| name | phase-brain-optimize |
| description | Phase guidance for the neuroflow /brain-optimize command. Loaded automatically when /brain-optimize is invoked to orient agent behavior, relevant skills, and workflow hints for parameter search and model fitting. |
phase-brain-optimize
The brain-optimize phase covers parameter exploration and model fitting — sweeping a parameter space to map model behaviour, or optimising parameters to match experimental data.
Approach
- Identify the goal early: exploratory sweep vs targeted fitting to experimental data
- Write
optimize-plan.md before any code — parameters, bounds, cost function, algorithm, convergence criterion
- Always run a minimal smoke test (2–3 evaluations) before launching a full search
- Prefer algorithms suited to the problem: grid search for low-dimensional spaces; differential evolution or Bayesian optimisation for high-dimensional or expensive simulations
Relevant skills
neuroflow:neuroflow-core — read first; defines the command lifecycle and .neuroflow/ write rules
Workflow hints
- All optimisation scripts and raw results go to
output_path (models/optimize/), not inside .neuroflow/
- Save
optimize-plan.md and post-run summaries to .neuroflow/brain-optimize/
- Log algorithm choice and cost function rationale in
.neuroflow/reasoning/brain-optimize.json
- Common libraries: DEAP, Optuna, scipy.optimize, BluePyOpt, scikit-optimize
Slash command
/neuroflow:brain-optimize — runs this workflow as a slash command.
