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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ecai-reproducibility --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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. ## M
- Mode 1 — Theory / KR / argumentation: proofs are the artifact
- Mode 2 — Empirical / ML / planning: seed, cache, pin
- Provenance pinning (both modes, where applicable)
- Double-blind, in the supplement too
- Honesty over completeness
- Fit the 7-page body
- Post-acceptance
- Output format
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
What does the ecai-reproducibility skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ecai-reproducibility --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.