experiment-plan

name	experiment-plan
description	Turn a refined research proposal or method idea into a detailed, claim-driven experiment roadmap. Use after `research-refine`, or when the user asks for a detailed experiment plan, ablation matrix, evaluation protocol, run order, compute budget, or paper-ready validation that supports the core problem, novelty, simplicity, and any LLM / VLM / Diffusion / RL-based contribution.
allowed-tools	Bash(*), Read, Write, Edit, Grep, Glob, WebSearch, WebFetch, Agent

Experiment Plan: Claim-Driven, Paper-Oriented Validation

Refine and concretize: $ARGUMENTS

Overview

Use this skill after the method is stable enough that the next question becomes: what exact experiments should we run, in what order, to defend the paper? If the user wants the full chain in one request, prefer /research-refine-pipeline.

The goal is not to generate a giant benchmark wishlist. The goal is to turn a proposal into a claim -> evidence -> run order roadmap that supports four things:

1. the method actually solves the anchored problem
2. the dominant contribution is real and focused
3. the method is elegant enough that extra complexity is unnecessary
4. any frontier-model-era component is genuinely useful, not decorative

Constants

• OUTPUT_DIR = refine-logs/ — Default destination for experiment planning artifacts.
• MAX_PRIMARY_CLAIMS = 2 — Prefer one dominant claim plus one supporting claim.
• MAX_CORE_BLOCKS = 5 — Keep the must-run experimental story compact.
• MAX_BASELINE_FAMILIES = 3 — Prefer a few strong baselines over many weak ones.
• DEFAULT_SEEDS = 3 — Use 3 seeds when stochastic variance matters and budget allows.

Workflow

Phase 0: Load the Proposal Context

Read the most relevant existing files first if they exist:

• refine-logs/FINAL_PROPOSAL.md
• refine-logs/REVIEW_SUMMARY.md
• refine-logs/REFINEMENT_REPORT.md

Extract:

• Problem Anchor
• Dominant contribution
• Optional supporting contribution
• Critical reviewer concerns
• Data / compute / timeline constraints
• Which frontier primitive is central, if any

If these files do not exist, derive the same information from the user's prompt.

Phase 1: Freeze the Paper Claims

Before proposing experiments, write down the claims that must be defended.

Use this structure:

• Primary claim: the main mechanism-level contribution
• Supporting claim: optional, only if it directly strengthens the main paper story
• Anti-claim to rule out: e.g. "the gain only comes from more parameters," "the gain only comes from a larger search space," or "the modern component is just decoration"
• Minimum convincing evidence: what would make each claim believable to a strong reviewer?

Do not exceed MAX_PRIMARY_CLAIMS unless the paper truly has multiple inseparable claims.

Phase 2: Build the Experimental Storyline

Design the paper around a compact set of experiment blocks. Default to the following blocks and delete any that are not needed:

1. Main anchor result — does the method solve the actual bottleneck?
2. Novelty isolation — does the dominant contribution itself matter?
3. Simplicity / elegance check — can a bigger or more fragmented version be avoided?
4. Frontier necessity check — if an LLM / VLM / Diffusion / RL-era component is central, is it actually the right tool?
5. Failure analysis or qualitative diagnosis — what does the method still miss?

For each block, decide whether it belongs in:

• Main paper — essential to defend the core claims
• Appendix — useful but non-blocking
• Cut — interesting, but not worth the paper budget

Prefer one strong baseline family over many weak baselines. If a stronger modern baseline exists, use it instead of padding the list.

Phase 3: Specify Each Experiment Block

For every kept block, fully specify:

• Claim tested
• Why this block exists
• Dataset / split / task — also state data provenance (existing = an existing dataset used as-is, adapted = an existing dataset transformed/relabeled/filtered, constructed = built from scratch / synthetic), the source (dataset name, or construction method when constructed), the total available size of that source (available_n), and the planned amount this project will actually use (used_n). When used_n < available_n, state how/why it is subsetted. Budget-gate the subset: subset only when a scientific reason applies (a ladder-of-evidence screen, or the full set genuinely exceeds the declared GPU / compute budget) — do not subset merely to save cost when task.md declares a budget that covers the full available_n; in that case plan used_n = available_n at the user's named model size. A declared, generous budget is a mandate to run at fuller scale, not just an upper bound (see /auto-experiment GPU-budget rule).
• Compared systems: strongest baselines, ablations, and variants only
• Metrics: decisive metrics first, secondary metrics second
• Setup details: backbone, frozen vs trainable parts, key hyperparameters, training budget, seeds
• Success criterion: what outcome would count as convincing evidence?
• Failure interpretation: if the result is negative, what does it mean?
• Table / figure target: where this result should appear in the paper

Special rules:

• A simplicity check should usually compare the final method against either an overbuilt variant or a tempting extra component that the paper intentionally rejects.
• A frontier necessity check should usually compare the chosen modern primitive against the strongest plausible simpler or older alternative.
• If the proposal is intentionally non-frontier, say so explicitly and skip the frontier block instead of forcing one.

Phase 4: Turn the Plan Into an Execution Order

Build a realistic run order so the user knows what to do first.

Use this milestone structure:

1. Sanity stage — data pipeline, metric correctness, one quick overfit or toy split
2. Baseline stage — reproduce the strongest baseline(s)
3. Main method stage — run the final method on the primary setting
4. Decision stage — run the decisive ablations for novelty, simplicity, and frontier necessity
5. Polish stage — robustness, qualitative figures, appendix extras

For each milestone, estimate:

• compute cost
• expected turnaround time
• stop / go decision gate
• risk and mitigation

Separate must-run from nice-to-have experiments.

Phase 5: Write the Outputs

Step 5.1: Write refine-logs/EXPERIMENT_PLAN.md

Use this structure:

# Experiment Plan

**Problem**: [problem]
**Method Thesis**: [one-sentence thesis]
**Date**: [today]

## Claim Map
| Claim | Why It Matters | Minimum Convincing Evidence | Linked Blocks |
|-------|-----------------|-----------------------------|---------------|
| C1    | ...             | ...                         | B1, B2        |

## Paper Storyline
- Main paper must prove:
- Appendix can support:
- Experiments intentionally cut:

## Experiment Blocks

### Block 1: [Name]
- Claim tested:
- Why this block exists:
- Dataset / split / task:
  - Provenance: existing | adapted | constructed
  - Source (name, or construction method if constructed):
  - Available N (total in source pool):
  - Planned used N (this project):
  - Subset note (only if planned used N < available N):
- Compared systems:
- Metrics:
- Setup details:
- Success criterion:
- Failure interpretation:
- Table / figure target:
- Priority: MUST-RUN / NICE-TO-HAVE

### Block 2: [Name]
...

## Run Order and Milestones
| Milestone | Goal | Runs | Decision Gate | Cost | Risk |
|-----------|------|------|---------------|------|------|
| M0        | ...  | ...  | ...           | ...  | ...  |

## Compute and Data Budget
- Total estimated GPU-hours:
- Data preparation needs:
- Human evaluation needs:
- Biggest bottleneck:

## Risks and Mitigations
- [Risk]:
- [Mitigation]:

## Final Checklist
- [ ] Main paper tables are covered
 [ ] Novelty is isolated
 [ ] Simplicity is defended
 [ ] Frontier contribution is justified or explicitly not claimed
 [ ] Nice-to-have runs are separated from must-run runs

Step 5.2: Write refine-logs/EXPERIMENT_TRACKER.md

Use this structure:

# Experiment Tracker

| Run ID | Milestone | Purpose | System / Variant | Split | Metrics | Priority | Status | Notes |
|--------|-----------|---------|------------------|-------|---------|----------|--------|-------|
| R001   | M0        | sanity  | ...              | ...   | ...     | MUST     | TODO   | ...   |

Keep the tracker compact and execution-oriented.

Step 5.3: Present a Brief Summary to the User

Experiment plan ready.

Must-run blocks:
- [Block 1]
- [Block 2]

Highest-risk assumption:
- [risk]

First three runs to launch:
1. [run]
2. [run]
3. [run]

Plan file: refine-logs/EXPERIMENT_PLAN.md
Tracker file: refine-logs/EXPERIMENT_TRACKER.md

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.

• Every experiment must defend a claim. If it does not change a reviewer belief, cut it.

• Prefer a compact paper story. Design the main table first, then add only the ablations that defend it.

• Defend simplicity explicitly. If complexity is a concern, include a deletion study or a stronger-but-bloated variant comparison.

• Defend frontier choices explicitly. If a modern primitive is central, prove why it is better than the strongest simpler alternative.

• Prefer strong baselines over long baseline lists. A short, credible comparison set is better than a padded one.

• Separate must-run from nice-to-have. Do not let appendix ideas delay the core paper evidence.

• Reuse proposal constraints. Do not invent unrealistic budgets or data assumptions.

• Do not fabricate results. Plan evidence; do not claim evidence.

Composing with Other Skills

/research-refine-pipeline -> one-shot method + experiment planning
/research-refine   -> method and claim refinement
/experiment-plan   -> detailed experiment roadmap
/run-experiment    -> execute the runs
/auto-iteration-loop  -> react to results and iterate on the paper