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Found 84 Skills
Execute multiple independent tasks simultaneously using parallel agent coordination to maximize throughput. Use when tasks have no dependencies, results can be aggregated, and agents are available for concurrent work.
Independence-validated parallel fleet that runs each worker (claude -p or codex exec) in its own git worktree. Use when tasks touch non-overlapping files and you need merge-safe isolation (each worker on its own branch). For DAG-ordered one-shot workers with budgets, use dag-fleet. For headless iteration with a reviewer loop, use iterative-fleet.
Autonomous TDD development loop with parallel agent swarm, category evolution, and convergence detection. Use when running autonomous game development, quality improvement loops, or comprehensive codebase reviews.
Decomposes complex, multi-day tasks into optimized milestones using parallel reviewer agents (ultraplan). Spawns 5 independent reviewers that analyze the problem from different angles, then synthesizes their findings into a milestone dependency DAG. Triggers when the user says "plan milestones", "break this into milestones", "ultraplan", or when long-run harness needs milestone generation.
explore — Deep codebase exploration with parallel agents. Use when exploring a repo or discovering architecture.
Fix grammar and spelling errors in one or multiple files while preserving formatting
Explore a codebase with parallel Haiku agents. Modes - --fast (1 agent), default (3), --deep (5). Use when user says "learn [repo]", "explore codebase", "study this repo".
This skill should be used when the user asks about new features, recent changes, or updates in Claude Code — for example "what's new in Claude Code?", "Claude Code changelog", "what did I miss in Claude?", "any recent updates?", "tell me about new Claude features", or "what's changed since version 1.0.30?". It fetches the official changelog, filters for notable features (excluding bug fixes), researches each feature for deeper context on Anthropic's website, and presents mini-article summaries. Supports both automatic tracking (since last check) and explicit version queries.
Launch N parallel subagents in isolated git worktrees to compete on the session task.
Process large codebases (>100 files) using the Recursive Language Model pattern. Orchestrates parallel sub-agents to map-reduce across files without context rot. Use when: analyzing large repositories; auditing security or auth across many files; finding patterns across 50+ files; processing large log files or data dumps
Extract structured data from multiple documents into comparison matrix with citations. Use for bulk document review.
Enhance a plan with parallel research agents for each section to add depth, best practices, and implementation details