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Dynamic Temperature

Name: Dynamic Temperature
Author: netanel-abergel

作者 Netanel Abergel · GitHub ↗ · v1.0.2 · MIT-0

cross-platform ✓ 安全检测通过

139

总下载

当前安装

版本数

在 OpenClaw 中安装

/install dynamic-temperature

功能描述

Dynamic LLM temperature selection based on task type. Use when deciding what temperature to apply for a given task — scheduling, communication, creative writ...

使用说明 (SKILL.md)

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.

安全使用建议

This skill is an advisory guideline for choosing temperature and appears safe: it asks for nothing sensitive and installs nothing. Before relying on it, consider: (1) the skill is instruction-only and will only take effect when invoked—if you want these rules applied to every model call, put them in your agent core identity (SOUL.md) or implement them in platform configuration; (2) the guide recommends confirming irreversible actions at temperature 0.0 — ensure your agent actually enforces a confirmation step rather than auto-sending; and (3) verify the per-skill names in the table match your deployed skills so recommendations are applied where intended.

能力评估

✓ Purpose & Capability

The name/description match the content: the SKILL.md contains temperature rules and per-skill recommendations. Nothing in the files requires access to unrelated services or secrets.

ℹ Instruction Scope

The instructions stay on-topic (classify task type and choose a temperature). Implementation notes suggest updating agents.defaults.models or passing temperature to subagents — this touches agent configuration and runtime behavior but is reasonable for the stated goal. The guide also mandates confirmations for irreversible actions; users should verify that confirmation steps are actually implemented by the agent platform.

✓ Install Mechanism

No install spec and no code files—this is instruction-only, so nothing is downloaded or written to disk by the skill itself.

✓ Credentials

No required environment variables, credentials, or config paths are requested. The skill only recommends configuration changes and temperature values; it does not ask for secrets or external endpoints.

✓ Persistence & Privilege

always is false and model invocation is allowed (default). The skill does not request permanent presence or modification of other skills' configurations. Note: DEPRECATED.md states this logic may be more appropriate in a core identity file (SOUL.md) if the user wants the rule applied universally.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install dynamic-temperature
安装完成后，直接呼叫该 Skill 的名称或使用 /dynamic-temperature 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v1.0.2

- Added "Communication Override Rules" section clarifying that certain communication tasks (e.g., sending messages, deleting data, exact replies) must use temperature 0.0 absolutely. - Specified that reaction signals and set-phrase replies are always deterministic (temperature 0.0). - No changes to recommendations or decision rules; documentation expanded for stricter application in sensitive communication scenarios.

v1.0.1

reactions rule, close-the-loop, reply-to rules; skill-master analytics hook; skill-analytics added

v1.0.0

- Initial release enabling dynamic selection of LLM temperature based on task type. - Provides a clear temperature scale and decision rules for task classification (irreversible actions, scheduling, summaries, communication, and creativity). - Includes per-skill temperature recommendations for commonly used assistant skills. - Offers interim implementation guidance for platforms without native dynamic temperature support. - Focuses on balancing precision and creativity to improve output quality and reliability.

元数据

Slug dynamic-temperature

版本 1.0.2

许可证 MIT-0

累计安装 1

当前安装数 1

历史版本数 3

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