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Comprehensive map for multi-brain, orchestration, and agent governance. Triggers when users ask to 'view the orchestration ecosystem', 'how do agents work together?', 'multi-brain workflows', or 'give agents access'.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Create, optimize, critique, and programmatically structure prompts for AI systems. Use this skill whenever the user is designing or improving a static prompt, system prompt, coding prompt, agent prompt, workflow prompt, MCP-oriented prompt package, or an algorithmic prompt optimization pipeline. Also use it when the user asks to turn vague AI behavior into a precise instruction set, tool policy, agent spec, evaluation metric, or prompt architecture.
Enforce and manage DESIGN.md specifications, extract design systems from URLs, and combine design reasoning with token roles to prevent drift.
Assumption-first architecture review skill to stress-test project plans and expose hidden risks.
Forces the agent to act with a Claude-like product mindset, prioritizing user journey, UX states, and visual quality before coding.
Compiles and extracts session knowledge into a living, interconnected LLM-Wiki. Instead of writing isolated logs, it identifies key entities, updates cross-referenced topic files in docs/knowledgelib/, and maintains an index and chronological log. Use this to ensure persistent, compounding project knowledge.
Overrides the agent's behavior to enter an interactive, consulting mode. Instead of guessing or immediately executing a task, the agent will analyze the request and ask guiding, targeted questions to clarify intent, constraints, and requirements first.
| name | ecosystem-orchestration |
| description | Comprehensive map for multi-brain, orchestration, and agent governance. Triggers when users ask to 'view the orchestration ecosystem', 'how do agents work together?', 'multi-brain workflows', or 'give agents access'. |
Welcome to the orchestration domain ecosystem. Unlike typical domains (which focus on specific tech stacks like frontend or databases), this meta-ecosystem defines the rules, workflows, and connectivity for how AI autonomous agents interact, plan, recover from errors, and evaluate themselves and each other.
For Orchestrator Agents (
task-decomposer,parallel-planner): This document is your internal operating manual. Use these workflows when structuring complex reasoning tasks, coordinating multiple AI models, or building secure entry points for external agentic integrations.
When the user asks to "integrate an LLM bot", "give an external agent access", or "design an AI pipeline into the app":
@claw-integration-design -> (MANDATORY) Designs the API endpoints, sets up {action}:{resource} OAuth scopes, and builds the generic tools.json manifest. (This must always be the first step for agent connectivity).@tool-selector -> Analyzes the available tooling landscape (APIs, generic scripts) and selects the optimal set of actions for the incoming agent.@context-compressor -> Steps in when agents retrieve massive payloads (logs, raw DB dumps) to compress the context window before it overwhelms the LLM.When given a large, ambiguous objective (e.g., "build an app", "migrate the server"):
@task-decomposer -> Breaks the massive goal into a step-by-step dependency graph.@parallel-planner -> Analyzes the decomposed steps to find where tasks can be run concurrently (saving time and tokens).@memory-ledger -> Used consistently throughout execution to maintain state across different sub-agents.@error-recovery -> Automatically takes over if an executed step fails (retries, attempts alternate tools, or safely escalates to a human).When the user asks to "find the best approach", "debate these two libraries", or "give me a robust solution":
@multi-brain -> General 3-perspective synthesis.@multi-brain-experts -> Specialized perspective synthesis based on strict domain personas (e.g., Security vs UX).@multi-brain-debate -> Used when two distinct choices clash; forces a 2-round adversarial debate.@assumption-checker -> Runs parallel to validate if the underlying facts driving the perspectives are hallucinated or real.@output-critic -> Acts as the final tollgate to score the proposed synthesis before showing it to the user. (For advanced scoring structures, @multi-brain-score can be used).When autonomous agents are operating on sensitive codebases or production databases:
@checkpoint-guardian -> A mandatory tollgate activated right before any destructive steps (e.g., Drop Table, Push to Main) are executed.@agent-reviewer -> An oversight entity that retrospectively audits what the execution agents did, looking for logic holes or skipped steps.Agentic orchestration is about reliability, oversight, and fallback mechanisms. Unlike standard script execution, agents can hallucinate or fail unexpectedly. Every skill in this ecosystem is designed to bound agent behavior within safe limits (claw-integration, checkpoint-guardian), ensure high-quality reasoning (multi-brain, critic), and provide resilient recovery (error-recovery). Never trust a single "one-shot" output for a complex scenario without decomposition and review.
As an AI Agent reading this ecosystem file, you must actively guide the user to install the missing skills for the selected workflow.
To install all skills within this ecosystem at once, you must execute or instruct the user to execute the provided installation script:
python skills/ecosystem-orchestration/scripts/install_all.py
Execution Rule: Do not attempt to perform the tasks of these skills yourself. Your job is to install them and hand off the execution to the appropriate skill, or instruct the user to trigger the newly installed skills in order.