ECAI Artifact Evaluation
SkillDev toolsUse when planning the reproducibility/artifact story for an ECAI paper, noting that ECAI/FAIA has NO ACM/IEEE-style artifact-badging committee, so credibility is carried by the paper and its supplement and judged by the same reviewers, and adapting an ML-style reproducibility package (or a complete proof appendix for theory work) to that reality.
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Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the ECAI Artifact Evaluation skill
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in ECAI-Skills/skills/ecai-artifact-evaluation/SKILL.md and read by ahel’s review.
Start with a correction that saves authors from importing the wrong workflow: ECAI does not run an ACM/IEEE-style artifact-evaluation track with a separate badge committee. There is no "Artifacts Available / Functional / Reusable / Reproduced" pipeline as at ACM SIGSOFT venues, and no separate artifact deadline to hit after acceptance. In ECAI, the reproducibility story is carried by the paper and its supplement and judged by the same reviewers who read the paper, during the one review round.
That makes the "artifact" a submission-time asset, not a post-acceptance badge chase. Its job is to make the reviewer trust the claim inside a 7-page body. (Confirm on the current call whether the edition adds any optional reproducibility checklist or appendix mechanism — this is 待核实 per cycle and can differ between a standalone ECAI and the joint IJCAI-ECAI 2026.)
Match the artifact to the contribution shape
ECAI is a general-AI venue, so "artifact" means different things:
| Contribution shape | The credibility artifact is... |
|---|---|
| Theory / KR / argumentation | A complete proof appendix (full proofs the body only sketches) plus, if applicable, a reference solver/encoding |
| Planning / search / optimization | The domain files, instances, seeds, and a runnable implementation reproducing the reported node/quality numbers |
| Machine learning | Code, data (or a loader), configs, seeds, and cached outputs so results reproduce without live API calls |
| Multi-agent systems | The environment, agent code, and the exact evaluation protocol (episodes, seeds, metrics) |
| Applied AI (PAIS) | Enough of the pipeline and (sanitized) data to make the deployment claim credible |
What "good" looks like at review time
- Anonymized. The supplement is read under double-blind review; strip repository owners,
institution names, and system names that identify you (
ecai-submission). - Self-contained. A reviewer opens it once, in a short window; it must run or be readable without chasing dependencies or your lab's private data.
- Decision-critical content stays in the body. The supplement holds support (full proofs,
extra tables, code) — not the claim itself. Nothing a reviewer needs to judge the paper may
live only in the supplement (
ecai-supplementary). - Proportional. Match effort to the claim: a theory paper's artifact is a rigorous proof appendix, not a Docker image; an empirical paper's artifact is a runnable, seeded package.
A pragmatic checklist (adapt, don't badge-chase)
[ ] Full proofs present for every theorem the body sketches (theory work)
[ ] Code runs from a clean checkout with a documented entrypoint (empirical work)
[ ] Data included or a script fetches a versioned public source; seeds fixed
[ ] Cached model/API outputs included so results do not re-sample at run time
[ ] A short README maps each paper claim/table -> the file that reproduces it
[ ] Archive anonymized: no owner, institution, funding, or system-name leaks
[ ] Total size and runtime reasonable for a reviewer's one-pass read
Do not import the wrong machinery
- No ACM/IEEE badges. Do not promise "Artifacts Evaluated - Reusable" or design around a badge committee — none exists at ECAI. Credibility is reviewer-judged, in-band.
- No separate artifact-track deadline. Everything ships with the paper (abstract 12 Jan / paper 19 Jan for IJCAI-ECAI 2026); there is no later artifact submission.
- Not a leaderboard. ECAI values understanding (a proof, a fair comparison) over a single
benchmark number; an artifact that only re-prints a leaderboard score misses the venue's bar
(
ecai-experiments).
Post-acceptance: make it permanent and open
Once accepted, convert the anonymized supplement into a permanent, open release to match ECAI's open-access ethos:
- Deposit code/data in a DOI-issuing archive (e.g. Zenodo/Software Heritage) with an open license.
- De-anonymize repository owners and restore acknowledgements (
ecai-camera-ready). - Put the permanent link in the camera-ready so the open-access paper points to a stable artifact.
Output format
[Artifact type] proof appendix / runnable code+data / environment+protocol / deployment pipeline
[Anonymity] clean / leaks: <where>
[Claim map] each theorem/table -> proof or reproducing file
[Self-containment] runs/readable in one pass? missing deps: <list>
[Reality check] no ACM/IEEE badge assumed; nothing decision-critical hidden in the supplement
[Post-acceptance] DOI archive + open license + de-anonymized link planned for camera-ready
Signals
- GitHub stars
- 1k
- Forks
- 155
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
ecai-artifact-evaluation- Source
- github.com/brycewang-stanford/awesome-journal-skills
github.com/brycewang-stanford/awesome-journal-skills
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