computational-experiments

# Computational Experiments > Lifecycle skill for algorithmic research projects where the code IS the scientific contribution. ## Modes | Mode | What it does | Phases | |------|-------------|--------| | **Scaffold** | Create/audit package structure + algorithm skeleton | 1–2 | | **Experiment** | Design and run pre-specified sweep campaigns | 1, 3–4 | | **Explore** | Adaptive experiment loop: modify → run → evaluate → keep/discard | 1, 3E–4 | | **Autonomous** | Parallel self-correcting sweep with sub-agents | 1, 3A–4 | | **Figures** | Generate publication output from results | 1, 4 | | **Full** | Complete pipeline | 1–5 | Default: **Full**. Detect mode from user request or ask if ambiguous. ### `--scaffold` Flag Sets a **stage progression template** for the experiment campaign. Templates provide structured checklists and exit criteria for each stage. | Scaffold | Stages | Best for | |----------|--------|----------| | `standard` | Init → Tune → Creative → Ablate | Algorithm development, ML, simulation | | `robustness` | Main spec → Alternatives → Placebo → Sensitivity | Causal inference, econometrics | | `replication` | Exact → Our data → Extensions → Robustness | Replicate-and-extend papers | Templates live in `templates/experiments/`. When a scaffold is active: 1. Present the current stage's checklist before starting work 2. Gate progression: don't move to the next stage until exit criteria are met 3. Log stage transitions in the experiment breadcrumb Default (no fla...