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Found 85 Skills
Spawn and manage parallel AI coding agents via tmux. Use when you need to orchestrate workers, delegate sub-tasks, run multi-agent improvement loops, or manage agent lifecycles with orca CLI commands like spawn, list, kill, steer, logs, and daemon.
Production-grade engineering skills for AI coding agents - lifecycle commands, workflow automation, and best practices for software development.
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics
AWS-curated copy-paste prompts for AI coding agents (MVP scaffolding, RAG chatbot with Claude on Bedrock, security baseline evaluation, cost anomaly detection, GPU quota requests, EKS deployment, Well-Architected review, etc.) plus downloadable installable agents (Multi-Account Transition Advisor, Bill Shock Preventer, Service Quota Agent, Bedrock Model Availability Agent, AWS DB Advisor). Use when the user asks for a prompt to do X on AWS, wants an installable agent for multi-account / cost monitoring / quota management / Bedrock model availability / database selection, or asks how to use AWS prompts. For migration intent (GCP to AWS, OpenAI/Gemini to Bedrock), route to the migration-to-aws skill. Do not use for: factual AWS Activate / programs / credits questions, learn articles, sample architectures, or for prompts that are not in the bundled `references/prompt-library/` tree.
Create, rewrite, shorten, or review /goal prompts for Codex, Claude Code, and other coding agents. Use only when the user explicitly requests /goal text or asks to convert a task into /goal; never start the goal automatically.
Manage background coding agents in tmux sessions. Spawn Claude Code or other agents, check progress, get results.
Disciplined spec-driven test-driven development workflow for building software with AI coding agents. Transforms ambiguous requests into verified implementations through structured specification, test derivation, and strict TDD. Handles greenfield projects, brownfield enhancements (with or without existing tests), refactors, and complex bug fixes with workflow-specific guidance for each. Use when the user requests a new feature, module, enhancement, refactor, API, data pipeline, CLI tool, or system with multiple requirements, edge cases, or unclear specifications. Also use for complex bug fixes requiring root cause analysis. Triggers on phrases like "add a feature", "implement", "build a new module", "build an API", "build a CLI", "build a data pipeline", "refactor", "fix this bug", "write tests for", "TDD", "test-first", "the requirements are unclear", "characterization tests", or "spec this out". Triggers when modifying code with adjacent test files (`tests/`, `*_test.py`, `*.test.ts`, `*.spec.ts`, `spec/`, `__tests__/`) or test framework config (pytest.ini, jest.config.*, go.mod with testing imports, Cargo.toml with [dev-dependencies], package.json with a test script). Triggers when the user mentions edge cases, invariants, acceptance criteria, EARS notation, or red-green-refactor. Do NOT use for simple one-line fixes, cosmetic changes, formatting, renames, dependency bumps, or tasks where requirements are already fully specified with tests provided.
Named Tmux Manager - Multi-agent orchestration for Claude Code, Codex, and Gemini in tiled tmux panes. Visual dashboards, command palette, context rotation, robot mode API, work assignment, safety system. Go CLI.
Professional prompt engineering, context engineering, and AI agent orchestration for coding agents (Claude Code, Codex, Cursor, Gemini CLI). Use when designing CLAUDE.md/AGENTS.md files, writing skills, planning multi-agent pipelines, optimizing token usage, managing session handoffs, or structuring any prompt for maximum agent performance. Do NOT use for general coding tasks or code review.
Expert AGENTS.md file assistant. Use when users want to create, verify, or improve AGENTS.md files. Helps with creating minimal, focused AGENTS.md files following progressive disclosure principles, verifying existing files for issues (bloat, contradictions, stale info), and refactoring bloated files.
Assesses and improves a git repository's readiness for AI coding agents with the agentready tool, and reports the score, the certification level, the failing findings, and where the report was written. Covers one repository or every RHDH repository under a directory, and applies the fixes the report supports. Use for "assess agent readiness", "run agentready", "improve our agent readiness score", "prepare this repository for coding agents", or "assess all the RHDH repositories".
Code comment hygiene for AI coding agents: remove generic AI-slop comments, keep the valuable ones, never touch the code.