| name | experiment-analyst |
| description | Analyze Tenkai agent experiments to find success factors, failure modes, and run patterns. |
Experiment Analyst
This skill helps analyze Tenkai agent runs to find why they succeed or fail.
Core Rules
- Use data to back up all claims. Cite run IDs, exact errors, or metric differences.
- Do not mix up correlation with causation. For example, if failed runs often call
read_file, it may be because agents use it to recover from errors, not because the tool itself is bad.
- Compare how different setups perform.
Setup & Database
Set the database path before you run any tools or scripts:
export TENKAI_DB_PATH=agents/tenkai/experiments/tenkai.db
references/tenkai_db_schema.md: Database structure details.scripts/analyze_experiment.py: Main analysis script for stats, tool usage, and success drivers.scripts/analyze_patterns.py: Script to rebuild agent workflows.scripts/get_experiment_config.py: Script to get the experiment settings.
Analysis Steps
1. View the Settings
Check the configuration first:
python3 agents/tenkai/.gemini/skills/experiment-analyst/scripts/get_experiment_config.py <EXP_ID>
Look at what changed (like models, prompts, or tools) and what you expect to see.
2. View the Stats
Run the main script to get general metrics:
python3 agents/tenkai/.gemini/skills/experiment-analyst/scripts/analyze_experiment.py <EXP_ID>
Check "Success Determinants Analysis" to see which tools drive success or signal failure. Also find the most common errors.
3. Trace Run Patterns
Pick specific runs to study why they succeeded or failed:
python3 agents/tenkai/.gemini/skills/experiment-analyst/scripts/analyze_patterns.py <EXP_ID> "<ALTERNATIVE>"
- Look at successful runs. Did a specific tool prevent a bug?
- Look at failed runs. Did a tool run in an endless loop?
- Find how agents handle errors. Check if they read or edit files after a failure, or if they repeat the same mistake.
Report Format
Experiment X: [Name]
Overview
Brief summary of the experiment goals and the tested setups.
Results
| Alternative | Success Rate | Duration | Tokens | Key Feature |
|---|
| Variant A | ... | ... | ... | ... |
Drivers & Signals
- Drivers: Tools and patterns that increased success rates.
- Signals: Tools and patterns linked to failure.
Detailed Workflow
- Successful pattern: The steps taken in good runs. Example:
project_init created folders and prevented path errors.
- Failure pattern: The steps taken in bad runs. Example: repeating a command on a file that does not exist.
Conclusion & Actions
- Best Variant: Which setup is better.
- Recommended Changes: Steps to fix the prompt or tools.
