experiment-plan

name	experiment-plan
description	Turn a refined research proposal or method idea into a detailed, question- and mechanism-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

Experiment Plan: Question-Driven, Paper-Oriented Validation

Capability Routing

This is a first-layer entry skill. Keep it loaded as the user-facing route; when a request needs a specialized capability below, resolve the debuffer repo root from .debuffer_skills/installed-skills-codex.txt (repo_root) when available, read the referenced library SKILL.md, then follow that skill. Do not copy the whole library skill into this file.

• /ablation-planner: read ../library/idea-method/ablation-planner/SKILL.md.
• /run-experiment: read ../library/experiments/run-experiment/SKILL.md.
• /training-check: read ../library/experiments/training-check/SKILL.md.
• /system-profile: read ../library/experiments/system-profile/SKILL.md.

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 understand the mechanism and prepare paper evidence? If the user wants the full chain in one request, run /research-refine first and continue here once the method thesis is stable.

The goal is not to generate a giant benchmark wishlist. The goal is to turn a proposal into a question -> 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

Project Guide Integration

Read ../shared-references/project-guide-protocol.md before writing outputs. Use it to keep experiment planning connected to the project's macro phase without generating redundant long documents:

• Update PROJECT_STATUS.md to the experiment protocol phase when the experiment story becomes stable enough for implementation planning.
• Keep refine-logs/EXPERIMENT_PLAN.md as the operational roadmap. Create or refresh docs/experiments/EXPERIMENT_PROTOCOL.md only when the plan is ready for implementation, AutoDL smoke preparation, or formal-run freezing.
• Before a formal experiment protocol or AutoDL formal-run plan, read docs/project/RESEARCH_BLUEPRINT.md and docs/project/BLUEPRINT_GATE.md when present, falling back to legacy root files only for old projects. If they are absent and the method is stable enough for formal runs, run or request research-blueprint first. Do not block exploratory experiment planning on a missing blueprint.
• If the idea is still exploratory, update only compact state such as docs/project/PROJECT_BRIEF.md and docs/project/NEXT_ACTIONS.md; do not emit docs/project/PROJECT_GUIDE.md, docs/evidence/EVIDENCE_LEDGER.md, or docs/paper/PAPER_GUIDE.md unless their gates are reached.
• Also read ../shared-references/autosci-lite-patterns.md. Before writing a formal run plan, scan idea-stage/IDEA_MEMORY.md and experiments/NEGATIVE_RESULTS.md, then add a pilot gate unless equivalent smoke evidence already exists or the user explicitly waives it.

Constants

• OUTPUT_DIR = refine-logs/ — Default destination for experiment planning artifacts.
• MAX_PRIMARY_FINDINGS = 2 - Prefer one dominant expected finding plus one supporting finding.
• 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:

• docs/project/RESEARCH_BLUEPRINT.md (fall back to legacy RESEARCH_BLUEPRINT.md)
• docs/project/BLUEPRINT_GATE.md (fall back to legacy BLUEPRINT_GATE.md)
• PROJECT_STATUS.md
• 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: Define Core Questions and Expected Findings

Before proposing experiments, write down the research questions and hypotheses the experiments must distinguish.

Use this structure:

• Primary question / expected finding: the main mechanism-level result the work is trying to establish
• Supporting finding: optional, only if it directly strengthens the main paper story
• Alternative explanation 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 finding believable to a strong reviewer?

Do not exceed MAX_PRIMARY_FINDINGS unless the paper truly has multiple inseparable findings.

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 establish the core findings
• 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:

• Goal / hypothesis tested
• Why this block exists
• Dataset / split / task
• Design: factors varied, fixed settings, number of seeds, run order
• 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
• Expected result / decision rule: what outcome would support, weaken, or falsify the hypothesis?
• Failure interpretation: if the result is negative, what does it mean?
• Table / figure target: where this result should appear in the paper
• Reproducibility entrypoint: config path, suite path, command, output directory

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

Insert a Pilot gate before any formal AutoDL/HPC suite:

• Pilot spec: reduced dataset/subset, baseline included, proposed variant, decisive metrics, bounded budget, and pass/fail/inconclusive rules.
• Output path: experiments/runs/pilot/<slug>/.
• If GPU time is needed, write an AutoDL-ready command block or suite path and wait for user approval or pasted results.
• Pilot pass means "no obvious collapse"; it is not paper evidence.
• Pilot fail or inconclusive must update experiments/NEGATIVE_RESULTS.md, and idea-level consequences should update idea-stage/IDEA_MEMORY.md.

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]

## Question / Finding Map
| Question or Finding | 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]
- Goal / hypothesis tested:
- Why this block exists:
- Dataset / split / task:
- Design:
- Compared systems:
- Metrics:
- Setup details:
- Expected result / decision rule:
- Failure interpretation:
- Table / figure target:
- Reproducibility entrypoint:
- Priority: MUST-RUN / NICE-TO-HAVE

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

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

## Pilot Gate
- Pilot spec:
- Reduced budget:
- Pass/fail/inconclusive rules:
- AutoDL handoff:
- Output path:
- Decision before formal runs:

## 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: Refresh docs/experiments/EXPERIMENT_PROTOCOL.md When Stable

If the plan is ready for implementation, AutoDL smoke preparation, or formal run freezing, create or refresh docs/experiments/EXPERIMENT_PROTOCOL.md using the shared schema from project-guide-protocol.md. Keep it compact: link to refine-logs/EXPERIMENT_PLAN.md, list the finalized experiment blocks, and record the reproducibility entrypoints. Freeze it before formal AutoDL/HPC runs.

If the plan is not stable yet, skip this file and update only PROJECT_STATUS.md plus docs/project/NEXT_ACTIONS.md.

Step 5.4: 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 docs/project/OUTPUT_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 answer a research question. 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 assert evidence.

Composing with Other Skills

/research-refine   -> method and mechanism refinement
/experiment-plan   -> detailed experiment roadmap
/run-experiment    -> execute the runs
/auto-review-loop  -> react to results and iterate on the paper