reasoning-skill advanced active

Adaptive Reasoning Skill

Complex tasks need chain-of-thought. Simple ones don't. Your agent applies the same strategy to both — badly.

What breaks without openclaw adaptive reasoning skill

Fixed reasoning patterns applied to all tasks. Over-thinking simple queries. Under-thinking complex multi-step problems.

Task-appropriate reasoning × adaptive CoT/ToT/direct strategy selection ÷ 15-minute install ÷ no custom reasoning code = smarter responses at the right depth.

openclaw adaptive reasoning skill — what it actually does

01
Analyzes each task's complexity before selecting a reasoning strategy.
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Applies chain-of-thought for multi-step, tree-of-thought for decision trees.
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Returns direct responses for simple queries without unnecessary overhead.
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Drops into any OpenClaw agent as a single SKILL.md file — no code changes.
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Serves as a production reference for building custom meta-reasoning skills.

Security check — openclaw adaptive reasoning skill

Privacy score: 7/10 — accesses connected platform APIs only. Lock it: review OAuth scopes before install, confirm OpenClaw ≥1.3; requires LLM provider with ≥32k context window recommended compatibility.

Quick start — openclaw adaptive reasoning skill in 15 minutes

Setup time: 15 minutes

!
You need:
  • OpenClaw core with skills support
  • access to an LLM with strong reasoning capabilities

Install the package:

git clone https://github.com/openclaw/skills
cp -r skills/enzoricciulli/adaptive-reasoning /your-bot/skills/
1
Clone the openclaw/skills repo
2
Copy the adaptive-reasoning/ directory to your bot's skills/ folder
3
Review SKILL.md for trigger conditions and configuration parameters
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Configure the reasoning_depth and fallback_strategy parameters
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Restart OpenClaw
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Test with a complex multi-step problem that requires planning

Troubleshooting openclaw adaptive reasoning skill

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1. Using this skill for simple tasks — it adds reasoning overhead that's unnecessary for simple commands
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2. Not configuring reasoning_depth — defaults may be too shallow for complex agent tasks
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3. Pairing with a weak LLM — adaptive reasoning benefits significantly from stronger models

Compatibility & status

Works with: OpenClaw ≥1.3; requires LLM provider with ≥32k context window recommended advanced Last updated: Nov 2025 ★ N/A on GitHub MIT

Official docs →

View on GitHub →

FAQ — openclaw adaptive reasoning skill

What LLM works best with this skill?

Claude or GPT-4-class models produce the best adaptive reasoning.

Can I add my own reasoning strategies to the strategy library?

Yes. The strategy library is defined in the SKILL.md config block.

Does this skill maintain state across turns?

Yes, within a session.

Related — more like openclaw adaptive reasoning skill

Agents that over-explain simple answers and under-reason complex ones lose user trust fast.

A fixed reasoning strategy is a ceiling every sophisticated user will hit.

Get it on GitHub →