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Found 163 Skills
Provides autonomous project pattern learning by analyzing the codebase to discover development conventions, architectural patterns, and coding standards, then generates project rule files in .claude/rules/. Use when user asks to "learn from project", "extract project rules", "analyze codebase conventions", "discover project patterns", or wants to auto-generate Claude Code rules for the current project.
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "multi-agent", "agent swarm", "coordinator agent", "worker agent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", "agents that communicate", "parallel agents", or needs guidance on agent structure, system prompts, triggering conditions, subagent orchestration, or multi-agent swarm development for Claude Code.
Internal protocol for evo optimization subagents. Not user-invocable -- read by subagents spawned from /optimize.
Execute one role inside the loop CLI orchestrator. Use when the CLI asks you to act as planner, coding, or review agent and return strict handoff JSON while using loop-owned commands for Git integration.
Design, test, and optimize prompts for LLM interactions. Cover prompt patterns (few-shot, chain-of-thought, ReAct), system prompt design, output formatting, prompt evaluation, and prompt optimization techniques. Triggers on "write prompt", "optimize prompt", "design system prompt", "few-shot examples", "chain of thought", "prompt evaluation", "LLM output formatting", "prompt testing", or "prompt patterns".
Decomposition playbook + anti-temptation rules for an orchestrator profile routing work through Kanban. The "don't do the work yourself" rule and the basic lifecycle are auto-injected into every kanban worker's system prompt; this skill is the deeper playbook when you're specifically playing the orchestrator role.
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
Vertical / parallel implementation planning skill. Creates DAG-structured plan directories where each step is an independent, QA-able vertical slice that sub-agents can pick up and implement in parallel. Use whenever the user wants a plan that fans out (multiple independent features), invokes /v-plan, or asks for a "parallel plan", "DAG plan", "vertical plan", or "plan that can be parallelized" — even if they don't say those exact words. Prefer the linear `planning` skill for strictly sequential work.
Execute a single DAG step as an autonomous background sub-agent. Sibling of phase-running for DAG plans produced by v-planning. Reads a step-<n>.md file directly, atomically claims it via frontmatter status, runs the three-bucket Success Criteria, and reports back. Spawned by v-implementing or by /run-step.
Plan implementation skill. Executes approved technical plans phase by phase with verification checkpoints.
Implement a task with automated LLM-as-Judge verification for critical steps