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supervisor-orchestrator
Supervisor-orchestrator pattern: Orchestrator controls task planning and delegation to specialized agents with full decomposition logic.
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Supervisor-orchestrator pattern: Orchestrator controls task planning and delegation to specialized agents with full decomposition logic.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | supervisor-orchestrator |
| description | Supervisor-orchestrator pattern: Orchestrator controls task planning and delegation to specialized agents with full decomposition logic. |
[!IMPORTANT] When to use: When an orchestrator needs to plan, decompose, and coordinate multiple sub-agents — with routing logic built into the orchestrator itself.
Does the task require multiple specialized capabilities?
├── YES
│ └── Do you need a supervisor to route and coordinate sub-agents?
│ ├── NO, sub-agents need autonomy with orchestrator control?
│ │ └── YES → Supervisor Orchestrator (you're here)
│ └── YES, just routing needed?
│ └── YES → supervisor-delegation
└── NO → single-agent
| ✅ Use Orchestrator When | ❌ Avoid When |
|---|---|
| Task decomposition is complex/unpredictable | Task has simple, fixed routing |
| Orchestrator needs fine-grained control over execution | Multi-level hierarchy needed |
| Sub-agents need to work on shared artifacts/data | Simple parallel execution suffices |
| You need conditional branching based on intermediate results |
┌─────────────────────────────────────────────┐
│ Orchestrator │
│ ┌───────────────────────────────────────┐ │
│ │ • Task decomposition │ │
│ │ • Step sequencing │ │
│ │ • Conditional branching │ │
│ │ • Result synthesis │ │
│ └───────────────────────────────────────┘ │
└──────────────────┬──────────────────────────┘
│
┌─────────────┼─────────────┐
▼ ▼ ▼
┌────────┐ ┌────────┐ ┌────────┐
│Agent A │◄──│Agent B │──►│Agent C │
│(Search)│ │(Analyze) │(Write) │
└────────┘ └────────┘ └────────┘
│ │ │
└─────────────┼─────────────┘
▼
Shared Context
(Artifacts, State)
You are an orchestrator controlling a team of specialized agents.
Task: {user_task}
Available agents:
- search_agent: Web search, fact retrieval
- analysis_agent: Data analysis, pattern recognition
- writing_agent: Content creation, editing
Orchestrator responsibilities:
1. Decompose the task into steps
2. Assign each step to the appropriate agent
3. Manage shared state between agents
4. Handle errors and retries
5. Synthesize final output
Always maintain shared context between agents.
| Approach | Use When |
|---|---|
| Central artifact (file/DB) | Agents produce/consume structured outputs |
| Message passing | Real-time coordination needed |
| Orchestrator intermediates | Supervisor holds all state, agents stateless |
| Mistake | Why It's Bad |
|---|---|
| Over-centralizing in orchestrator | Bottleneck, single point of failure |
| No shared state mechanism | Agents work in isolation, results don't integrate |
| Rigid step sequencing | Can't adapt to intermediate results |
| Orchestrator does work itself | Defeats purpose of delegation |
Need multiple levels of supervisory control?
├── YES → hierarchical-supervisor
└── NO, but agents should operate independently?
└── YES → Consider parallel-execution for independent branches
Is orchestration actually simpler than expected?
├── YES, just routing based on type?
│ └── YES → supervisor-delegation
├── YES, just parallel independent tasks?
│ └── YES → parallel-execution
└── YES, one LLM can handle it?
└── YES → single-agent
| Factor | Supervisor Orchestrator |
|---|---|
| Latency | High (decomposition + coordination + execution) |
| Cost | Higher (orchestrator + agents + state management) |
| Complexity | High |
| Reliability | Orchestrator critical; agents can be isolated |
| Best for | Complex multi-step tasks with shared state |
Pattern ID: supervisor-orchestrator | AGEM-compatible
Formal reasoning via FOL theorem proving, model finding, and categorical logic
Use modern, token-efficient command-line tools (fd, ripgrep, ast-grep, bat, sd, jq, yq, tokei, eza, dust, procs, delta, hyperfine, just) instead of legacy coreutils (find, grep, cat, sed, ls, du, ps) for any shell, file, or codebase task — on any platform (Linux/macOS/Windows) and through any executor (a terminal, Claude Code's bash tool, Desktop Commander, or an MCP shell bridge). Trigger whenever you are about to search files, grep for text, read or inspect files, find-and-replace, refactor code, traverse a repository, process JSON/YAML, or inspect disk usage or processes — even if the user did not name a specific tool. These tools are faster, .gitignore-aware, and emit clean structured output that protects the context window. For installation across operating systems and platform-specific quirks, see references/. If you run commands specifically through the LocalREPL MCP bridge, also consult the local-repl-cli skill for environment-specific gotchas.
Operating guide for the AGEM reasoning engine: native tools, honest metric interpretation, formal-logic verification via mcp-logic, and the meta-tool MCP interface.
Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.
Master orchestrator for the five-question decision tree that selects the right agentic pattern from Sequential, ReAct, Planning, Reflection, or Multi-Agent.
Multi-agent pattern for tasks requiring specialization or scale beyond single-agent capacity. Covers topology, routing, and coordination design.