colm-reproducibility
Use when hardening a COLM paper's reproducibility story — pinning open-weight checkpoints and tokenizers, handling API-model drift and deprecation honestly, versioning evaluation harnesses and prompts, disclosing compute, and writing availability statements that distinguish what is releasable from what is not.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-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.
# COLM Reproducibility Reproducibility at a language-modeling venue is a different problem than at a classical ML venue, because the central artifact — the model — is often either too large to rerun or owned by someone else and changing weekly. COLM papers therefore need a *layered* story: what any reader can redo, what a funded lab can redo, and what will drift no matter what anyone does. Say whi
What does the colm-reproducibility skill do?
Use when hardening a COLM paper's reproducibility story — pinning open-weight checkpoints and tokenizers, handling API-model drift and deprecation honestly, versioning evaluation harnesses and prompts, disclosing compute, and writing availability statements that distinguish what is releasable from what is not.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-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.