coach-improve-design

name	coach.improve.design
description	IntelligentDesign improvement iteration. Directly analyzes code and prompts, identifies a specific improvement, implements it, tests, and submits. Use when asked to "review the code", "improve the prompts", or "intelligent design".

IntelligentDesign Improvement

Directly read the code and prompts, identify a specific improvement, implement it, test, submit.

Reads architecture and alpha.0 reference from IMPROVE.md.

Steps

1. Eval Baseline

Run eval from IMPROVE.md on seed 42 to establish baseline score.

2. Analyze

Pick ONE focus area based on IMPROVE.md strategies, .coach/todos.md, and what hasn't been tried:

1. Code review: Read engine files (agent/main.py, roles.py, targeting.py, pressure.py). Look for bugs, inefficiencies, or gaps vs the alpha.0 reference in IMPROVE.md
2. Prompt review: Read _build_analysis_prompt() and _parse_analysis() in programs.py. Is the LLM seeing the right info? Could it return more than just resource_bias? Could it detect stagnation like alpha.0 does?
3. Scoring review: Read helpers/targeting.py. Are aligner_target_score and scramble_target_score well-tuned? Compare weights vs alpha.0
4. Parameter comparison: Compare constants in helpers/types.py and pressure.py against alpha.0 (e.g. RETREAT_MARGIN 15 vs 20, enemy AOE radius 4 vs 20)
5. Architecture improvement: Read cvc_policy.py. Is the LLM feedback loop working? Could the analyze program influence more than mining? Could it adjust role allocation or targeting?

3. Implement

Make a focused, isolated change. Write the code directly.

• Prompt improvements: modify _build_analysis_prompt() or _parse_analysis() in programs.py
• Code improvements: modify the relevant engine file in agent/
• Parameter changes: modify helpers/types.py or the relevant mixin
• New programs: add to programs.py and wire into all_programs()

4. Test Across Seeds

Run eval across 5+ seeds. If average score drops vs baseline, revert.

5. Submit if Improved

Use the submit command from IMPROVE.md. Log the submission version.

Output

Report back with:

• improved: whether scores improved
• score_before: baseline average
• score_after: post-change average (or null if reverted)
• focus: which area was analyzed (code/prompt/scoring/parameter/architecture)
• description: what changed and why