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Found 416 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.
Design and engineer System Prompts, prompt templates, and multi-agent orchestration contracts for deterministic, leak-proof AI systems. Use when creating agents, writing skill definitions, designing prompt templates with safe variable injection, structuring I/O contracts, or building multi-agent pipelines.
Coordinates skills, frameworks, and workflows throughout the project lifecycle using pattern-based sequencing, goal decomposition, phase-gate validation, and multi-agent orchestration. Use when starting multi-phase projects, sequencing frameworks, decomposing goals into capability plans, validating phase-gate readiness, coordinating subagents, or designing MCP-based tool orchestration.
Autonomous coding agent. Delegate any task that involves understanding, writing, or running code — from a GitHub issue, a bug report, or a user request. It explores, implements, and verifies on its own.
Generate and critically evaluate grounded ideas about a topic. Use when asking what to improve, requesting idea generation, exploring surprising directions, or wanting the AI to proactively suggest strong options before brainstorming one in depth. Triggers on phrases like 'what should I improve', 'give me ideas', 'ideate on X', 'surprise me', 'what would you change', or any request for AI-generated suggestions rather than refining the user's own idea.
Route tasks to optimal agents using learned patterns, model recommendations, and confidence scoring
Run fable-mode execution discipline on Claude Opus — the strongest staged run available. Routes the task to the @fable-orchestrator agent (Opus, Write-less), which stages the work, delegates artifact production to @fable-worker-sonnet / @fable-worker-haiku, and cold-checks deliverables with @fable-verifier. Trigger when the user explicitly asks for thorough/systematic/"deep work" handling on the strongest model ("fable on opus", "stage this on opus", "deep work mode, opus"). Do NOT use for ordinary single-pass tasks — and prefer fable-sonnet or fable-haiku when the task doesn't need peak reasoning.
Implement a task with automated LLM-as-Judge verification for critical steps
Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification
Systematically fix all failing tests after business logic changes or refactoring
Execute tasks through competitive multi-agent generation, multi-judge evaluation, and evidence-based synthesis
Launch multiple sub-agents in parallel to execute tasks across files or targets with intelligent model selection and quality-focused prompting