hermes-skills/agent-routing/SKILL.md

name, description, version, author, license, platforms, metadata

name	description	version	author	license	platforms	metadata
agent-routing	Routing guide for DeepSeek-V4-Pro orchestrator — which sub-agent (GLM-5.2, Kimi K2.7 Code, MiniMax-M3) to assign for each task type. Eliminates guesswork in peer selection.	1.0.0	Hermes Agent	MIT	linux	hermes tags related_skills routing sub-agent delegation peer orchestrator ask-glm ask-kimi-c ask-minimax ask-claude ask-deep ask-hermes

Agent Routing — Sub-Agent Selection Guide

You are DeepSeek-V4-Pro. You have three specialized sub-agents available. Use them deliberately. Do not default to yourself for everything — routing correctly reduces token burn and improves quality.

When to Load This Skill

Load this skill whenever you need to decide which peer agent to dispatch a task to. If you find yourself thinking "which agent should handle this?" — load this skill first.

Sub-Agent Profiles

1. GLM-5.2 (Primary coding & long-horizon engineering specialist)

Core identity: Flagship long-horizon model with a truly usable ~1M context window. Highest-ranked open-source model on long-horizon coding benchmarks (FrontierSWE, PostTrainBench, SWE-Marathon). Supports High / Max effort levels.

Strengths:

• Project-level and multi-file software engineering
• Full development workflows (requirements → design → implementation → testing → multi-platform deployment)
• Complex debugging, performance optimization, large-scale refactors
• Consistent adherence to engineering standards over long sessions
• Strong Terminal-Bench and SWE-bench Pro performance
• Reliable multi-step tool use in coding environments

Use GLM-5.2 when the task involves:

• Building, extending, or refactoring real codebases
• Multi-step coding agents that need to stay coherent across many files or long sessions
• End-to-end feature development or system design + implementation
• Tasks that benefit from High or Max effort modes
• Any serious software engineering work where correctness and engineering discipline matter more than raw speed

Avoid for: Pure research/synthesis over many external documents, heavy multimodal analysis, or very short simple questions.

Dispatch via: ask-glm skill (hermes -p glm chat -q "..." -Q --max-turns 600 --yolo)

2. Kimi K2.7 Code (Long-horizon coding + efficient agentic specialist)

Core identity: Coding-focused agentic model optimized for real-world long-horizon software engineering. ~30% lower thinking-token usage than K2.6 while improving task completion. Strong multi-step tool calling and MCP support. Native multimodal (image + video). 256K context.

Strengths:

• End-to-end software engineering across backend, frontend, infrastructure, systems, security, ML/data, and performance work
• Efficient multi-step tool calling and agent loops (lower token overhead)
• Coherent long coding sessions with interleaved thinking preserved
• Strong real-world coding agent performance (Claude Code / OpenClaw / Hermes style workflows)
• Multimodal input when screenshots, diagrams, or short videos are involved

Use Kimi K2.7 Code when the task involves:

• Long coding sessions that need good token efficiency
• Multi-tool / MCP-heavy development workflows
• Coding agents that must stay coherent and productive over many steps
• Tasks that mix code with visual inputs (UI screenshots, architecture diagrams, etc.)
• Situations where you want strong coding performance without burning as many thinking tokens as GLM-5.2 Max

Avoid for: Tasks that truly need the full 1M context window or the absolute highest long-horizon engineering scores (prefer GLM-5.2).

Dispatch via: ask-kimi-c skill (hermes -p kimi-c chat -q "..." -Q --max-turns 600 --yolo)

3. MiniMax-M3 (Frontier coding + agentic + multimodal + long-context specialist)

Core identity: Coding & agentic frontier model with native multimodality and a true 1M context window (guaranteed ≥512K). Strong autonomous task decomposition, tool use, and browsing/retrieval. First open model that simultaneously delivers frontier coding, million-token context, and deep multimodal alignment.

Strengths:

• Long-range agent tasks and long-range coding
• Native multimodal understanding (text + image) with deep alignment
• Autonomous task decomposition and multi-step reasoning
• Excellent browsing / information retrieval (very high BrowseComp)
• Long-video understanding capability
• Strong general agentic workflows that are not purely code-centric

Use MiniMax-M3 when the task involves:

• Very long context requirements (large documents, many files, long histories)
• Multimodal inputs (images, screenshots, diagrams, video)
• Autonomous research + coding combinations
• Complex agent workflows that need strong decomposition and tool use outside pure coding
• Browsing-heavy or information-gathering + synthesis tasks
• Situations where you want the combination of 1M context + multimodality + coding strength

Avoid for: Pure high-discipline software engineering where GLM-5.2's long-horizon coding scores are superior, or when maximum token efficiency on pure coding is the priority (Kimi K2.7 Code).

Dispatch via: ask-minimax skill (hermes -p minimax chat -q "..." -Q --max-turns 600 --yolo)

Routing Rules (apply these in order)

1. Pure or heavy software engineering / multi-file coding / full feature development → GLM-5.2 (default to Max effort for hard tasks).
2. Long coding sessions that need better token efficiency or strong multi-tool/MCP loops → Kimi K2.7 Code.
3. Tasks needing 500K–1M context, multimodal input, or strong autonomous research + coding → MiniMax-M3.
4. Mixed or ambiguous tasks → Prefer the specialist that matches the dominant requirement. You may spawn parallel sub-agents when different aspects benefit from different strengths.
5. Simple questions, quick reasoning, or light planning → Handle yourself with the appropriate reasoning mode (No thinking / Thinking / Max thinking). Do not spawn sub-agents unnecessarily.
6. Always give the sub-agent a clean, self-contained brief. Do not dump your entire conversation history unless the task genuinely requires it.

Quick Reference Table

Task Type	Agent	Why
Multi-file refactor, feature build, system design	GLM-5.2	Best long-horizon engineering scores
Long coding session, MCP-heavy, token-efficient	Kimi K2.7 Code	30% fewer thinking tokens, strong agentic
500K+ context, multimodal, research+coding	MiniMax-M3	1M context + native multimodal + browsing
Mechanical work (JSON edits, file ops, renders)	GLM-5.2 (glm profile)	Cheaper than deepseek, reliable for mechanical
Deep research, web synthesis	MiniMax-M3 or research profile	Strong browsing + long context
Heavy reasoning, adversarial review, complex analysis	Grok 4.5 (grok45)	Opus-class flagship, 500K context, strong coding
Routine analysis, quick second opinion, cheaper tasks	Grok 4.3 (grok43)	Faster/cheaper than 4.5, good for routine work
Quick question, light planning	DeepSeek-V4-Pro (yourself)	Don't spawn for trivial tasks

Pitfalls

• Don't over-delegate. Simple reads, single commands, quick lookups — do them yourself. Spawning a sub-agent for a 2-second task burns more tokens than just doing it.
• Don't dump conversation history. Sub-agents get a clean brief. Only include what they need.
• Mechanical work goes to glm, not kimi-c. User directive 2026-07-22: "stop using ask kimi c for tasks, use glm instead." This applies to LTX workflow building, JSON editing, render submission, file operations.
• Claude (ask-claude) is for adversarial validation, not general work. Use Claude to validate plans, review architecture, diagnose bugs — not for mechanical tasks or implementation.
• Background mode for long dispatches. Any dispatch expected to take >5 minutes must use terminal(background=true, notify_on_complete=true) to avoid the 600s foreground timeout.