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supervisor-delegation
Supervisor delegation pattern: One supervisor LLM routes tasks to specialized sub-agents. Best for multi-capability routing.
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。
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Supervisor delegation pattern: One supervisor LLM routes tasks to specialized sub-agents. Best for multi-capability routing.
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
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.
| name | supervisor-delegation |
| description | Supervisor delegation pattern: One supervisor LLM routes tasks to specialized sub-agents. Best for multi-capability routing. |
[!IMPORTANT] When to use: When one supervisor LLM routes to multiple specialized LLMs. Best for tasks with distinct capability requirements.
Does the task require multiple specialized capabilities?
├── YES
│ └── Do you need a supervisor to route and coordinate sub-agents?
│ ├── YES → Supervisor Delegation (you're here)
│ └── NO, sub-agents need more autonomy?
│ └── YES → supervisor-orchestrator
└── NO → single-agent
| ✅ Use Supervisor Delegation When | ❌ Avoid When |
|---|---|
| Task has distinct phases needing different expertise | Sub-agents need to collaborate on shared context |
| You want centralized control/logic in supervisor | Task decomposition is complex/unpredictable |
| Sub-agents are largely independent workers | You need multi-level handoffs |
┌─────────────┐
│ Supervisor │ ← Routes based on task type
│ LLM │
└──────┬──────┘
│
┌──────────┼──────────┐
▼ ▼ ▼
┌────────┐ ┌────────┐ ┌────────┐
│ Code │ │ Legal │ │Research│
│ Expert │ │ Expert │ │ Expert │
└────────┘ └────────┘ └────────┘
│ │ │
└──────────┼──────────┘
▼
Synthesize
You are a supervisor router. Given a user task:
1. Identify the required capabilities
2. Route to the appropriate specialist agent
3. Synthesize results if multiple agents respond
Agents available:
- code_expert: Python, JavaScript, debugging
- legal_expert: contracts, compliance, privacy
- research_expert: web search, citations, analysis
Respond with your routing decision and reasoning.
| Aspect | Recommendation |
|---|---|
| Scope | Narrow, well-defined capabilities |
| Interface | Consistent input/output format |
| Fallback | Default response if task doesn't fit specialty |
| Independence | Each specialist should be independently usable |
| Mistake | Why It's Bad |
|---|---|
| Vague specialist definitions | Supervisor can't route reliably |
| Overloading supervisor | Supervisor becomes a bottleneck |
| No synthesis layer | User gets fragmented outputs |
| Sub-agents that need shared context | Creates coordination nightmares |
Need multiple levels of supervision?
├── YES → hierarchical-supervisor
│ (Supervisor → Supervisor → Workers)
└── NO, but agents need to collaborate?
└── YES → supervisor-orchestrator
Is the task actually simpler than expected?
├── YES, just parallel independent tasks?
│ └── YES → parallel-execution
└── YES, one LLM can handle it?
└── YES → single-agent
| Factor | Supervisor Delegation |
|---|---|
| Latency | Medium (routing + sub-agent) |
| Cost | Medium (supervisor + specialists) |
| Complexity | Moderate |
| Reliability | Sub-agents isolated, supervisor is single point |
| Best for | Multi-capability routing with central control |
Pattern ID: supervisor-delegation | AGEM-compatible