Methods Review

Core Rule

A Methods section passes only if a competent stranger, with the paper and the deposited materials, could regenerate the result. This skill enumerates every reproduction ingredient, checks the section against it, and flags each missing one — it does not fill the gap by inventing a value. The honest output for an unstated seed is "GAP — seed not reported", never a plausible "seed = 42" written into the prose. And changing what the Methods say was done is a Protected Claim (CLAUDE.md): it alters the record of the experiment, so this skill flags gaps and proposes what the author must add — it never rewrites the procedure to make it look more reproducible than it was.

This operationalizes agent_docs/reproducibility.md. A vague Methods section is the single most common reason a reviewer cannot sign off; this catches the omissions before they do.

When to Use

Invoke with /methods-review when:

• A Method(s) / Experimental-setup section is drafted and you want it reproducible before review.
• Before submission to a venue with a reproducibility checklist (NeurIPS/ICML/ICLR require one; ACL expects a responsible-NLP checklist) — pre-fill it from this output.
• A reviewer asked for "more detail" / "missing hyperparameters" / "no version reported" — run this to find all such gaps at once, not just the one flagged.
• The prompt-router flagged the task [Methods].

Scope it: /methods-review sections/method.tex (include the experimental-setup subsection and any appendix the Methods defer detail to — "see App. C" is only a pass if App. C exists).

Process

Phase 1: Load the Reproducibility Standard

1. Read agent_docs/reproducibility.md — the reproducible-vs-reported-only distinction, the "if changing it would change the result, report it" rule, and the reproducibility checklist this skill executes.
2. Read agent_docs/field/ai-ml.md for ML/agents work — the field's reproduction bar is high and specific (seeds, hyperparameter grid + selection criterion, compute, data splits/license, exact model checkpoint and date, decoding params + verbatim prompts, environment/harness version + commit). This is the per-item source for the ML checklist.
3. Read MANUSCRIPT_MAP.md → Data & reproducibility — what the authors have stated is reproducible vs reported-only, and where data/code live. The Methods must match this; a conflict is a finding.
4. Read the target Methods .tex in full, plus the appendix sections it defers to.

Phase 2: Enumerate Reproduction Requirements

Build the requirement list before reading the section for what is present — otherwise you only check what is there, not what is missing. For an LLM-agent paper, the requirements are:

• Base model — exact name, version/checkpoint, and snapshot date (closed APIs drift; an unpinned model is reported-only, not reproducible).
• Data — dataset/benchmark, version, the train/val/test splits and how they were separated, preprocessing, and license.
• Hyperparameters — the full grid searched, the selected values, and the selection criterion (chosen on val, reported on test once).
• Seeds — the random seeds, and how many runs/rollouts results are averaged over (a single run is not reproducible variance).
• Compute — hardware, GPU-hours / wall-clock — needed for fair-budget comparison and cost reporting.
• Decoding parameters — temperature, top-p, max tokens, stop conditions, and the prompt templates verbatim (in an appendix).
• Eval harness — the agent scaffold (e.g. ReAct loop, max steps), the tool inventory, the harness/environment version + commit hash, and the metric definitions (full vs partial task success, how "hallucinated tool call" is operationalized).
• Code/data availability — repository or archive, a pinned commit/tag, a real minted DOI (or [VALUE — verify] until the deposit exists — never a placeholder DOI), license, and a statement of what reproduces (which figures/tables).
• Exclusions — every dropped task/sample named, counted, justified.
• Reproducible vs reported-only — stated explicitly where the work mixes the two (e.g. offline eval reproducible; results against a proprietary closed-API model reported-only).

Drop or add rows to fit the actual study (a method with no learned component has no hyperparameter grid) — but justify omissions; do not silently skip a required row.

Phase 3: Check the Section Against Each Requirement

For every requirement, mark PASS (stated with a reproducible value), PARTIAL (mentioned but underspecified — "default settings" is PARTIAL, since defaults drift across versions), or GAP (absent). Quote the locator or note its absence. "Default settings", "a large model", "standard preprocessing", "an agent loop" are GAP/PARTIAL — they are not reproducible.

When a requirement is genuinely reported-only and the section says so, that is a PASS for honesty (it is correctly labeled), even though the result itself cannot be regenerated. Silence that implies reproducibility for a reported-only result is a GAP.

Phase 4: Report the Checklist and Fixes

Produce the pass/GAP checklist with the fix per item. The fix names what the author must add (a value they hold, a deposit they must make). Do not invent the missing value — a seed, a version, a DOI you write in is a fabrication, exactly the failure the kit forbids. A [VALUE — verify] placeholder is the honest stand-in. Adding/altering procedure detail is a Protected Claim — propose it, let the author confirm and record in tasks/decisions.md.

Output Format

# Methods Review — sections/method.tex (reproducibility)

## Verdict
A competent stranger could NOT currently rerun this work: 4 GAPs (model date, seeds,
decoding params, code availability) block reproduction. 3 PARTIALs need tightening.

## Reproducibility checklist
| Requirement | Status | Locator / note | Fix (author action) |
|---|---|---|---|
| Base model + version + **date** | GAP | "we use a large LLM" — no name/version/date | Name the model + checkpoint + snapshot date; if closed API, label reported-only |
| Data: benchmark + version + splits + license | PARTIAL | benchmark named; splits + license absent | State how train/val/test were separated; add the license |
| Hyperparameters: grid + selected + criterion | PARTIAL | selected τ = 0.7 given; no grid, no criterion | Add the searched grid and "selected on val by X" |
| Seeds + #runs | GAP | single run implied; no seeds | Report seeds and average over ≥3 runs/rollouts with CIs |
| Compute (hardware, GPU-hours / wall-clock) | GAP | not reported | Add hardware + wall-clock (also needed for fair-budget claim) |
| Decoding: temp, top-p, max tokens, **prompts verbatim** | GAP | none stated | Add decoding params; put prompt templates verbatim in an appendix |
| Eval harness: scaffold + tools + version + commit | PARTIAL | "ReAct agent" named; no max-steps, tool list, or commit | Add max steps, tool inventory, harness version + commit hash |
| Metric definitions (full vs partial success; "hallucinated call") | PASS | sec:setup ¶2 defines all three | — |
| Code/data availability + DOI + commit + license | GAP | no statement | Add an availability statement; deposit + pin commit; DOI is `[VALUE — verify]` until minted — do NOT fabricate |
| Exclusions named/counted/justified | PASS | "12 malformed tasks dropped (see App. B)" | — |
| Reproducible vs reported-only stated | GAP | not drawn | State the line: offline eval reproducible; proprietary-model results reported-only |

## Summary
- PASS: 2 · PARTIAL: 3 · GAP: 6

## Priority fixes (block reproduction first)
1. **[GAP]** Model name + version + **date** — without it nothing else reproduces. If closed API, say so and label those results reported-only.
2. **[GAP]** Seeds + multiple runs + CIs — a single run gives no variance (ties to /stats-check).
3. **[GAP]** Decoding params + verbatim prompts — agent behavior is undefined without them.
4. **[GAP]** Code/data availability — add the statement; DOI `[VALUE — verify]`, never a placeholder.

## Notes for the author
- Every "Fix" adds a value YOU hold — I did not invent seeds, versions, or a DOI.
- Adding procedure detail changes what the Methods say was done = Protected Claim. Confirm each addition; record in tasks/decisions.md.

End with (PASS / PARTIAL / GAP) counts and the single gap that most blocks reproduction. Never report Methods "reproducible" while a result-affecting ingredient is a GAP.

Pairs With

• agent_docs/reproducibility.md — the standard this skill executes (reproducible vs reported-only, the availability-statement and FAIR-deposit rules, the master checklist).
• agent_docs/field/ai-ml.md — the per-item ML/agents reproduction requirements (seeds, grid, compute, model date, decoding, harness version) the enumeration draws from.
• integrity-reviewer agent — escalate when missing methods detail co-occurs with overclaim or selective reporting; it scans the whole manuscript for integrity risk, this skill verifies the Methods recipe.
• /stats-check — the seeds/runs/variance GAPs found here feed directly into the statistics pass; reproducible variance and reported uncertainty are two views of the same requirement.

Common Rationalizations

Notes

• This skill never invents a missing value. A seed/version/DOI you cannot point to is a fabrication; the honest output is GAP + [VALUE — verify] — per the cardinal rule in CLAUDE.md.
• Enumerating requirements and judging reproducible-vs-reported-only is Reasoner-tier (CLAUDE.md → Model Selection).
• block-fabrication.sh (PreToolUse) blocks a fake-shaped DOI if you try to write one into an availability statement — that is the system working: flag [VALUE — verify], do not fabricate.
• A recurring reproducibility gap (reviewer keeps asking for versions or the availability statement) is a rule — log under tasks/reviews/, applies_to: [reproducibility, methods], promote to ## Top Rules if it recurs (CLAUDE.md → Self-Improvement Loop).