Analyze outputs to identify root causes and create improvement plans.
npx skills add https://github.com/AX661s/openclaw-metacog-template --skill self-analyze
Please help me install this Agent Skill. Skill: Self-Analyze Source: https://github.com/AX661s/openclaw-metacog-template/tree/main/skills/self-analyze Command: npx skills add https://github.com/AX661s/openclaw-metacog-template --skill self-analyze
This skill enables deliberate, structured analysis of your own outputs to identify and implement improvements.
After analyzing outputs, run the Self-Analyze skill to start a guided review and produce a structured improvement plan.
High-intent search queries and answers about installing and using this skill.
Structured self-analysis is a repeatable framework to identify and fix quality issues in outputs through evidence gathering, root-cause analysis, and pattern recognition to reduce repeated mistakes. It closes the learning loop by producing modification logs and verification plans.
Perform root-cause analysis on AI errors by running a structured post-output review to gather data, analyze patterns, and generate an improvement proposal. This process logs modifications and creates a verification plan to prevent recurring mistakes.
Yes, you can schedule automated self-analysis for ongoing quality assurance across tasks. The framework supports periodic heartbeats or triggered reviews to continuously identify output issues and apply structured modifications.
The best way to track and correct recurring output errors is by maintaining a modification log with a verification plan. This structured approach uses pattern analysis to identify root causes and implement targeted improvements.
Yes, a data-gathering protocol is required before starting meta-cognition reviews. This protocol provides the necessary evidence and output history to accurately perform root-cause analysis and identify recurring quality patterns.
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