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 strengthening the reproducibility of a COLT (Conference on Learning Theory) paper, where reproducing means re-deriving — complete proofs, explicit assumptions, tracked…
Install Colt Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colt-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colt-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/COLT-Skills/skills/colt-reproducibility .claude/skills/colt-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 "colt-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-reproducibility into .claude/skills/colt-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-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/COLT-Skills/skills/colt-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 colt-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colt-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/COLT-Skills/skills/colt-reproducibility .agents/skills/colt-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 "colt-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-reproducibility into .agents/skills/colt-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-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 colt-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colt-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/COLT-Skills/skills/colt-reproducibility .cursor/skills/colt-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 "colt-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-reproducibility into .cursor/skills/colt-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-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 COLT-Skills/skills/colt-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 colt-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills colt-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/COLT-Skills/skills/colt-reproducibility .gemini/skills/colt-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 "colt-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-reproducibility into .gemini/skills/colt-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-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 colt-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 colt-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/COLT-Skills/skills/colt-reproducibility .github/skills/colt-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 "colt-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-reproducibility into .github/skills/colt-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-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 colt-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 colt-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/COLT-Skills/skills/colt-reproducibility .opencode/skills/colt-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 "colt-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/COLT-Skills/skills/colt-reproducibility into .opencode/skills/colt-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "colt-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
colt-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 787 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening the reproducibility of a COLT (Conference on Learning Theory) paper, where reproducing means re-deriving — complete proofs, explicit assumptions, tracked…
- Works in 5 steps: Print the numbered-statement list (all… → For each, a non-author coauthor answers:… → Verify proofs in dependency order,… → …
- Strengthening the reproducibility of a COLT (Conference on Learning Theory) paper
- SKILL.md covers The re-derivability standard, Audit table: where…, Assumption bookkeeping pattern and Numerical illustrations, when…, plus 4 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Colt Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening the reproducibility of a COLT (Conference on Learning Theory) paper, where reproducing means re-deriving — complete proofs, explicit assumptions, tracked constants, correctly invoked external results, self-contained notation — plus seeds and scripts for any numerical illustration the paper carries.
Its SKILL.md is about 1.6k 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. 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
- Strengthening the reproducibility of a COLT (Conference on Learning Theory) paper
- Where reproducing means re-deriving — complete proofs
- Explicit assumptions
- Tracked constants
Example prompts
- “/colt-reproducibility”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Print the numbered-statement list (all definitions, assumptions, lemmas, theorems).
- For each, a non-author coauthor answers: can I state precisely what this claims,
- Verify proofs in dependency order, marking each line verified/unverified; the
- Re-check every external citation against the cited source's actual hypotheses —
- Reconcile body sketches against appendix proofs: a sketch that describes an older
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
No scripts in the folder and no shell commands in SKILL.md (its code samples are latex).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Colt Reproducibility loads about 1.6k tokens when it runs. Until then it costs about 86 tokens; SKILL.md has 787 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). 787 words, ~1,633 tokens.
Download SKILL.mdSave it as
.claude/skills/colt-reproducibility/SKILL.md (or your agent's skills folder).- name
- colt-reproducibility
- description
- Use when strengthening the reproducibility of a COLT (Conference on Learning Theory) paper, where reproducing means re-deriving — complete proofs, explicit assumptions, tracked constants, correctly invoked external results, self-contained notation — plus seeds and scripts for any numerical illustration the paper carries.
COLT Reproducibility
At COLT, reproducibility means a competent reader can re-derive every claim from the PDF alone. There is no reproducibility checklist form in the COLT 2026 CFP (checked 2026-07-08) — the venue enforces the property the hard way, through referees who attempt the re-derivation during review. This skill is the pre-submission audit that makes that attempt succeed.
The re-derivability standard
A theorem is reproducible when all of the following hold:
- Its statement is formally complete: every symbol quantified, every assumption named in the statement itself or cited by an explicit assumption label, the probability space and adversary model unambiguous.
- Its proof exists in full inside the submitted PDF — COLT's unlimited appendix removes every excuse for "omitted for lack of space."
- Each proof step is locally checkable: an expert reading line k needs only lines 1..k-1, cited external results, and standard background — never your unpublished intuition.
- Constants and parameter regimes survive the chain: if Theorem 1 needs n ≥ C·d·log(1/δ), the reader can trace what C is or where it was declared absolute.
Audit table: where re-derivation fails
| Failure mode | Typical symptom in the PDF | Repair |
|---|---|---|
| Hypothesis smuggling | Proof uses independence never assumed | Add the assumption to the statement, or redo the step |
| External-result misuse | "By [X], ..." where [X] needs bounded support you don't have | Check [X]'s hypotheses; find the right variant or prove a lemma |
| Constant drift | C doubles silently between two displays | Number constants (C_1, C_2, ...) and track them in a ledger |
| Case leakage | "The case d=1 is analogous" when it is not | Write the case or prove the reduction |
| Notation overload | Same symbol for a filtration and a function class | One notation table, enforced globally |
| Silent regime switch | Bound proved for T large, quoted for all T | State the threshold explicitly in the theorem |
Assumption bookkeeping pattern
Give assumptions their own numbered environment and cite them by label everywhere:
\newtheorem{assumption}{Assumption}
\begin{assumption}[Bounded losses]\label{ass:bounded}
For all $t$, the loss $\ell_t$ maps to $[0,1]$.
\end{assumption}
\begin{theorem}\label{thm:regret}
Under Assumptions~\ref{ass:bounded} and~\ref{ass:oblivious}, the algorithm's
regret satisfies $R_T \le 4\sqrt{T \log K}$ for all $T \ge 1$.
\end{theorem}The payoff is auditable dependency: a reviewer can grep which theorems rely on obliviousness, and your rebuttal can answer "is Assumption 2 needed for Theorem 3?" with a pointer instead of an essay.
Numerical illustrations, when present
Some COLT papers plot a simulated regret curve or a phase transition to illustrate a bound. The theory community's floor for those figures:
- Fixed seeds, stated replication counts, and error bars whose meaning the caption defines; a noisy single run "consistent with the theory" persuades no one here.
- The simulated regime must be the theorem's regime — matching horizon, dimension, and noise assumptions — or the mismatch must be acknowledged as exploratory.
- The generating script should be one dependency-light file, releasable after acceptance; during review, describe the procedure precisely in the appendix since no upload channel exists.
- Never let an illustration silently extend the claim ("the bound appears to hold for heavy tails too") without labeling it as conjecture.
Show full SKILL.md (323 more words)Show less
Vignette: the spine of a lower-bound paper
Consider a submission whose main result is a $\Omega(\sqrt{TK})$ lower bound via a new instance family. Its re-derivability spine, in the order a referee will attack it:
- the instance family's construction, with every distribution parameter explicit and the randomization protocol (oblivious vs. adaptive) named;
- the information-theoretic step (say, a KL-divergence calculation) with the exact divergence bound displayed, not cited as "standard";
- the reduction from learner performance to the divergence quantity, where hypothesis smuggling most often hides (does the argument secretly assume deterministic learners? say so or generalize);
- the final optimization over instance parameters, with the maximizing choice written out — "choosing ε appropriately" is where constants go to die.
A referee who can walk this spine without leaving the PDF marks correctness resolved; each externalized step converts into a review question, and three review questions into a reject.
Pre-submission re-derivation drill
- Print the numbered-statement list (all definitions, assumptions, lemmas, theorems).
- For each, a non-author coauthor answers: can I state precisely what this claims, including quantifiers, without reading the proof? Rewrite until yes.
- Verify proofs in dependency order, marking each line verified/unverified; the
colt-artifact-evaluationskill's ledger format works here. - Re-check every external citation against the cited source's actual hypotheses — allocate real time; this is where careful papers die.
- Reconcile body sketches against appendix proofs: a sketch that describes an older proof strategy than the appendix executes reads as a gap to a referee.
Cycle-volatility warnings
- If a future COLT cycle adds any checklist, code policy, or supplementary channel, the current CFP announces it; the 2026 cycle had none (待核实 in later cycles).
- The 12-page body and single-PDF rules that shape where proofs live are the 2026 formulation; re-read the live CFP before restructuring a paper around them.
- Formatting of assumptions and environments is a house-style choice, not a CFP rule; the CFP-level constraints remain the 12-page body and the single PDF.
Output format
[Re-derivability verdict] re-derivable / gaps found
[Statement completeness] <theorems needing quantifier or assumption repair>
[Constant ledger] tracked / drift at <displays>
[External-results audit] <citations with unchecked hypotheses>
[Illustration floor] seeds+replications stated / absent / no numerics in paper© 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 COLT-Skills/skills/colt-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Colt 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 |
|---|---|---|---|---|---|---|
| Colt Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | 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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Related
Categories
Questions about Colt Reproducibility
What does Colt Reproducibility do?
A skill your agent uses when strengthening the reproducibility of a COLT (Conference on Learning Theory) paper, where reproducing means re-deriving — complete proofs, explicit assumptions, tracked…. Colt Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening the reproducibility of a COLT (Conference on Learning Theory) paper, where reproducing means re-deriving — complete proofs, explicit assumptions, tracked constants, correctly invoked external results, self-contained notation — plus seeds and scripts for any numerical illustration the paper carries.
When should I use Colt Reproducibility?
Colt Reproducibility fits situations like: strengthening the reproducibility of a COLT (Conference on Learning Theory) paper; where reproducing means re-deriving — complete proofs; explicit assumptions; tracked constants.
How do I install Colt Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colt-reproducibility -a claude-code`. Or copy the skill folder (COLT-Skills/skills/colt-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/colt-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Colt Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill colt-reproducibility -a codex`. Or copy the skill folder (COLT-Skills/skills/colt-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/colt-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Colt 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 colt-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/colt-reproducibility, .gemini/skills/colt-reproducibility, .github/skills/colt-reproducibility and .opencode/skills/colt-reproducibility in your project.
What does Colt Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Colt Reproducibility is instructions for the agent only.
Does Colt Reproducibility access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Colt 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 Colt Reproducibility use?
Colt 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 Colt Reproducibility use?
About 1.6k tokens (SKILL.md is roughly 6.5k 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 Colt Reproducibility?
Skills that share tags, products or a category with Colt 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 Colt 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.