🎁 Get the FREE AI Skills Starter Guide β€” Subscribe β†’
BytesAgainBytesAgain
BytesAgain β€Ί Skills β€Ί GLM Autoroute
πŸ¦€ ClawHub

GLM Autoroute

by @raufimusaddiq

Routes tasks between GLM-4.7-FlashX for simple queries and GLM-5 for coding, analysis, reasoning, and complex tasks, switching automatically as needed.

Versionv1.2.0
Downloads1,403
#clawhub
TERMINAL
clawhub install glm-autoroute

πŸ“– About This Skill

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: "",
  model: "zai/glm-5",
  label: ""
})
3. 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 β€” 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 β†’ GLM-5 (better safe than sorry) 8. Heartbeat checks β†’ 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 β€” 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 β†’ 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.

    πŸ’‘ 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.