Individual skill
neurips-artifact-evaluation
Use when packaging NeurIPS code, data, models, demos, benchmarks, or other research artifacts for anonymous review, reproducibility, public release, or MLRC-style artifact scrutiny.
NeurIPS-Skills/skills/neurips-artifact-evaluation/SKILL.md
Source commit 932eb23331721b16e967c59f093781be1928b498
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- Yes
- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 1,401
- Repository last updated
- Sep 27, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- academic-writing Repository topic
- agent-skills Repository topic
- journal Repository topic
- academic-research Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- awesome-list Repository topic
- claude Repository topic
- claude-code Repository topic
- economics Repository topic
- data Derived category
- research Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 2,377 bytes
Read excerpt
# NeurIPS Artifact Evaluation
NeurIPS main track does not reduce artifact quality to a generic badge workflow. It expects code,
data, and execution details when they are needed to support the scientific claim, and its checklist
and code/data guidance make artifact quality visible to reviewers.
## Artifact decision
- If the contribution is a method, include training and evaluation code or justify why it cannot be
shared.
- If the contribution is a dataset or benchmark, provide metadata, license, preservation plan,
representative-use discussion, and access restrictions.
- If the contribution depends on a model, include weights, prompts, decoding settings, compute
resources, or a precise explanation of unavailable components.