Experiment Bridge

Override: /experiment-bridge "EXPERIMENT_PLAN.md" — compact: true, base repo: https://github.com/org/project

Inputs

This skill expects one or more of:

1. refine-logs/EXPERIMENT_PLAN.md (best) — claim-driven experiment roadmap from /experiment-plan
2. refine-logs/EXPERIMENT_TRACKER.md — run-by-run execution table
3. refine-logs/FINAL_PROPOSAL.md — method description for implementation context
4. idea-stage/IDEA_CANDIDATES.md — compact idea summary (preferred when COMPACT = true) (fall back to ./IDEA_CANDIDATES.md if not found)
5. idea-stage/IDEA_REPORT.md — fallback if refine-logs don't exist (fall back to ./IDEA_REPORT.md if not found)

If none exist, ask the user what experiments to implement.

Workflow

Phase 1: Parse the Experiment Plan

Read EXPERIMENT_PLAN.md and extract:

1. Run order and milestones — which experiments run first (sanity → baseline → main → ablation → polish)
2. For each experiment block:
   • Dataset / split / task
   • Compared systems and variants
   • Metrics to compute
   • Setup details (backbone, hyperparameters, seeds)
   • Success criterion
   • Priority (MUST-RUN vs NICE-TO-HAVE)
3. Compute budget — total estimated GPU-hours
4. Method details from FINAL_PROPOSAL.md — what exactly to implement

Present a brief summary:

📋 Experiment plan loaded:
- Milestones: [N] (sanity → baseline → main → ablation)
- Must-run experiments: [N]
- Nice-to-have: [N]
- Estimated GPU-hours: [X]

Proceeding to implementation.

Phase 2: Implement Experiment Code

If BASE_REPO is set — clone the repo first:

git clone <BASE_REPO> base_repo/

For each milestone (in order), write the experiment scripts:

1. Check existing code — scan the project (or cloned base_repo/) for existing experiment scripts, model code, and data loaders. Reuse as much as possible.

2. Implement missing pieces:

   • Training scripts with proper argparse (all hyperparameters configurable)
   • Evaluation scripts computing the specified metrics
   • Data loading / preprocessing if needed
   • Baseline implementations if not already present
   • Fixed random seeds for reproducibility
   • Results saved to JSON/CSV for later analysis
   • Proper logging (wandb if configured in AGENTS.md)

3. Follow the plan's run order — implement sanity-stage experiments first, then baselines, then main method, then ablations.

4. Self-review before deploying:

   • Are all hyperparameters from EXPERIMENT_PLAN.md reflected in argparse?
   • Is the random seed fixed and controllable?
   • Are results saved in a parseable format (JSON/CSV)?
   • Does the code match FINAL_PROPOSAL.md's method description?
   • CRITICAL: does evaluation compare predictions against dataset ground truth, never another model's output?

Phase 3: Sanity Check (if SANITY_FIRST = true)

Before deploying the full experiment suite, run the sanity-stage experiment:

/run-experiment [sanity experiment command]

Wait for completion. Verify:

• Training loop runs without errors
• Metrics are computed and saved correctly
• GPU memory usage is within bounds
• Output format matches expectations

If sanity fails → fix the code, re-run. Do not proceed to full deployment with broken code.

Phase 4: Deploy Full Experiments

Deploy experiments following the plan's milestone order:

/run-experiment [experiment commands]

For each milestone:

1. Deploy experiments in parallel (up to MAX_PARALLEL_RUNS)
2. Use /monitor-experiment to track progress
3. Collect results as experiments complete

🚦 Checkpoint (if AUTO_DEPLOY = false):

🔧 Code implementation complete. Ready to deploy:

Milestone 0 (sanity): [status — passed/pending]
Milestone 1 (baseline): [N experiments, ~X GPU-hours]
Milestone 2 (main method): [N experiments, ~X GPU-hours]
Milestone 3 (ablations): [N experiments, ~X GPU-hours]

Total estimated: ~X GPU-hours on [N] GPUs

Deploy now? Or review the code first?

Phase 5: Collect Initial Results

As experiments complete:

1. Parse output files (JSON/CSV/logs) for key metrics
2. Training quality check — if W&B data is available, invoke /training-check to detect NaN, loss divergence, plateaus, or overfitting. If W&B is not configured, skip silently.
3. Update refine-logs/EXPERIMENT_TRACKER.md — fill in Status and Notes columns
4. Check success criteria from EXPERIMENT_PLAN.md — did each experiment meet its bar?
5. Write initial results summary:

# Initial Experiment Results

**Date**: [today]
**Plan**: refine-logs/EXPERIMENT_PLAN.md

## Results by Milestone

### M0: Sanity — PASSED
- [result]

### M1: Baselines
| Run | System | Key Metric | Status |
|-----|--------|-----------|--------|
| R001 | baseline_1 | X.XX | DONE |

### M2: Main Method
| Run | System | Key Metric | Status |
|-----|--------|-----------|--------|
| R003 | our_method | X.XX | DONE |

### M3: Ablations
...

## Summary
- [X/Y] must-run experiments completed
- Main result: [positive/negative/inconclusive]
- Ready for /auto-review-loop: [YES/NO]

## Next Step
→ /auto-review-loop "[topic]"

Phase 5.5: Write Compact Log (when COMPACT = true)

Skip entirely if COMPACT is false.

Append each completed experiment to EXPERIMENT_LOG.md:

## [Run ID] — [timestamp]
- **System**: [method name]
- **Config**: [key hyperparameters]
- **Result**: [primary metric = X.XX]
- **Verdict**: [positive / negative / inconclusive]
- **Reproduce**: `python train.py --config configs/run_id.yaml --seed 42`

Phase 5.6: Auto Ablation Planning

After main experiments (M2) complete with positive results, invoke /ablation-planner to design ablation studies:

• Read the main results and method description
• Generate a claim-driven ablation plan: which components to remove, what to compare, and expected outcomes
• Append ablation blocks to refine-logs/EXPERIMENT_PLAN.md and refine-logs/EXPERIMENT_TRACKER.md
• If main results are negative or inconclusive, skip ablation planning and note that in the summary

If /ablation-planner is unavailable, skip silently.

Phase 6: Handoff

Present final status:

🔬 Experiment bridge complete:
- Implemented: [N] experiment scripts
- Deployed: [N] experiments on [M] GPUs
- Completed: [X/Y] must-run, [A/B] nice-to-have
- Main result: [one sentence]

Results: refine-logs/EXPERIMENT_RESULTS.md
Tracker: refine-logs/EXPERIMENT_TRACKER.md

Ready for Workflow 2:
→ /auto-review-loop "[topic]"

Output Protocols

Follow these shared protocols for all output files:

• Output Versioning Protocol — write timestamped file first, then copy to fixed name
• Output Manifest Protocol — log every output to MANIFEST.md
• Output Language Protocol — respect the project's language setting

Key Rules

• Large file handling: If the Write tool fails due to file size, immediately retry using Bash (cat << 'EOF' > file) to write in chunks. Do NOT ask the user for permission — just do it silently.
• CRITICAL — Evaluation must use dataset ground truth. Always compare model predictions against the dataset's actual labels/targets, never another model's output. If the task has official eval scripts, prefer them.
• Follow the plan. Do not invent experiments not in EXPERIMENT_PLAN.md. If you think something is missing, note it but don't add it.
• Sanity first. Never deploy a full suite without verifying the sanity stage passes.
• Reuse existing code. Scan the project before writing new scripts. Extend, don't duplicate.
• Save everything as JSON/CSV. The auto-review-loop needs parseable results, not just terminal output.
• Update the tracker. EXPERIMENT_TRACKER.md should reflect real status after each run completes.
• Don't wait forever. If an experiment exceeds 2x its estimated time, flag it and move on to the next milestone.
• Budget awareness. Track GPU-hours against the plan's budget. Warn if approaching the limit.

Composing with Other Skills

/idea-discovery "direction"          ← Workflow 1: find + refine + plan
/experiment-bridge                   ← you are here (Workflow 1.5: implement + deploy)
/auto-review-loop "topic"            ← Workflow 2: review + iterate
/paper-writing "NARRATIVE_REPORT.md" ← Workflow 3: write the paper

Or use /research-pipeline for the full end-to-end flow (includes this bridge).