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experiment-analyst

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Diagnose why agent experiments succeed or fail.

Data & Analytics#ai agents#sqlite#tool usage#experiment analysis#failure modes#success determinants#workflow reconstruction
Authordanicat
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill helps you understand why AI agents succeed or fail in specific experiment runs by turning raw run logs into evidence-backed success determinants and behavioral patterns.

Core Features & Use Cases

  • Evidence-based experiment deconstruction: Extracts performance overview, tool usage breakdown, and top failure signals from a Tenkai experiments SQLite database.
  • Success determinants via tool correlation: Identifies tools that correlate with higher success or failure rates by comparing tool usage across successful vs failed runs.
  • Targeted behavioral deep dives: Reconstructs run workflows (message/tool steps) to compare a winning pattern against a failure loop for specific alternatives.

Quick Start

Run the analysis for experiment ID 12 by executing: python3 agents/tenkai/.gemini/skills/experiment-analyst/scripts/analyze_experiment.py 12

Dependency Matrix

Required Modules

None required

Components

scriptsreferences

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: experiment-analyst
Download link: https://github.com/danicat/skills/archive/main.zip#experiment-analyst

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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