experiment-controller git:20260506.86420c0A

Immutable. This exact content is served forever at /api/v1/blob/80b6f77e2d4daddf.

---
name: experiment-controller
description: "Use this skill whenever the user wants to execute experiments based on a finalized method and record results. Triggers include: 'run experiment', 'experiment controller', 'implement experiment', 'run model', 'execute training', 'experiment-controller', 'record results', 'ablation study', or any request to turn METHOD.md into concrete runs and output to EXPERIMENT.md. This skill is the **mandatory interface-layer experiment executor** in NeuroClaw: it searches literature/GitHub for matching experimental setups and codebases, proposes one scheme + repo after user discussion, uses git skills to download and setup, runs the experiment(s), and iteratively appends every result + observation to EXPERIMENT.md."
license: MIT License (NeuroClaw custom skill – freely modifiable within the project)
layer: interface
skill_type: workflow
dependencies:
  - claw-shell
  - dependency-planner
  - git-essentials
---
# Experiment Controller
## Overview
This skill implements the **Literature/GitHub Search → Scheme Confirmation → Git Execution → Iterative Logging** process for the NeuroClaw experiment-controller phase.

It acts as the Experiment Manager within the multi-agent framework:
- Reads the latest **IDEA.md** and **METHOD.md** from the workspace.
- Searches recent literature (multi-search-engine, arxiv-search, pubmed-search) and GitHub for reproducible experimental setups and open-source repositories that match the proposed architecture.
- Summarizes candidate schemes (hyperparameters, datasets, baselines, training protocols) and proposes the most suitable GitHub repo.
- Iteratively discusses with the user to confirm the exact scheme/repo.
- After confirmation: uses git-essentials/git-workflows to clone, dependency-planner to install environment, claw-shell to run the experiment (training/inference/ablation).
- After every run (or ablation), automatically records: setup details, metrics, logs, observations, and any issues.
- Saves everything in **EXPERIMENT.md** (with dated sections for each run).

**Research use only** — the output is a complete, reproducible EXPERIMENT.md ready for paper-writing and future replication.

## Quick Reference (Experiment Flow)
| Step | Description                              | Output File               |
|------|------------------------------------------|---------------------------|
| 1. Read & Parse | Load IDEA.md + METHOD.md                | 01_idea_method_summary.md |
| 2. Literature & GitHub Search | Find matching setups & repos            | 02_search_results.md      |
| 3. Proposal | Recommend best scheme + repo            | 03_proposal.md            |
| 4. User Discussion | Confirm scheme/repo                     | 04_discussion.md          |
| 5. Git Clone & Setup | Clone + install dependencies            | 05_setup_log.md           |
| 6. Run Experiment | Execute training/inference/ablation     | 06_run_log_*.md (per run) |
| 7. Record Results | Append metrics + observations           | EXPERIMENT.md             |

## Harness Engineering Protocol (Mandatory for Reproducibility)

All experiments executed via experiment-controller **must** follow the **Task Decomposition → Agent Initialization → Execution → Verification** protocol to ensure self-validation, auditability, and resumability.

### Phase 1: Task Decomposition
- Parse experimental goal from METHOD.md into discrete tasks (data preprocessing, feature extraction, model training, inference, evaluation)
- Define success criteria for each task (BIDS format compliance, feature vector shape/range, metric thresholds)
- Identify cross-task dependencies and data flow
- Output: `experiment_task_manifest.json` with task graph

### Phase 2: Agent Initialization
- **Pre-flight checks**: verify all dependencies installed, data paths accessible, Docker/GPU resources available (if required)
- **Environment snapshot**: capture Python version, library versions, hardware specs, random seeds → `environment_manifest.json`
- **Checkpoint management**: determine checkpoint frequency, rollback strategy, memory constraints
- **Logging setup**: initialize structured logging with unique experiment session ID (timestamp + hash)

### Phase 3: Execution with Incremental Logging
- Execute each task with **real-time progress tracking**
- Save checkpoint after each completed task (enables resumption on failure)
- Log detailed metrics, intermediate outputs, and timing information
- Generate cryptographic hash (SHA256) for each output artifact
- Stream results to EXPERIMENT.md in real-time sections with timestamps

### Phase 4: Verification and Post-Execution Validation
- **Self-verification checks** (module-specific):
  - Preprocessing: verify BIDS compliance, check for NaN/Inf values, validate normalization ranges
  - Feature extraction: check output shape consistency, verify statistical properties (mean/std within expected bounds)
  - Model training: validate loss curve smoothness, check for NaN gradients, verify train/val split integrity
  - Inference: cross-check predictions for domain-specific constraints (probability bounds, anatomical plausibility)
- **Result integrity validation**:
  - Recompute hash of all output files and compare with stored values
  - Flag any mismatches as potential corruption/tampering
- **Generate final audit report**: `experiment_audit_report.md` with task execution times, success/failure status, verification results, and reproducibility metadata

### Self-Verification Implementation Details

Each skill integrated into experiment-controller must include **automatic validation steps**:

**Preprocessing validation**:
```
- BIDS compliance: confirm file naming, JSON sidecars, required fields
- Data integrity: NaN/Inf count, range of pixel values, histogram sanity check
- Statistical bounds: mean/std within neuroimaging norms (e.g., T1w intensity ~0-4000 HU)
```

**Feature extraction validation**:
```
- Output shape check: row/column count match expected dataset size
- Distribution check: ensure features are not constant or degenerate
- Correlations: detect and warn on features with >0.95 mutual correlation
```

**Training validation**:
```
- Loss curve smoothness: flag sudden spikes or plateau too early
- Gradient health: ensure no NaN/Inf gradients during backprop
- Validation metric monotonicity (for early stopping): warn if validation improves inconsistently
- Train/val split: cross-verify split ratio and no subject leakage
```

**Inference validation**:
```
- Output shape consistency: predictions match expected target cardinality
- Domain constraints: probability predictions in [0,1], regression outputs within physiologically plausible ranges
- Batch effect check: compare results across batch sizes (should be near-identical with same seed)
```

## Installation
```bash
# Place files in: skills/experiment-controller/
```

## Important Notes & Limitations
- Always starts from latest IDEA.md + METHOD.md; stops and prompts if missing.
- Every step and every run is saved as numbered Markdown files for full transparency and resumption.
- Git clone uses git-essentials/git-workflows (never manual commands outside skills).
- Dependencies are handled exclusively by dependency-planner.
- Multiple runs (e.g., ablations) are supported; results are appended with timestamps.
- Final output always saved/updated as `EXPERIMENT.md` in workspace root.
- Logs include: command executed, hyperparameters, metrics (accuracy, loss, Dice, etc.), runtime, observations, and any errors.

## When to Call This Skill
- Immediately after method-design completes METHOD.md
- When the user wants to run, reproduce, or compare experiments
- Before paper-writing (to populate quantitative results)

## Complementary / Related Skills
- `research-idea` → provides IDEA.md
- `method-design` → provides METHOD.md (input)
- `multi-search-engine` → literature & GitHub search
- `git-essentials` / `git-workflows` → clone and manage repositories
- `dependency-planner` → environment & dependency installation
- `claw-shell` → internal execution of training scripts
- `paper-writing` → consumes EXPERIMENT.md for results/tables

## Reference
NeuroClaw architecture (section 1.7 experiment-controller skill).  
Flow: literature/GitHub search → user-confirmed scheme → git clone → dependency setup → iterative execution → EXPERIMENT.md logging.  

---
Created At: 2026-03-24 00:00 HKT
Last Updated At: 2026-04-05 02:01 HKT
Author: chengwang96