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 CVPR paper's reproducibility story, covering the Compute Reporting Form's hardware and compute sections, training-recipe disclosure, benchmark protocol and…
Install Cvpr Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cvpr-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cvpr-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/CVPR-Skills/skills/cvpr-reproducibility .claude/skills/cvpr-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 "cvpr-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-reproducibility into .claude/skills/cvpr-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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/CVPR-Skills/skills/cvpr-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 cvpr-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cvpr-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/CVPR-Skills/skills/cvpr-reproducibility .agents/skills/cvpr-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 "cvpr-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-reproducibility into .agents/skills/cvpr-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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 cvpr-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cvpr-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/CVPR-Skills/skills/cvpr-reproducibility .cursor/skills/cvpr-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 "cvpr-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-reproducibility into .cursor/skills/cvpr-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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 CVPR-Skills/skills/cvpr-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 cvpr-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills cvpr-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/CVPR-Skills/skills/cvpr-reproducibility .gemini/skills/cvpr-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 "cvpr-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-reproducibility into .gemini/skills/cvpr-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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 cvpr-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 cvpr-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/CVPR-Skills/skills/cvpr-reproducibility .github/skills/cvpr-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 "cvpr-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-reproducibility into .github/skills/cvpr-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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 cvpr-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 cvpr-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/CVPR-Skills/skills/cvpr-reproducibility .opencode/skills/cvpr-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 "cvpr-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/CVPR-Skills/skills/cvpr-reproducibility into .opencode/skills/cvpr-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cvpr-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
cvpr-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 733 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 CVPR paper's reproducibility story, covering the Compute Reporting Form's hardware and compute sections, training-recipe disclosure, benchmark protocol and…
- Hardening a CVPR papers reproducibility story
- SKILL.md covers The CRF as a reproducibility…, The recipe ledger, Benchmark hygiene that… and Variance at vision scale, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering the Compute Reporting Forms hardware and compute sections
What it does
Cvpr Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening a CVPR paper's reproducibility story, covering the Compute Reporting Form's hardware and compute sections, training-recipe disclosure, benchmark protocol and split hygiene, seed and variance reporting for vision experiments, and closing the gaps reviewers probe at a benchmark-driven venue.
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
- Hardening a CVPR papers reproducibility story
- Covering the Compute Reporting Forms hardware and compute sections
- Training-recipe disclosure
- Benchmark protocol and split hygiene
Example prompts
- “s reproducibility story, covering the Compute Reporting Form”
- “/cvpr-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
No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml).
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
Cvpr Reproducibility loads about 1.6k tokens when it runs. Until then it costs about 83 tokens; SKILL.md has 733 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). 733 words, ~1,626 tokens.
Download SKILL.mdSave it as
.claude/skills/cvpr-reproducibility/SKILL.md (or your agent's skills folder).- name
- cvpr-reproducibility
- description
- Use when hardening a CVPR paper's reproducibility story, covering the Compute Reporting Form's hardware and compute sections, training-recipe disclosure, benchmark protocol and split hygiene, seed and variance reporting for vision experiments, and closing the gaps reviewers probe at a benchmark-driven venue.
CVPR Reproducibility
At CVPR, reproducibility failures rarely look like fraud; they look like a table nobody can match because one augmentation flag, one crop size, or one pretraining corpus went unstated. This skill hardens the paper against that fate, anchored in the 2026-cycle machinery (checked 2026-07-08): the Compute Reporting Form, the anonymous supplement, and reviewers trained on a decade of un-reproducible state-of-the-art claims.
The CRF as a reproducibility floor
The 2026 cycle attached a Compute Reporting Form to every submission — Section 1 (hardware specification) and Section 5 (verification) mandatory, deeper sections optional, with an explicit opt-out route for proprietary constraints. Treat the mandatory floor as the start, not the ceiling:
| CRF layer | What it pins down | Why reviewers care |
|---|---|---|
| Hardware (mandatory) | GPU model, count, primary configuration | Grounds every "real-time" and "efficient" claim |
| Verification (mandatory) | Author attestation | Somebody owns the numbers |
| Task/compute (optional) | GPU-hours or FLOPs per result | Separates a 4-GPU method from a 512-GPU method |
| Full logs (optional) | Run-level records | The strongest possible "we actually ran this" |
If your contribution is efficiency, filling only the mandatory sections undercuts your own claim — report the compute and let the numbers argue.
The recipe ledger
Vision results are recipe-sensitive. Maintain one machine-readable ledger from the first experiment, and generate the paper's implementation-details paragraph from it instead of reconstructing details in deadline week:
# recipe-ledger.yaml — one block per reported table row
table3_row2:
backbone: vit-b16, pretrain: <corpus + checkpoint hash>
data: <dataset version + split file sha256>
aug: [rrc-224, hflip, randaug-m9]
optim: adamw, lr: 1.0e-4, sched: cosine, epochs: 90, batch: 1024
seed: 3407 # and whether cudnn deterministic was set
hardware: 8xA100-80G # must agree with CRF Section 1
command: scripts/train.sh configs/table3_row2.yamlThe ledger's second job is internal: when a reviewer asks in January which schedule produced Figure 5, you answer from the file in minutes.
Benchmark hygiene that survives scrutiny
- Split discipline. Name the exact split files; never tune on anything downstream of the test set — including "just checking" runs that leak into architecture choices.
- Test-set frequency. State how many times the test set was evaluated. Val-set ablations + one final test run is the defensible pattern.
- Metric provenance. Cite the metric implementation (which mAP variant? whose FID code, which feature extractor?). Same-name metrics differ across repos by whole points.
- Pretraining disclosure. "Initialized from X" changes the comparison class; a method beating from-scratch baselines while riding a web-scale pretrain is a different claim.
- Baseline re-runs. Say which baseline numbers are quoted from papers versus re-run under your protocol, and match backbones and schedules when you re-run.
Variance at vision scale
Full multi-seed grids are often unaffordable at modern training budgets, and reviewers know it. The credible middle ground: multi-seed the cheap decisive experiments (small backbone, headline ablation) and report mean ± std; run the flagship once but state so explicitly; never present a 0.2-point gain as a finding when the same table shows seed-level noise of 0.4. If evaluation itself is stochastic (generation, sampling-based detection), repeat evaluation, not just training.
Show full SKILL.md (297 more words)Show less
Determinism knobs, documented once
Bit-exact reproduction is often impossible on GPU stacks, but stating your determinism posture is always possible and costs three lines in the supplement:
- Which seeds were fixed (Python/NumPy/framework/dataloader workers) and whether deterministic kernels were enabled — note the speed cost if they were not.
- Sources of accepted nondeterminism: atomics in scatter ops, multi-GPU reduction order, augmentation pipelines keyed to worker scheduling.
- The reproduction tolerance you actually observed: "re-running row 2 varies by ±0.15 mAP across identical-seed runs on different node counts" tells a reproducer whether their 0.1-point discrepancy is a bug or physics.
Teams that measure this once, early, stop having the "is 78.4 vs 78.6 a failure to reproduce?" argument — with reviewers and with themselves.
Statement-level honesty
Reproducibility text is a claims surface. "Code will be released" is a promise the community tracks; "results reproducible from the supplement" is checkable in January. Write the availability paragraph to match what is genuinely packaged: what ships in the supplement now, what is released at camera-ready (datasets claimed as contributions must be public by then — verified 2026 policy), and what cannot be released and why.
Held-out evaluation servers
Several vision benchmarks score on withheld test sets via submission servers with rate limits. This changes reproducibility mechanics: your reported test number is a server receipt, not a rerunnable command. Record the submission ID and date in the recipe ledger, respect per-week submission caps as an ethics matter (burning entries to tune on test is the community's canonical sin), and give reproducers the exact validation-set protocol that predicts the server number.
Reverify each cycle
- CRF structure, which sections are mandatory, and award tie-ins.
- Any reproducibility checklist added to the OpenReview form.
- Supplement caps that constrain how much recipe/code detail ships at review time (2026 caps 待核实).
Output format
[Repro grade] recipe-complete / gaps found
[CRF] hardware row consistent with paper claims: yes/no; optional sections: <filled?>
[Ledger] rows covering all reported tables: <n/m>
[Benchmark hygiene] splits · test-set count · metric provenance · pretrain disclosure
[Variance] multi-seeded: <experiments>; single-run flagged: <experiments>
[Fix list] <ordered, highest reviewer-visibility 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 CVPR-Skills/skills/cvpr-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Cvpr 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 |
|---|---|---|---|---|---|---|
| Cvpr 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 |
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CHARLS Paper Reproduction Guide
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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
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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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Questions about Cvpr Reproducibility
What does Cvpr Reproducibility do?
A skill your agent uses when hardening a CVPR paper's reproducibility story, covering the Compute Reporting Form's hardware and compute sections, training-recipe disclosure, benchmark protocol and…. Cvpr Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening a CVPR paper's reproducibility story, covering the Compute Reporting Form's hardware and compute sections, training-recipe disclosure, benchmark protocol and split hygiene, seed and variance reporting for vision experiments, and closing the gaps reviewers probe at a benchmark-driven venue.
When should I use Cvpr Reproducibility?
Cvpr Reproducibility fits situations like: hardening a CVPR papers reproducibility story; covering the Compute Reporting Forms hardware and compute sections; training-recipe disclosure; benchmark protocol and split hygiene.
How do I install Cvpr Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cvpr-reproducibility -a claude-code`. Or copy the skill folder (CVPR-Skills/skills/cvpr-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/cvpr-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Cvpr Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cvpr-reproducibility -a codex`. Or copy the skill folder (CVPR-Skills/skills/cvpr-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/cvpr-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Cvpr 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 cvpr-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/cvpr-reproducibility, .gemini/skills/cvpr-reproducibility, .github/skills/cvpr-reproducibility and .opencode/skills/cvpr-reproducibility in your project.
What does Cvpr Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Cvpr Reproducibility is instructions for the agent only.
Does Cvpr 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 Cvpr 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 Cvpr Reproducibility use?
Cvpr 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 Cvpr 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 Cvpr Reproducibility?
Skills that share tags, products or a category with Cvpr 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 Cvpr 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.