icdm-artifact-evaluation
Use when packaging code, data, and logs for an ICDM (IEEE International Conference on Data Mining) paper - building the anonymized, history-scrubbed repository that the PDF must cite for a triple-blind Research Track submission, how the single-blind Applied Track changes what may be revealed, and the smoke checks that make an ICDM artifact reviewer-usable.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdm-artifact-evaluation --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.
# ICDM Artifact Evaluation Package the artifact so a reviewer can actually use it, under ICDM's anonymity rules. ICDM does not run a separate stamped artifact-badging track the way some venues do (verify per edition); instead, the artifact's job is to be the **cited, anonymized evidence** that supports the paper. Because the Research Track is triple-blind and traditionally offers no rebuttal, the repository must be complete and anonymous *at submission time* — there is no later chance to reveal it. ## The repository the PDF must cite - Reference the code/data repository **inside the submitted PDF**. A repository not cited at submission is invisible to reviewers for the entire cycle (no rebuttal to add it later). - For the Research Track, the link must resolve to an **anonymized** location, not a named account, and the contents must reveal no identity. - For the 2026 **Applied Track (single-blind)**, anonymization of the artifact is not required the same way — but confirm the current call, and still avoid shipping secrets or private data. ## Anonymize for the triple-blind regime (Research Track) | Leak surface | Fix | |---|---| | Git history (author names, emails) | Export a fresh r
- The repository the PDF must cite
- Anonymize for the triple-blind regime (Research Track)
- Make it reviewer-usable
- Handle un-releasable data honestly
- Vignette: the commit that would have unmasked the authors
- Output format
smoke-check an anonymized ICDM reproduction package before citing it in the PDF python3 ../../../shared-resources/ml-conference-methods/code/check_repro_package.py \ then manually confirm: no .git, no author paths, no internal dataset names, one entry script, pinned deps, seed list present, README free of identity.
What does the icdm-artifact-evaluation skill do?
Use when packaging code, data, and logs for an ICDM (IEEE International Conference on Data Mining) paper - building the anonymized, history-scrubbed repository that the PDF must cite for a triple-blind Research Track submission, how the single-blind Applied Track changes what may be revealed, and the smoke checks that make an ICDM artifact reviewer-usable.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icdm-artifact-evaluation --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.