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Found 42 Skills
Spawn Codex subagents via background shell to offload context-heavy work. Use for: deep research (3+ searches), codebase exploration (8+ files), multi-step workflows, exploratory tasks, long-running operations, documentation generation, or any other task where the intermediate steps will use large numbers of tokens.
Comprehensive codebase review and parallel agent-based remediation skill. Use PROACTIVELY when agent needs to perform full codebase audit, generate master findings report with quantified metrics, and execute remediation using parallel goodvibes background agents (max 6 concurrent, one task per agent with fresh context). Triggers on: codebase review, code audit, full project analysis, quality assessment, technical debt analysis, parallel remediation, bulk fixes.
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.
Interactive agent picker for composing and dispatching parallel teams
6-phase investigation workflow for understanding existing systems. Auto-activates for research tasks. Optimized for exploration and understanding, not implementation. Includes parallel agent deployment for efficient deep dives and automatic knowledge capture to prevent repeat investigations.
Launch 3 research agents in parallel — market, users, tech — fast answers
Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".
Comprehensive codebase quality audit with parallel agent orchestration, GitHub issue creation, automated PR generation per issue, and PM-prioritized recommendations. Use for code review, refactoring audits, technical debt analysis, module quality assessment, or codebase health checks.
Use when user wants to find a note to publish as a blog post. Triggers on「选一篇笔记发博客」「note to blog」「写博客」「博客选题」. Scans Obsidian notes via Python script, evaluates blog-readiness, supports batch selection with fast/deep dual-track and parallel Agent dispatch.
Conduct web research and material downloading for each node. Read node-list.txt, launch multiple sub-agents to perform parallel web research on node content, deeply retrieve relevant webpages/articles/blogs/literature, download and save them locally, and output a download.txt file to record the material sources for each node. Suitable for document writing scenarios that require extensive background information, data verification, and reference sources.
Use this method when fact-checking drafts that include dates, quantities, or causal claims by cross-referencing multiple independent sources.
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.