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Productivity & Tasks @raufimusaddiq Updated 6/28/2026 1,296 downloads 0 stars Security: Pass

Glm Autoroute OpenClaw Plugin & Skill | ClawHub

Looking to integrate Glm Autoroute into your AI workflows? This free OpenClaw plugin from ClawHub helps you automate productivity & tasks tasks instantly, without having to write custom tools from scratch.

What this skill does

Binary model routing for ZAI GLM models - lightweight vs heavyweight tasks.

Install

ClawHub CLI
openclaw skills install @raufimusaddiq/glm-autoroute
Node.js (npx)
npx clawhub@latest install glm-autoroute

Full SKILL.md

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SKILL.md content below is scrollable.

GLM Autoroute

Binary model routing for ZAI GLM models - lightweight vs heavyweight tasks.

Introduction

  1. GLM-4.7 is the default model. Only spawn GLM-5 when the task actually needs it.
  2. Use sessions_spawn to run tasks with GLM-5:
sessions_spawn({
  task: "<the full task description>",
  model: "zai/glm-5",
  label: "<short task label>"
})
  1. After done with GLM-5, the main session continues with GLM-4.7 as default.

Models

GLM-4.7 (DEFAULT - zai/glm-4.7)

Use for lightweight tasks:

  1. Simple Q&A - What, When, Who, Where
  2. Casual chat - No reasoning needed
  3. Quick lookups
  4. File lookups
  5. Simple tasks - repetitive tasks, formatting
  6. Cron Jobs - if it needs reasoning, THEN ESCALATE TO GLM-5
  7. Status checks
  8. Basic confirmations
  9. Provide concise output, just plain answer, no explaining

DO NOT:

  • ❌ DO NOT CODE WITH GLM-4.7
  • ❌ DO NOT ANALYZE USING GLM-4.7
  • ❌ DO NOT ATTEMPT ANY REASONING USING GLM-4.7
  • ❌ DO NOT RESEARCH USING GLM-4.7
  • If you think the request does not fall into point 1-8, THEN ESCALATE TO GLM-5
  • If you think you will violate the DO NOT list, THEN ESCALATE TO GLM-5

GLM-5 (zai/glm-5)

Use for heavyweight tasks:

  1. Coding (any complexity)
  2. Analysis & debugging
  3. Multi-step reasoning
  4. Research & investigation
  5. Critical planning
  6. Architecture decisions
  7. Complex problem solving
  8. Deep research
  9. Critical decisions
  10. Detailed explanations

Examples

Task Model Why
"Check calendar" GLM-4.7 Simple lookup
"What time is it?" GLM-4.7 Simple Q&A
"Heartbeat check" GLM-4.7 Routine
"Read this file" GLM-4.7 Simple lookup
"Summarize this" GLM-4.7 Basic task
"Write Python script" GLM-5 Coding
"Debug this error" GLM-5 Analysis
"Research market trends" GLM-5 Deep research
"Plan migration" GLM-5 Complex planning
"Analyze this issue" GLM-5 Analysis

Other Notes

  1. When the user asks to use a specific model, use it
  2. Always mention which model is used in outputs — example: "(GLM-5)" or "(GLM-4.7)" at the end of responses
  3. After done with GLM-5 (via sessions_spawn), continue with GLM-4.7 as default
  4. If you think the request does not fall into GLM-4.7 use cases, THEN ESCALATE TO GLM-5
  5. If you think you will violate the DO NOT list, THEN ESCALATE TO GLM-5
  6. Coding = always GLM-5
  7. When in doubt → GLM-5 (better safe than sorry)
  8. Heartbeat checks → always GLM-4.7 unless complex analysis needed

Memory Management with sessions_spawn

When spawning GLM-5 sub-agent sessions for ANY task (coding, research, analysis, planning, etc.), follow this pattern:

Output Rules

1. Code Output (Important)

  • Full code ONLY in files — do NOT include in announce unless explicitly requested
  • Provide summary: what was created, file path, status, dependencies
  • Full code disclosure ONLY when:
    • User explicitly requests: "Show me the code"
    • Debugging needs code review
    • User wants to improve/modify it

2. Full Announce for Other Results

  • Research findings, analysis results, solutions → announce FULLY to user
  • Do NOT shorten, summarize, or condense non-code output
  • User gets complete findings, not a brief summary

3. Two-Layer Memory Strategy

MEMORY.md (Curated Long-Term)

  • ONLY key insights, decisions, lessons, significant findings, preferences
  • Clean, concise, actionable
  • Skip routine data, step-by-step reasoning, temporary thoughts

Detailed Reports (Task-Specific Files)

  • For research: research/YYYY-MM-DD-topic.md (full findings, data, analysis)
  • For coding: add inline docs/README in code folder if needed
  • For analysis: output files in relevant project directories

Examples

Research task:

sessions_spawn({
  task: "Research X. Announce full findings to user. Write full report to research/YYYY-MM-DD-X.md, then write ONLY key insights to MEMORY.md (clean, concise).",
  model: "zai/glm-5",
  label: "Research X"
})

Coding task:

sessions_spawn({
  task: "Write Python script for X. Save full code to file. Provide summary (what created, path, status, dependencies) in announce. Write key implementation decisions to MEMORY.md (important only).",
  model: "zai/glm-5",
  label: "Python script X"
})

Apply this pattern to ALL GLM-5 spawns. Code in files only, summary in announce, full disclosure on request.

ClawHub Registry URL: https://clawhub.ai/raufimusaddiq/skills/glm-autoroute

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