE agents, 9 skills, 9 commands, the agentic-qe MCP server pinned to the plugin version, and the aqe-mod guard that refuses destructive operations on AQE learning data

9 skills · 9 commands · 11 agents · 1 MCP server

Skill: add headroom-ai compress() to a Python or TypeScript app to cut LLM input tokens (tool outputs, logs, JSON, chat history). No API key.

1 skill

Cross-session memory and token savings for coding agents.

6 skills · 1 agent · 1 MCP server · hooks