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Found 402 Skills
Discourse 패턴으로 다관점 리뷰를 수행한다. Tech Spec 리뷰와 에픽 통합 코드 리뷰를 모두 처리하며, 쟁점 해소 프로토콜에 따라 합의/미합의를 분리한다.
Launch multiple sub-agents in parallel to execute tasks across files or targets with intelligent model selection and quality-focused prompting
Execute a task with sub-agent implementation and LLM-as-a-judge verification with automatic retry loop
This skill should be used when the user requests to "initialize team", "create development team", "team init", "form a team", or "start project team". It collects project information through interactive Q&A and creates an Agent engineering team with professional roles. 8 team types are supported: software development, software testing, reverse engineering, debugging/bug fixing, security research, CTF competition, software and server operation & maintenance, discussion/seminar.
2-stage pipeline: trace (causal investigation) -> deep-interview (requirements crystallization) with 3-point injection
Multi-agent swarm orchestration where AI agents spawn, coordinate, and self-organize into collaborative teams. Use when running parallel AI agent tasks, orchestrating multi-agent workflows across Claude Code / Codex / Cursor / custom agents, isolating agent workspaces via git worktrees, tracking task dependencies across agents, or running autonomous experiments. Triggers on: clawteam, agent swarm, spawn agents, multi-agent team, agent orchestration, parallel agents, agent coordination, swarm intelligence, agent spawn, clawteam spawn, agent worktree, agentic team, ml agent experiments, autonomous agents, agent team.
Orchestrate parallel AI coding agents across git worktrees for autonomous CI fixes, code reviews, and PR management
Read a story file and implement it. Loads the full context (story, GDD requirement, ADR guidelines, control manifest), routes to the right programmer agent for the system and engine, implements the code and test, and confirms each acceptance criterion. The core implementation skill — run after /story-readiness, before /code-review and /story-done.
Design and build multi-agent harness architectures for long-running AI application development. GAN-inspired Generator-Evaluator pattern, Sprint Contract negotiation, context management, quality criteria calibration. Based on Anthropic Engineering patterns. Use when: "build a harness", "multi-agent architecture", "agent orchestration", "generator-evaluator", "long-running app", "harness design", "agent pipeline", "quality evaluation loop", "sprint contract", "build app with agents", "Claude Agent SDK architecture", or when building complex full-stack apps that need planning → generation → evaluation cycles. Also use when discussing context degradation, self-evaluation bias, or assumption testing in AI workflows.
LangChain / LangGraph engineering pitfalls and verified fixes. Covers DeepAgents, OpenAI-compatible model integration (including Chinese provider adapters: DeepSeek, Qwen, GLM, etc.), middleware, streaming, multi-agent orchestration, and other common development issues. Use when hitting unexpected behavior, making architecture decisions, or integrating Chinese LLM providers during LangChain development.
Orchestrates infrastructure cost estimation with tier-based or custom TPS sizing. Offers pre-configured tiers (Starter/Growth/Business/Enterprise) or custom TPS input. Skill discovers components, asks shared/dedicated for EACH, selects environment(s), reads actual Helm chart configs, then dispatches agent for accurate calculations.
Controlled plan execution with human review checkpoints - loads plan, executes in batches, pauses for feedback. Supports one-go (autonomous) or batch modes.