research

arturh85's avatarfrom arturh85

Run structured research experiments for Sunaba creature evolution. Use for creating experiments, investigating problems, making code changes, running training, and tracking progress across physics, morphology, neural, chemistry, and integration work.

⭐1stars🔀0forks📁View on GitHub🕐Updated Jan 3, 2026
[Experiment Analysis]

When & Why to Use This Skill

This Claude skill facilitates systematic research and structured experimentation for Sunaba creature evolution. It empowers developers to automate the creation, execution, and tracking of complex experiments across physics, morphology, neural networks, and chemistry, ensuring a data-driven and organized approach to simulation development and AI training.

Use Cases

  • Automated Experiment Generation: Quickly create detailed experiment templates, hypotheses, and documentation based on specific project goals or automated suggestions from current progress files.
  • Systematic Code & Physics Testing: Investigate how changes in gravity, friction, or joint limits affect creature behavior through structured 'code-change' and 'investigation' workflows.
  • Evolutionary Training Management: Execute and monitor training runs to establish baselines, analyze neural network architectures, and evaluate fitness improvements in various scenarios like locomotion or survival.
  • Research Progress Tracking: Maintain a centralized 'PROGRESS.md' log to visualize iteration chains, highlight current phase goals, and prioritize future research areas based on historical data.
nameresearch
descriptionRun structured research experiments for Sunaba creature evolution. Use for creating experiments, investigating problems, making code changes, running training, and tracking progress across physics, morphology, neural, chemistry, and integration work.
allowed-toolsRead, Grep, Glob, Write, Edit, Bash(cargo run:*), Bash(just:*), Bash(time:*), Bash(RUST_LOG=*:*), Bash(cargo test:*), Bash(cargo build:*)

Research Experiments Skill

Systematic research for Sunaba creature evolution across all systems.

Research Areas

Area What Changes How to Test
Physics Gravity, damping, friction, thrust, joints Training runs, observe behavior
Morphology Body generation, part sizes, joint limits Training runs, visual inspection
Neural Network architecture, sensors, inputs Training runs, behavior analysis
Chemistry Reactions, materials, world generation Manual testing, emergent behavior
Integration How systems work together In-game testing with creatures

Commands

/research [<goal>]

Create new experiments or suggest experiments based on current progress.

Auto-detect mode:

  • With <goal> argument: Create experiment from goal description
  • Without arguments: Suggest 2-5 experiments based on PLAN.md and PROGRESS.md

What I'll do (create mode - with goal):

  1. Parse the goal (often from PLAN.md or user request)
  2. Determine research area and appropriate template
  3. Generate descriptive experiment name
  4. Create experiments/YYYY-MM-DD-<name>.md (not inside .claude/skills, at the top level!)
  5. Add entry to experiments/PROGRESS.md

What I'll do (suggest mode - no arguments):

  1. Read PLAN.md for current phase goals
  2. Read experiments/PROGRESS.md for completed work
  3. Analyze what hasn't been tried yet
  4. Propose 2-5 experiments with:
    • Goal and hypothesis
    • Research area (physics/morphology/neural/etc.)
    • What we expect to learn
    • Estimated effort
  5. Ask which ones to create

Examples:

/research                                              # Suggest mode
/research "creatures should move >100px consistently"  # Create mode
/research "investigate why creatures don't use their legs"
/research "test higher joint angular velocity"

/research status

Show overview of all experiments.

What I'll do:

  1. Read experiments/PROGRESS.md
  2. List experiments by status
  3. Show iteration chains
  4. Highlight current phase goals from PLAN.md

Templates

Choose based on experiment type:

Template Use When
code-change.md Modifying game systems (physics, chemistry, morphology, neural)
training-run.md Running training to test changes or establish baseline
investigation.md Understanding a problem before trying fixes
iteration.md Building on previous experiment results

Workflow

Investigation Flow

  1. /research "understand why X happens"
  2. Read code, run tests, observe behavior
  3. Document findings in experiment file
  4. Propose fixes as new experiments

Code Change Flow

  1. /research "try changing X to improve Y"
  2. Make code changes
  3. Run training or manual tests
  4. Document results
  5. Keep or revert changes

Training Flow

  1. Make code changes (or use current state)
  2. /research "establish baseline for X"
  3. Run just train-quick or full training
  4. Analyze results manually
  5. Create iteration with /research

Reference

Training Commands

just train-quick              # Fast validation (~1 min), outputs to training_output/
time just train-quick         # With timing

# Custom scenario/parameters (still uses training_output/)
just train scenario="locomotion" generations="100" population="50"

Scenarios

locomotion, simple, foraging, survival, balanced, parcour

Key Files

Area Key Files
Physics creature.rs, physics.rs, morphology.rs
Morphology morphology.rs, genome.rs
Neural neural.rs, sensors.rs
Training headless/training_env.rs, headless/scenario.rs
Fitness headless/fitness.rs