team-setup
Esta skill debe usarse cuando el usuario pide 'configura un team', 'prepara equipo de agentes', 'crea un team de agentes', 'setup del team', 'team de agentes para', 'orquesta agentes', 'team-setup', 'arma un equipo', 'necesito un team para', o cuando quiere coordinar multiples agentes trabajando en repos/directorios distintos. Genera el prompt estructurado para el TeamLead incluyendo roles, workspaces, contexto inicial y reglas universales del equipo. Detecta automaticamente si el objetivo es negociacion de contratos (endpoints, sockets, SSEs) y activa el flujo especializado de debate balanceado.
What this skill does
# Team Setup Extract team configuration from context or user input, then generate a structured TeamLead prompt. ## Phase 0: Pre-flight Before gathering configuration, orient the user on two setup decisions: ### 0.1 Display Mode Ask which display mode they prefer, or infer from context: - **`auto`** — split-panes if tmux is available, falls back to in-process. Best for most setups. - **`in-process`** — all teammates run inside the current terminal; switch with Shift+Down. - **`tmux`** — forces split-panes; requires tmux. If unsure, default to `auto` and mention it. ### 0.2 Team Size & Task Budget Share these guidelines so the user can plan their team accordingly: - **Optimal team size**: 3–5 teammates. More than 5 increases token cost and coordination overhead significantly. - **Task budget per teammate**: 5–6 tasks each keeps everyone productive without overloading any agent. - **File ownership**: each teammate should own different files — parallel edits to the same file cause conflicts. --- ## Phase 1: Gather Configuration Gather the following before generating the prompt. Extract from context first; use `AskUserQuestion` for anything missing. ### 1.1 Agents & Roles For each agent on the team, collect: - **Role name**: What this agent represents (e.g., "Frontend Engineer", "Backend Engineer", "QA") - **Responsibilities**: What this agent is accountable for - **Workspace path**: Absolute path to the repo/directory on the filesystem where this agent works - **Model** (optional): Which Claude model to assign to this agent. Recommendations: - `haiku` — research, QA, simple review tasks - `sonnet` — general implementation (default if unspecified) - `opus` — complex architectural decisions, critical reviews ### 1.2 Initial Context For each agent, determine what context to provide upfront: - CLAUDE.md files in their workspace - Relevant architecture docs, schemas, or specs - Or instructions for where the agent should look to orient itself ### 1.3 Team Type Detect the team's objective from context. Two main types: - **General team**: Agents collaborate toward a common goal (code, infra, data, etc.) - **Contract negotiation**: Frontend and backend agents negotiate an API/socket/SSE contract If the objective involves defining endpoints, events, payloads, or communication protocols between a frontend and a backend, treat it as a contract negotiation and read [`references/contract-negotiation.md`](references/contract-negotiation.md) to include the specialized debate flow in the TeamLead prompt. --- ## Phase 2: Generate TeamLead Prompt Build the TeamLead prompt with these sections: ### Section A: Team Overview ``` You are the TeamLead for a [general | contract negotiation] team. Your job is to coordinate the following agents and drive them toward [objective]. ## Agents - [Role Name] — [Responsibilities] Workspace: [absolute path] Initial context: [CLAUDE.md path or "read CLAUDE.md and explore the repo structure"] - [Role Name] — [Responsibilities] Workspace: [absolute path] Initial context: [...] ``` ### Section B: Universal Team Rules Always include this block verbatim: ``` ## Team Rules (non-negotiable) 1. No agent may make file modifications or commits without explicit order from you (TeamLead) or from the user. If an agent believes it needs to write code, it must propose the change and wait for approval before touching the filesystem. 2. Task tracking: whenever an agent works on 3 or more files in a single task, it must use the Tasks tool to register and track progress. Single-file or two-file edits do not require task registration. 3. Agents are free to communicate with each other directly. However, any decision that affects scope, architecture, or implementation direction must be surfaced to you (TeamLead) before it is acted upon. You decide whether it also needs to go to the user. 4. Doubt escalation: when any agent has a question — about business rules, UX behavior, or anything outside its own domain — it may ask the user directly using AskUserQuestion, or route it through you if it requires team-level context or coordination. Either path is valid; use judgment based on the scope of the question. ``` ### Section C: Objective & First Actions Describe what the team should accomplish and list the first concrete actions the TeamLead should take: - Orient each agent to its workspace - Provide initial context - Kick off the first round of work (or the contract negotiation debate) --- ## Phase 3: Output Present the generated TeamLead prompt to the user in a code block so they can copy it directly. Confirm it covers their intent and offer to adjust roles, paths, or objectives before they proceed. After presenting the prompt, include this limitations note as a brief callout — not inside the prompt itself: ``` Note: Claude Code agent teams are experimental. Keep in mind: - /resume does not restore teammates — if the session closes, the team is gone. - Teammates cannot spawn their own teams (no nested teams). - Only one team can be active per session. ``` Use [`references/teamlead-prompt-template.md`](references/teamlead-prompt-template.md) as the structural template for the generated prompt. It defines the exact sections and ordering to follow. --- ## Resources - **[`references/contract-negotiation.md`](references/contract-negotiation.md)** — Detailed rules and flow for the frontend/backend contract negotiation case. Load this when team type is contract negotiation. - **[`references/teamlead-prompt-template.md`](references/teamlead-prompt-template.md)** — Full template for the generated TeamLead prompt. Always use this as the base for Phase 3 output.
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.