Dynamic Temperature
by @netanel-abergel
Dynamic LLM temperature selection based on task type. Use when deciding what temperature to apply for a given task — scheduling, communication, creative writ...
clawhub install dynamic-temperature📖 About This Skill
name: dynamic-temperature description: "Dynamic LLM temperature selection based on task type. Use when deciding what temperature to apply for a given task — scheduling, communication, creative writing, or irreversible actions. Ensures precision where needed and warmth where appropriate."
Dynamic Temperature Skill
Purpose
Select the right LLM temperature for each task to balance precision and creativity. Lower = more deterministic. Higher = more creative/natural.Temperature Scale
| Task Type | Temperature | Examples | |---|---|---| | Irreversible actions | 0.0 | Delete calendar event, send official email, destructive CLI ops | | Scheduling / Commands | 0.2 | Meeting coordination, dates, facts, CLI commands | | Analysis / Summaries | 0.3 | Status reports, structured thinking, meeting notes | | General communication | 0.5 | Daily WhatsApp replies, updates, follow-ups | | Briefings / Drafts | 0.6 | Morning briefing, drafting emails with warmth | | Creative writing | 0.8 | Jokes, stories, icebreakers, tone-heavy content |
Decision Rule
Before generating any output, classify the task:
1. Is this an irreversible action (delete, send, post)?
→ temperature: 0.02. Is this scheduling, dates, or commands?
→ temperature: 0.2
3. Is this a summary or structured analysis?
→ temperature: 0.3
4. Is this a standard reply or update?
→ temperature: 0.5
5. Is this a briefing or warm message?
→ temperature: 0.6
6. Is this creative, funny, or expressive?
→ temperature: 0.8
When in doubt → 0.5
Per-Skill Recommendations
| Skill | Recommended Temp | Reason |
|---|---|---|
| owner-briefing | 0.6 | Warm, readable, but structured |
| meeting-scheduler | 0.2 | Precision required |
| ai-meeting-notes | 0.3 | Factual summaries |
| supervisor | 0.2 | Status facts only |
| billing-monitor | 0.1 | Alerts must be accurate |
| git-backup | 0.0 | No creativity needed |
| self-learning | 0.4 | Reflective but grounded |
| pa-eval | 0.3 | Analytical |
Implementation Notes
OpenClaw does not yet support per-message dynamic temperature natively.
Until it does, apply this guide by:
1. Setting temperature in your agents.defaults.models config per model
2. Or noting the recommended temperature in each skill's SKILL.md frontmatter
3. When spawning subagents for specific tasks, pass the appropriate temperature