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fluxloop-evaluate

Analyze AI agent test results and generate improvement suggestions with file:line references.

9|Updated Sep 26, 2025
Search Tags:#debugging#iterative development#evaluate#agent improvement#analyze results#test insights
One-click install
npx skills add https://github.com/Fluxloop-AI/fluxloop --skill fluxloop-evaluate
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fluxloop-evaluate
Source: https://github.com/Fluxloop-AI/fluxloop/tree/main/fluxloop-plugin/skills/evaluate
Command: npx skills add https://github.com/Fluxloop-AI/fluxloop --skill fluxloop-evaluate

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the analysis of test results, identifies key insights and failure patterns, and suggests actionable improvements to AI agents, streamlining the iterative development and refinement process.

Core Features & Use Cases

  • Automated Evaluation: Runs server-side evaluations on test outcomes.
  • Insight Generation: Extracts key findings, strengths, and weaknesses from test runs.
  • Improvement Suggestions: Recommends specific code or prompt modifications based on analysis.
  • Iterative Loop: Facilitates a continuous test-evaluate-fix-retest cycle.
  • Use Case: After running a suite of tests for a customer support agent, this skill analyzes the results to pinpoint why certain refund requests failed and suggests specific changes to the agent's logic to handle those cases correctly.

Quick Start

Evaluate the results of the last test run to identify areas for agent improvement.

Frequently Asked Questions about fluxloop-evaluate

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

FAQPage Schema
How do I analyze AI agent test results to find failure patterns?▼

To analyze AI agent test results, you run server-side evaluations that extract failure patterns and generate actionable insights for agent improvement. This process pinpoints specific weaknesses and recommends targeted code modifications.

What is an iterative test-evaluate-fix-retest loop for agent development?▼

An iterative test-evaluate-fix-retest loop is a continuous agent refinement cycle where evaluations analyze test outcomes, suggest code modifications, and validate fixes through retesting. This loop streamlines development by automating insight generation after each test run.

How do I get specific code modification suggestions for failing AI agent tests?▼

To get specific code modification suggestions for failing AI agent tests, you evaluate the test run to analyze failure patterns. The evaluation provides actionable insights and recommends changes with file:line references for precise agent improvement.

Can I synchronize local test memory with server-side evaluations?▼

Yes, you can synchronize local test memory with server-side evaluations. This synchronization ensures that local test outcomes are evaluated consistently on the server, generating unified insights and improvement suggestions for your AI agents.

Does this evaluation skill work without external dependencies?▼

Yes, this evaluation skill works without external dependencies. It operates independently using internal scripts and references to analyze test results, identify failure patterns, and generate actionable insights for agent improvement.

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