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Ecai reproducibility

Skill brycewang-stanford/Awesome-Journal-Skills/ECAI-Skills/skills/ecai-reproducibility

Use when building the reproducibility story for an ECAI paper — a complete proof appendix for theory/KR work, a seeded and cached package for empirical/ML work, provenance pinning for datasets and models, and an anonymized supplement that satisfies double-blind review inside ECAI's tight 7-page body with no separate artifact track.From its SKILL.md

Install
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill ecai-reproducibility

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

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ECAI Reproducibility

ECAI reproducibility is in-band: there is no separate artifact-evaluation track, so the same reviewers who judge the paper judge whether the results and proofs are believable, from the 7-page body plus an anonymized supplement. Build the reproducibility story to survive that read — one pass, double-blind, in a short window — not a badge committee.

Because ECAI is a general-AI venue, "reproducible" means different things across its breadth. Pick the mode that matches your contribution.

Mode 1 — Theory / KR / argumentation: proofs are the artifact

  • The body sketches; the supplement carries every full proof. A theorem stated without a checkable proof is a claim, not a result.
  • State all assumptions explicitly (finiteness, admissibility, monotonicity, language fragment). The most common reject-driving misreading is a reviewer assuming a hidden condition.
  • If the theory has a computational side (a solver, an encoding, complexity results), include a reference implementation or the exact encoding so a reviewer can re-run a small instance.
  • Define objects once, precisely; ECAI's symbolic-AI reviewers check definitions against lemmas.

Mode 2 — Empirical / ML / planning: seed, cache, pin

  • Fix and report seeds; report central tendency and spread across seeds, not a single lucky run (ecai-experiments).
  • Cache raw outputs (model predictions, planner traces, API responses) so results reproduce without live calls — a package that re-queries an API re-samples rather than reproduces.
  • Pin provenance: dataset name and version/date, preprocessing scripts, model identifiers with dates, hardware where it affects timing.
  • Provide a claim→file map: each reported table/number points to the script that regenerates it.

Provenance pinning (both modes, where applicable)

[ ] Dataset: name, version/DOI, download date, license, preprocessing script committed
[ ] Splits: exact train/val/test (or instance sets) fixed and included or scripted
[ ] Models: identifiers + dates (for hosted/LLM components); prompts/configs committed
[ ] Seeds: fixed and reported; number of runs stated
[ ] Environment: dependency versions pinned (lockfile / environment.yml / requirements)
[ ] Outputs: raw results cached so re-run does not depend on a live service

Double-blind, in the supplement too

The supplement is read under double-blind review. Anonymize it as carefully as the PDF:

# Sweep the staged supplement before zipping
grep -rniE 'university|@[a-z0-9.]+\.(edu|ac\.[a-z]+)|acknowledg|funded by|grant (no|number)' supplement/ | head
unzip -l supplement.zip | grep -Ei '\.git/|/home/|/Users/|\.DS_Store' | head

Strip repository owners, institution names, funding lines, and any system named after your group. A de-anonymizing supplement can trigger a summary reject before the science is even read.

Honesty over completeness

  • If data cannot be shared (privacy, licensing, industrial confidentiality — common in PAIS applications), say so and why, and share what you can (code, a synthetic sample, the protocol). A silent gap reads worse than a stated, justified limitation.
  • Do not claim reproducibility you have not tested. Run the package from a clean checkout yourself before submitting.

Fit the 7-page body

Reproducibility content that a reviewer needs to judge the paper (the core proof idea, the evaluation protocol, the key numbers) belongs in the body; full proofs, extra tables, and code belong in the supplement. Nothing decision-critical may live only outside the 7 pages (ecai-supplementary).

Post-acceptance

Convert the anonymized supplement into a permanent, open release — DOI-issuing archive, open license, de-anonymized owners — and link it from the open-access camera-ready (ecai-camera-ready).

Output format

[Mode] theory (proof appendix) / empirical (seeded+cached) / mixed
[Proof completeness] every theorem has a full checkable proof + explicit assumptions? yes/no
[Provenance] datasets/models/seeds/env pinned? gaps: <list>
[Claim map] each table/number -> regenerating file
[Anonymity] supplement clean / leaks: <where>
[Body/supplement split] nothing decision-critical outside the 7-page body
[Post-acceptance] DOI + open license + de-anonymized link planned

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.