refactor-research-code

name	refactor-research-code
description	Clean research code for public release while preserving reported results. Uses release_audit.py to separate safe mechanical cleanup from ask-first behavior risks and identity leaks. Use for release-ready repos, dead experiment branches, config extraction, documented entrypoints, and double-blind code cleanup.

Refactor Research Code

Take a research repo from "works on my machine" to a clean, documented, re-runnable public release — without changing a single number the paper reports. Research code accretes dead experiment branches, hardcoded paths, magic hyperparameters, an undocumented "run this file, then that one" ritual, and identifying breadcrumbs. This skill removes that cruft and gives the repo a sensible layout, but it treats result preservation as the prime directive: it classifies every proposed change as safe (mechanical, behavior-preserving) or ask-first (could move the numbers), and it never touches an ask-first item without the author's explicit say-so.

It is not generic refactoring (no perf rewrites, no "modernize the API," no restructuring for its own sake). The only goal is release readiness with identical behavior. When a "cleanup" would change what the code computes, the right move is to flag it and ask — not to make the repo prettier at the cost of the paper's claims.

When to use

• "Clean up / refactor my research code for release." / "It's a mess — get it publishable."
• "Remove the dead experiment branches and commented-out code."
• "Separate config from code." / "These hyperparameters are hardcoded everywhere."
• "Make my runs deterministic / re-runnable." / "Document the entrypoint."
• "Give the repo a clear layout." / "Strip my identity for double-blind."
• Before test-research-code (which adds tests/seeds/env) and prepare-artifacts (which packages and hosts for a badge) — this is the structural cleanup that comes first.

What this is NOT (route elsewhere)

You want…	Use instead
Add smoke tests, pin seeds, capture the environment	test-research-code
Package + archival DOI/SWHID + badge taxonomy + appendix	prepare-artifacts
Deep double-blind sweep of the paper + reversible de-anon	anonymize-paper
Confirm the produced numbers match the paper's tables	verify-results
Reorganize the paper's sections/argument	refactor-structure

This skill flags seeding/env gaps and identity leaks and hands them off; it does the structural cleanup itself.

Inputs

1. The research-code directory (the repo, or a subfolder holding the experiment scripts). This skill reads local files only; it does not clone.
2. Whether review is double-blind / single-blind / not blind (drives the identity scan), and any identifying terms to scan for (author names, institution, internal codenames).
3. Optionally .paper-memory/profile.yml (contribution type — a system or dataset paper is judged harder on code quality, leaning toward a Reusable release).
4. Optionally the target venue's artifact track, if the cleanup is in service of one — but badge rules are fetched live by prepare-artifacts, not here.

Prime directive: preserve exact numerical behavior

Every action this skill takes falls in one of three buckets. Treat the boundary between them as sacred.

• SAFE — apply freely (with the author's nod): add a README / LICENSE / .gitignore; move files into a src//package layout (then fix imports); delete pure junk (__pycache__, *.pyc, .DS_Store, editor swap files); document the existing entrypoint; reformat whitespace/comments only. These cannot change what the code computes.
• ASK-FIRST — never touch without explicit confirmation: delete a code path (a backup file, a commented-out block, an if False: branch — it may be a toggled experiment or silently shadow the live version); extract a hardcoded hyperparameter or path into config (the value must be preserved exactly); change anything touching seeding, RNG, thread/worker count, dtype/precision, or evaluation order (these define the numbers). For each, present the finding, the proposed change, and why it might change results, then wait.
• IDENTITY — scrub before any blind upload: emails, home paths, author names, .git history. Surface them here; hand the deep sweep to anonymize-paper.

If you cannot tell whether a change is safe, it is ask-first by default. A prettier repo that reports different numbers is a failure, not a success.

Process

This follows plan → confirm → apply-safe → ask-on-risk, with the plan grounded in an external, measurable signal — the static audit, not the model's sense that the repo "looks clean." Name assumptions, show competing readings, and keep the author in control.

1. Read memory first. Read .paper-memory/lessons.md (and profile.yml if present) so you skip what the author already fixed and lead with their recurring habits (e.g. "you tend to ship .git and hardcoded /home paths"). See paper-memory-convention.md.

2. Confirm scope and blind level. Ask whether review is double-blind and what the release is for (a public GitHub release, a blind-review supplement, an artifact track). Don't anonymize a single-blind/non-blind release — it mangles a fine repo. Get the identifying terms to scan for if blind.

3. Run the static release-refactor audit (deterministic). Do not hand-grep:

   python3 scripts/release_audit.py <code-dir> \
       --blind <none|single|double> --names "Jane Doe,Example University"
   # machine-readable:
   python3 scripts/release_audit.py <code-dir> --blind double --json
   

   It walks the repo (read-only, no execution, no network, no writes) and emits findings tagged by category (dead-code, config, entrypoint, layout, determinism, identity, hygiene) and, critically, by risk: SAFE, ASK-FIRST (behavior-risk), or IDENTITY. Flags: --strict (any finding fails), --max-files N, --max-bytes N (in-tree blob threshold). Exit codes: 0 no ask-first/identity findings, 1 ask-first or identity findings present, 2 usage error. This is the signal the plan is built on (an external check, not self-judgment); the audit maps opportunities — it does not prove the repo reproduces.

4. Present a refactor plan the author approves BEFORE any edit. Group the findings into the three buckets, ordered SAFE → ASK-FIRST → IDENTITY. For each ask-first item, state the change and the result-risk ("deleting this if False: block is safe only if it is unreachable in every config — confirm?"). The author approves or vetoes per item. Categories and the safe refactor recipes are in references/release-refactor-catalog.md.

5. Apply SAFE refactors (with the author's go-ahead), in this order — each maps to an audit category:

   • Layout — add README (entrypoint, the exact reproduce command per result, deps, the directory map), LICENSE, .gitignore; move a flat dump of scripts into a src//package layout and fix the imports.
   • Entrypoint — document (or wire) one obvious command that runs the pipeline end to end (make reproduce / run.sh / python -m pkg).
   • Hygiene — remove __pycache__, *.pyc, .DS_Store, editor swaps from the release copy; move large in-tree data/model blobs to a download script / archival host (this last one is ask-first — confirm the blob isn't needed at runtime).

6. Walk ASK-FIRST items one at a time, never in bulk. For each approved change: make the minimal edit, then verify behavior is preserved by an external check — re-run the pipeline (or the smoke test) and diff the output, not by eyeballing the diff. Behavior preservation is confirmed by re-running, which is test-research-code / verify-results territory; this skill flags and coordinates, and stops to ask when a change is genuinely risky. Escalation is a feature. In particular:

   • dead-code — only after confirming nothing imports/reaches it.
   • config — extract the literal into config/CLI with the identical value; a typo here silently changes results.
   • determinism — set/record seeds and pin order in coordination with the author, then re-run to re-confirm the paper's numbers. Adding a seed changes an unseeded run — this is the highest-risk edit. Hand the seed/env mechanics to test-research-code.

7. Handle IDENTITY leaks for blind review. Surface emails, home paths, names, and .git history; hand the deep sweep (commit history, notebook metadata, self-citation phrasing, anonymized mirror) to anonymize-paper. Never expose the author through the repo on a blind upload.

8. Re-run the audit until SAFE-clean, with an explicit stop condition. Loop audit → fix → audit until no SAFE/layout/hygiene findings remain and every ask-first item is resolved or consciously deferred — not open-endedly. Hard cap ~3 passes; if the audit still flags the same ask-first item, that is a judgment call to escalate, not to keep editing.

9. Write the refactor plan + change log to paper-workspace/submission/refactor-research-code-plan.md and append a line to paper-workspace/INDEX.md. Lead with what was done (safe), then the ask-first decisions (applied / vetoed / deferred, each with the result-risk noted), then the identity handoff. Cite each item's audit finding.

Output

• A refactor plan (SAFE / ASK-FIRST / IDENTITY buckets, each finding with category + risk + file[:line]) plus a change log of what was applied, vetoed, or deferred — written to paper-workspace/submission/.
• The edited repo files (only with per-item approval): README / LICENSE / .gitignore, a src//package layout, a documented entrypoint, junk removed, and any ask-first edits the author confirmed.
• A clear handoff list: seeds/env/tests → test-research-code; packaging/DOI/ badge → prepare-artifacts; deep anonymization → anonymize-paper; number-vs-paper check → verify-results.

Adapt to your discipline

The audit reads a directory, not a discipline, so the layout/dead-code/identity checks apply broadly. For non-Python stacks, the determinism and config patterns differ (R set.seed, Julia Random.seed!, a renv.lock/Manifest.toml for the env) — the script already knows several; extend the pattern tables in scripts/release_audit.py for your language. The result-preservation contract is universal: clean the repo, keep the numbers.

Guardrails

• Preserve exact numerical behavior — this is the whole point. Never apply an ask-first change (delete code, extract a constant, touch seeding/threading/ dtype/order) without explicit author approval, and verify preservation by re-running, never by reading the diff (self-reflection validates its own mistakes — the verification must be external).
• When unsure whether a change is safe, it is ask-first. A prettier repo that reports different numbers is a failure.
• Run nothing destructive and nothing untrusted. release_audit.py is static, read-only, no network. Re-running the pipeline to verify behavior is the author's call and may need a sandbox/GPU.
• Anonymization-aware: for blind review, never expose identity through the repo or .git history; hand the deep sweep to anonymize-paper.
• Stay in your lane. Don't add tests/seeds/env (that's test-research-code), mint a DOI or package an artifact (that's prepare-artifacts), or claim the results reproduce (that's verify-results, and "reproduce" is never bit-exact and never this skill's call).
• Copilot, not pilot: it plans, asks, and edits on approval; it never submits, deposits, or pushes on the author's behalf.
• references/ files stay one level deep; keep this file under 500 lines.

Memory

Uses the shared .paper-memory/ convention (full spec: paper-memory-convention.md).

• At start: read lessons.md to skip already-fixed cleanups and lead with recurring release habits (e.g. "you ship hardcoded /home/<you> paths every repo"); read profile.yml for the contribution type.
• At end: append one dated entry per recurring habit, in the canonical format, via reflect-and-improve's reflect_log.py:

  python3 ../reflect-and-improve/scripts/reflect_log.py append \
      --memory .paper-memory --skill refactor-research-code --scope recurring \
      --issue "hyperparameters hardcoded across scripts, no config file" \
      --rec "extract to config.yaml/CLI with identical values every release"
  

• Create .paper-memory/ on demand and offer to add it to .gitignore; local-only, never uploaded, never copied into this repo.