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Found 20 Skills
Adopt multiple expert personas sequentially for complex problem analysis from diverse perspectives. Single-agent only — do NOT spawn sub-agents.
PeachSolution 신규 모듈 개발을 조율하는 통합 팀 스킬. 준비된 DB 스키마와 Spec, ui-proto 기반 표준 모드 + Spec만 모드 + 자연어 prompt 모드를 지원. "팀으로 만들어줘", "풀스택 개발", "팀 개발", "백엔드+UI 전체 생성", "버그 수정해줘", "이 화면에 X 추가해줘", "API와 화면 같이 만들어줘", "백엔드만 만들어줘", "API만 만들어줘", "UI만 추가" 키워드로 트리거. mode=backend(API+Store) | ui(UI만) | fullstack(전체) 지원하며, mode/proto 없이 자연어 입력만으로도 즉흥적 버그 수정·기능 추가 가능. 대규모 작업은 기능 큐와 Contract Gate로 1차 완성도를 높이는 방향을 따른다. peach-team-e2e와 함께 하나의 개발-검증 납품 흐름을 이루되, E2E 검증 독립성은 유지한다. 팀 실행 방식은 요청 범위와 런타임 도구 가용성을 분석해 single-agent / role-queue / agent-team 중 선택한다. 기존 팀 개발 스킬의 개발 조율 역할을 대체하며, DB 생성은 peach-gen-db 선행 단계로 분리한다.
Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
Master skill for parallel subagent-driven execution with automatic fallback to single-agent sequential mode. Use when implementing plans with multiple independent sub-phases (SP1, SP2...) to dispatch parallel subagents, or when requiring code review between implementation and testing.
Byzantine fault-tolerant consensus and distributed coordination. Queen-led hierarchical swarm management with multiple consensus strategies. Use when: distributed coordination, fault-tolerant operations, multi-agent consensus, collective decision making. Skip when: single-agent tasks, simple operations, local-only work.
Build single-agent and multi-agent systems using Google's Agent Development Kit (ADK) in Python, Java, Go, or TypeScript. Use when creating AI agents with ADK, designing multi-agent architectures, implementing agent tools, configuring agent callbacks, managing agent state, orchestrating sequential/parallel/loop agent workflows, or when the user mentions ADK, google-adk, google agent development kit, agentic AI with Gemini, or agent orchestration with Google tools. Also use when setting up ADK projects, writing agent tests, deploying agents, or integrating MCP tools with ADK.
Spawns an Agent Team to collaboratively plan Power Platform / Dataverse applications. Three specialists (Data Architect, UX Designer, The Skeptic) debate and refine the plan before any code is written. Falls back to structured single-agent planning if agent teams are not enabled. Triggers on: "plan my app", "plan with team", "design my app", "architect this app", "plan the schema", "team planning", "agent team plan", "plan power app", "plan dataverse app", "design the data model".
This skill should be used when the user wants to review code, audit a diff, get a second opinion on changes, or run an adversarial review of files in the current working tree. Common triggers include "review this code", "audit this diff", "find issues in", "second opinion on this", "harsh review of", "adversarial review", and "security review of". Picks one or more reviewer personas (adversarial, security, architecture, performance). Reviews local files, `git diff`, or `git diff --staged` only — does not fetch external content. Runs in one of four modes: single-agent (one persona in the current agent), cross-model handoff (independent second opinion via another local AI CLI, with secret-shield preflight + prompt-shield wrap), multi-bg-agent (one persona per parallel background subagent), or agent-team (Claude Code Teams or equivalent on supporting agents). Skip when the user wants formatting fixes (use a linter) or refactoring patterns (use ts-best-practices or ts-best-practices-functional).
Orchestrate and coordinate coding workers in Herdr by spawning parallel Codex workers in git worktree tabs, tracking status and reports, and routing owned task, review, and pull-request lifecycles. Use only when the request explicitly mentions Herdr and asks to orchestrate, coordinate, fan out, parallelize, delegate, or route work among coding workers — or a prompt tells you that you are a Herdr worker reporting to an orchestrator. Never use it merely because a task involves parallel work, multiple agents, or git worktrees without Herdr being named; also not for single-agent Herdr control or general Herdr CLI questions, which the herdr skill covers.
Coordinate Claude Code Agent Teams through filesystem-based protocol. Use when orchestrating multiple Claude agents on parallel tasks, need task dependency management, multi-agent code review or implementation. Do not use when single-agent work suffices, task is not parallelizable.
Agent spawning, lifecycle management, and coordination patterns. Manages 60+ agent types with specialized capabilities. Use when: spawning agents, coordinating multi-agent tasks, managing agent pools. Skip when: single-agent work, no coordination needed.
Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.