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improve-agent

Refine AI agent role templates using execution feedback and behavioral evidence.

1|Updated May 6, 2022
Search Tags:#automation#template#feedback#refinement#optimization#agent
One-click install
npx skills add https://github.com/cloud-native-tools/cws-lib-bash --skill improve-agent-cloud-native-tools
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: improve-agent
Source: https://github.com/cloud-native-tools/cws-lib-bash/tree/main/.specify/skills/improve-agent
Command: npx skills add https://github.com/cloud-native-tools/cws-lib-bash --skill improve-agent-cloud-native-tools

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill resolves performance issues, behavioral drift, and output quality problems in custom AI agents and role templates by applying evidence-based refinements.

Core Features & Use Cases

  • Targeted Refinement: Updates specific sections of role templates or supervision snippets based on real-world execution feedback.
  • Evidence-Based Optimization: Uses collected logs and failure reports to ensure changes are grounded in actual performance data rather than generic best practices.
  • Use Case: If an agent consistently fails to follow a specific workflow step or produces incorrect output formats, use this skill to analyze the failure evidence and apply a precise, minimal fix to the underlying template.

Quick Start

Invoke the improve-agent skill to analyze the recent execution failures of the researcher agent and apply the necessary template corrections.

Frequently Asked Questions about improve-agent

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fix an AI agent that keeps failing to follow workflow steps?▼

You can resolve AI agent behavioral drift by applying evidence-based refinements to role templates. This skill analyzes execution feedback and failure reports to target specific sections of agent definitions, ensuring changes are grounded in actual performance data rather than generic best practices.

What is evidence-based optimization for AI agent role templates?▼

Evidence-based optimization for AI agent role templates involves refining agent definitions by analyzing execution feedback and behavioral evidence. This process targets specific sections of the template to resolve performance issues and output quality defects using actual failure data.

How do I correct output format defects in a custom AI agent template?▼

Correcting output format defects involves analyzing execution feedback to apply precise, minimal fixes to specific sections of the agent's role template. This targeted refinement resolves the quality issues while maintaining established template structures and handoff consistency.

Can I update specific sections of a supervision snippet without breaking handoff consistency?▼

Yes, updating specific sections of supervision snippets requires adherence to established template structures to maintain handoff consistency. Targeted refinements resolve performance issues and output quality defects while preserving the integrity of agent definitions and workflow handoffs.

When should I use targeted refinement instead of rewriting an entire agent role template?▼

Use targeted refinement when execution feedback shows consistent failures in specific workflow steps or output formats. Applying precise, minimal fixes to specific template sections resolves the exact behavioral drift without the overhead and risk of rewriting the entire agent definition.

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