An advanced mechanism that enables AI agents to detect user disagreement, re-evaluate original prompts, and deliver accurate corrections by refocusing on the user true intent.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install self-correction
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install self-correction using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Self-Correction Skill for AI Agents?
The self-correction skill is a sophisticated framework designed for Openclaw Skills to handle instances where an AI agent misinterprets a user request. Rather than simply tweaking the previous answer, this skill triggers a deep-dive analysis of the user's original input to identify where the misunderstanding occurred. It focuses on logical traceback, intent reconstruction, and proactive clarification.
By prioritizing the user original intention over the AI's previous output, the skill ensures that corrections are substantial and technically accurate. It is particularly effective in complex environments like coding, data processing, and technical documentation where subtle constraints can be easily overlooked in the first round of dialogue.
Self-Correction Skill for AI Agents Use Cases
- Handling direct denials when a user states the response is wrong or incorrect
- Responding to requests for re-understanding or clarification of a previous prompt
- Correcting technical stack errors such as incorrect programming languages or frameworks
- Refining output formats when a user prefers structured data over prose
- Addressing missing constraints or supplementary information provided after the first response
How Self-Correction Skill for AI Agents Works
- Detection and Identification: The agent monitors for specific linguistic triggers such as no, wrong, or wait to stop the current dialogue flow.
- Traceback: The system bypasses the previous AI response and re-examines the complete original input from the user.
- Intent Analysis: It identifies core goals, constraints (language, format, scope), and potential points of ambiguity or hidden assumptions.
- Hypothesis Generation: The agent generates 2-3 likely scenarios for what the user actually meant based on the new feedback.
- Structured Output: Based on the highest-probability hypothesis, the agent generates a tripartite response: a self-check status, a re-understanding summary, and the final corrected answer.
Self-Correction Skill for AI Agents Setup
To implement this logic within your Openclaw Skills workflow, you need to define the trigger dictionary and the response schema in your agent profile.
# Example of adding the self-correction capability to your agent
openclaw skill install self-correction --target-agent dev-assistant
Ensure that your system instructions are configured to follow the neutral correction tone, avoiding excessive apologies while focusing on intent analysis.
Self-Correction Skill for AI Agents Data Schema & Taxonomy
The skill utilizes a structured output schema to communicate its reasoning process clearly to the user. This metadata taxonomy ensures the user understands how the agent corrected its logic.
| Field | Description | Content Type |
|---|---|---|
| Self-Check | Notification of feedback detection | Status String |
| Re-understanding | Analysis of original intent and error points | Markdown List |
| New Answer | The final, optimized response | Technical Content |
Self-Correction Skill for AI Agents Advanced Features
- Multi-hypothesis intent testing for ambiguous user prompts
- Proactive clarification hooks that ask for specific missing details
- Neutral tone enforcement to maintain professional interaction without over-apologizing
- Pattern recognition to identify and avoid recurring misunderstanding types in a session
- In-place logic reconstruction that bypasses faulty previous context
SKILL.md
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