colm-supplementary
Use when deciding what goes into a COLM paper's appendices and supplementary material versus the strict 9-page main text — verbatim prompts, full evaluation configurations, per-task result tables, contamination analyses, human-evaluation protocols, and anonymized code/data packages that survive double-blind review.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-supplementary --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 Supplementary Material The 2026 format gives you a strict 9-page main text, unlimited citation pages, and appendix space whose guaranteed audience is *nobody* — reviewers may read it, the rebuttal may point into it, but no rule obliges anyone to open it. That asymmetry is the whole design principle: the main text carries every load-bearing element, and the supplement carries the *audit trail* that makes the main text checkable. ## The placement rule Ask of each artifact: *is this needed to believe the claim, or to re-run it?* Believe → main text. Re-run → appendix/supplement. Both → summarize in the main text, deposit fully in the appendix, and cross-reference in both directions. | Artifact | Placement | Note | |---|---|---| | Representative prompt (or skeleton) | Main text | Readers cannot judge an LM evaluation without seeing how the model was asked | | Full prompt set, verbatim, incl. system prompts | Appendix | Paraphrased prompts are not reproducible; include exact strings | | Decoding configs per experiment | Appendix table | One row per reported table cell group (`colm-experiments`) | | Contamination analysis method + results | Summary in main text; full in appendix |
- The placement rule
- LM-specific supplement content reviewers actually use
- Anonymization of the code/data package
- Referencing the supplement from the main text
- Ordering the appendix for the reader you hope exists
- What not to include
- Timing note for the 2026-style calendar
- Output format
Build the review package from a clean export — never zip a working directory
git archive --format=tar HEAD | (mkdir -p /tmp/colm-pkg && tar -x -C /tmp/colm-pkg)
grep -rniE 'wandb|hf_[A-Za-z0-9]{20,}|api[_-]?key|/home/[a-z]|<lab-name>' /tmp/colm-pkg | head
grep -rlE 'from_pretrained\("[^"]*/' /tmp/colm-pkg --include='*.py' | head # org-scoped model IDsWhat does the colm-supplementary skill do?
Use when deciding what goes into a COLM paper's appendices and supplementary material versus the strict 9-page main text — verbatim prompts, full evaluation configurations, per-task result tables, contamination analyses, human-evaluation protocols, and anonymized code/data packages that survive double-blind review.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-supplementary --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.