Dynamic Temperature

提供 LLM temperature 选择指南：按不可逆操作、排期、分析、日常沟通、简报草拟、创意写作等任务类型给出 0.0–0.8 的建议取值与判断顺序，列出若干技能的建议温度，并规定发送消息、删除数据等不可逆动作须先确认。内容为文字指导，无脚本或工具调用。

netanel-abergel

热度 53.02

最近更新 2026-05-11 17:39:22

分类：知识依赖：纯提示词应用场景：工作流 / 模板

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add dynamic-temperature

Metadata

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: 0.0

2. 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:

Setting temperature in your agents.defaults.models config per model
Or noting the recommended temperature in each skill's SKILL.md frontmatter
When spawning subagents for specific tasks, pass the appropriate temperature

Communication Override Rules (Temperature 0.0 absolute)

Sending messages to people → always confirm before sending (irreversible)
Deleting data → always confirm
"sure thing" reply → exact string, no creativity, temperature 0.0
Reaction signals (👍, ✅) → deterministic, no variation

Learned From

Training session between Heleni (Netanel's PA) and Selena (Daniel's PA), April 2026. Key insight from Selena: irreversible actions = 0.0, no exceptions.