Skill: raufimusaddiq/glm-autoroute (version 1.2.0)

# 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>" }) ``` 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** — 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.

path: package.json
content: {
  "name": "glm-autoroute",
  "description": "Binary model routing for ZAI GLM models - lightweight vs heavyweight tasks",
  "version": "1.0.3",
  "main": "SKILL.md"
  "openclawMin": "2026.2.13"
  "dependencies": {},
  "author": "Raufi Musaddiq",
  "license": "MIT"
  "tags": ["openclaw", "skills", "models", "zai", "glm"]
}

Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)