Peer Review
K-Dense-AI/claude-scientific-writer
Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments.
A skill your agent uses when hardening a COLM paper's reproducibility story — pinning open-weight checkpoints and tokenizers, handling API-model drift and deprecation honestly, versioning evaluation…
Install Colm Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colm-reproducibility --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/COLM-Skills/skills/colm-reproducibility .claude/skills/colm-reproducibility && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "colm-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLM-Skills/skills/colm-reproducibility into .claude/skills/colm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colm-reproducibility", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLM-Skills/skills/colm-reproducibilityType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colm-reproducibility --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/COLM-Skills/skills/colm-reproducibility .agents/skills/colm-reproducibility && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "colm-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLM-Skills/skills/colm-reproducibility into .agents/skills/colm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colm-reproducibility", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colm-reproducibility --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/COLM-Skills/skills/colm-reproducibility .cursor/skills/colm-reproducibility && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "colm-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLM-Skills/skills/colm-reproducibility into .cursor/skills/colm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colm-reproducibility", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path COLM-Skills/skills/colm-reproducibility--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colm-reproducibility --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/COLM-Skills/skills/colm-reproducibility .gemini/skills/colm-reproducibility && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "colm-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLM-Skills/skills/colm-reproducibility into .gemini/skills/colm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colm-reproducibility", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colm-reproducibilityInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-reproducibility -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/COLM-Skills/skills/colm-reproducibility .github/skills/colm-reproducibility && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "colm-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLM-Skills/skills/colm-reproducibility into .github/skills/colm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colm-reproducibility", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-reproducibility -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colm-reproducibility --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/COLM-Skills/skills/colm-reproducibility .opencode/skills/colm-reproducibility && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "colm-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLM-Skills/skills/colm-reproducibility into .opencode/skills/colm-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colm-reproducibility", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
colm-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 770 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when hardening a COLM paper's reproducibility story — pinning open-weight checkpoints and tokenizers, handling API-model drift and deprecation honestly, versioning evaluation…
- Hardening a COLM papers reproducibility story — pinning open-weight checkpoints and tokenizers
- SKILL.md covers The three reproducibility layers, API models: the honesty protocol, What "pinned" means, concretely and Training-side recipe (budgeted…, plus 5 more sections
- Calls git
- Handling API-model drift and deprecation honestly
What it does
Colm Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Reproducible research and LLM evaluation. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Hardening a COLM papers reproducibility story — pinning open-weight checkpoints and tokenizers
- Handling API-model drift and deprecation honestly
- Versioning evaluation harnesses and prompts
- Disclosing compute
Example prompts
- “/colm-reproducibility”
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
git
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Colm Reproducibility loads about 1.7k tokens when it runs. Until then it costs about 83 tokens; SKILL.md has 770 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 770 words, ~1,662 tokens.
Download SKILL.mdSave it as
.claude/skills/colm-reproducibility/SKILL.md (or your agent's skills folder).- name
- colm-reproducibility
- description
- 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.
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 which layer each claim lives in.
The three reproducibility layers
| Layer | Who can redo it | Your obligation |
|---|---|---|
| Exact | Anyone with the artifact | Pinned checkpoint + harness + prompts + decoding → same numbers (within stated sampling noise) |
| Budgeted | A lab with comparable compute | Full training recipe: data, hyperparameters, schedule, hardware, wall-clock, known failure modes |
| Drifting | Nobody, eventually | API-model results: version string, query dates, and an expiry-honest caveat in the paper |
A paper whose headline claim lives entirely in the drifting layer needs to say so on the page where the claim is made — and ideally to anchor the finding on at least one open-weight model so the exact layer is not empty.
API models: the honesty protocol
Closed models are legitimate scientific objects at COLM — much of the field studies them — but they demand a specific discipline:
- Record the full version identifier and the date range of every query; "GPT-class model, spring 2026" is not a citation.
- Treat model deprecation as a known risk: state which results a reader can expect to reproduce only while the version is served.
- Do not average over silent version changes — if a provider updated mid-experiment, the runs before and after are different systems.
- Cache raw responses. The response archive is the reproducible artifact when the model disappears; releasing (input, output) pairs, licenses permitting, converts a drifting claim into an exact one about the recorded behavior.
What "pinned" means, concretely
# An open-weight result is pinned when all four hashes exist in the paper/repo:
MODEL_REV=8f3c21a # checkpoint revision, not just the model name
TOKENIZER_REV=8f3c21a # tokenizers change under stable names too
HARNESS_SHA=$(git -C eval-harness rev-parse --short HEAD)
PROMPTS_SHA=$(sha256sum prompts/*.txt | sha256sum | cut -c1-12)
echo "model=$MODEL_REV tok=$TOKENIZER_REV harness=$HARNESS_SHA prompts=$PROMPTS_SHA"The harness hash matters more than newcomers expect: extraction rules, answer normalization, and few-shot formatting differ across harness releases and can move benchmark scores by points. A score without a harness version is not comparable to anyone else's score.
Training-side recipe (budgeted layer)
If the paper trains or fine-tunes, report: base checkpoint and revision; dataset composition with filtering steps and, where possible, released data or reproducible filtering code; optimizer, learning-rate schedule, batch size, steps/epochs, precision; hardware and GPU-hours; and everything that went wrong — divergences, restarts, hand-tuned interventions. The failure narrative is what separates a recipe someone can follow from a recipe that only worked once.
Compute and cost disclosure
Give a single table: training GPU-hours (or API fine-tuning spend), evaluation GPU-hours or API token spend, and totals including preliminary experiments if material. This is a COLM-community expectation for two reasons — cost is part of any efficiency claim, and disclosure lets smaller groups judge whether replication is even feasible before they try.
Show full SKILL.md (312 more words)Show less
The availability statement
Write it as commitments, not vibes. For each artifact class — code, prompts, trained weights, training data, evaluation data, raw model outputs — state one of: released at submission (anonymized), released on acceptance, available on request, or cannot be released because <specific reason: license, privacy, ToS>. "Code will be made available" with no object and no date is the pattern reviewers have learned to distrust.
Note: no dedicated COLM reproducibility checklist or artifact-certification track was verifiable for the 2026 cycle (checked 2026-07-08, 待核实 each edition) — this discipline is enforced by reviewers, not by a form, which in practice makes it more visible in scores, not less.
When you cannot release
Legitimate blockers exist; the discipline is to substitute the strongest releasable proxy instead of going silent:
| Blocked artifact | Reason | Releasable proxy |
|---|---|---|
| Training corpus | Licensed / scraped text | Filtering code + corpus statistics + a datasheet of sources |
| Fine-tuned weights | Base-model license forbids derivatives | Training recipe + LoRA/adapter deltas if permitted + eval harness |
| Raw API outputs | Provider ToS limits republication | Scored per-item results + a small quoted-excerpts sample within fair use |
| Human-eval transcripts | Annotator privacy | Aggregated ratings + instructions + anonymized examples with consent |
| Internal eval set | Planned reuse as a held-out set | A public dev split + hash commitments to the held-out items |
The hash-commitment trick in the last row deserves wider use: publishing item hashes today proves later that the held-out set did not move, which protects both you and the benchmark.
Pre-submission audit
- Every number's layer identified (exact / budgeted / drifting), and the paper says which.
- Four hashes present for every open-weight result; version strings + query dates for every API result; raw responses cached.
- Training recipe complete enough that its absence would be the reviewer's only question.
- Compute table present.
- Availability statement itemized by artifact class with concrete commitments.
- Anonymization of released artifacts checked (
colm-submissionsweep) — an identity-leaking repo is worse than none at submission time.
Output format
[Repro layers] exact: <claims> / budgeted: <claims> / drifting: <claims>
[Pinning audit] model ▢ tokenizer ▢ harness ▢ prompts ▢ query-dates ▢
[Response cache] exists / missing for API experiments
[Compute table] present / absent
[Availability] itemized commitments / vague — rewrite
[Top gap] <the one omission a reviewer will find first>© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in COLM-Skills/skills/colm-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Colm Reproducibility next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Colm Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Peer ReviewK-Dense-AI/claude-scientific-writer | 2.4k | 2 repos | ~3.1k | Automated safety check: Notes | MIT | |
| CHARLS Paper Reproduction Guidexjtulyc/MedgeClaw | 617 | 1 repos | ~1.8k | Automated safety check: Pass | None | |
| Compute Environment Setupaipoch/open-science | 5.5k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Figure Styleaipoch/open-science | 5.5k | — | ~5.1k | Automated safety check: Pass | Apache-2.0 | |
| Add Bactopia Toolbactopia/bactopia | 522 | — | ~4.1k | Automated safety check: Pass | MIT |
Similar skills
CHARLS Paper Reproduction Guide
xjtulyc/MedgeClaw
Guides an agent through reproducing papers built on the CHARLS health and retirement survey, from variable mapping to cognition, depression and isolation scores.
Compute Environment Setup
aipoch/open-science
Prepares setup instructions and a named activation file for a user-managed software environment on an Open-Science SSH or Slurm compute host.
Figure Style
aipoch/open-science
Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots.
Add Bactopia Tool
bactopia/bactopia
Scaffold a complete Bactopia Tool across all three tiers -- module, subworkflow, and workflow entry point under workflows/bactopia-tools/.
Modeling Code and Result Contracts
yushui2022/MathModel-Skill
Generates result-evidence contracts, tables and runnable q1 to q3 modeling code scaffolds for a math modeling paper from a model route, a data plan and cleaned data.
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Categories
Questions about Colm Reproducibility
What does Colm Reproducibility do?
A skill your agent uses when hardening a COLM paper's reproducibility story — pinning open-weight checkpoints and tokenizers, handling API-model drift and deprecation honestly, versioning evaluation…. Colm Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. 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.
When should I use Colm Reproducibility?
Colm Reproducibility fits situations like: hardening a COLM papers reproducibility story — pinning open-weight checkpoints and tokenizers; handling API-model drift and deprecation honestly; versioning evaluation harnesses and prompts; disclosing compute.
How do I install Colm Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-reproducibility -a claude-code`. Or copy the skill folder (COLM-Skills/skills/colm-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/colm-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Colm Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-reproducibility -a codex`. Or copy the skill folder (COLM-Skills/skills/colm-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/colm-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Colm Reproducibility in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colm-reproducibility -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/colm-reproducibility, .gemini/skills/colm-reproducibility, .github/skills/colm-reproducibility and .opencode/skills/colm-reproducibility in your project.
What does Colm Reproducibility need to run?
Going by SKILL.md and its folder, Colm Reproducibility needs the command-line tools its instructions call (git).
Does Colm Reproducibility access the network?
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Is Colm Reproducibility safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Colm Reproducibility use?
Colm Reproducibility is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Colm Reproducibility use?
About 1.7k tokens (SKILL.md is roughly 6.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Colm Reproducibility?
Skills that share tags, products or a category with Colm Reproducibility: Peer Review (K-Dense-AI/claude-scientific-writer, 2.4k stars), CHARLS Paper Reproduction Guide (xjtulyc/MedgeClaw, 617 stars), Compute Environment Setup (aipoch/open-science, 5.5k stars) and Figure Style (aipoch/open-science, 5.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Colm Reproducibility?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.