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 the reproducibility story of an ICCV paper, covering full recipe disclosure without a mandated compute form, protocol pinning for foundation-model and…
Install Iccv Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iccv-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iccv-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/ICCV-Skills/skills/iccv-reproducibility .claude/skills/iccv-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 "iccv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-reproducibility into .claude/skills/iccv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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/ICCV-Skills/skills/iccv-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 iccv-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iccv-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/ICCV-Skills/skills/iccv-reproducibility .agents/skills/iccv-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 "iccv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-reproducibility into .agents/skills/iccv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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 iccv-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iccv-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/ICCV-Skills/skills/iccv-reproducibility .cursor/skills/iccv-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 "iccv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-reproducibility into .cursor/skills/iccv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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 ICCV-Skills/skills/iccv-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 iccv-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills iccv-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/ICCV-Skills/skills/iccv-reproducibility .gemini/skills/iccv-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 "iccv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-reproducibility into .gemini/skills/iccv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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 iccv-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 iccv-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/ICCV-Skills/skills/iccv-reproducibility .github/skills/iccv-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 "iccv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-reproducibility into .github/skills/iccv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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 iccv-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 iccv-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/ICCV-Skills/skills/iccv-reproducibility .opencode/skills/iccv-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 "iccv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICCV-Skills/skills/iccv-reproducibility into .opencode/skills/iccv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iccv-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
iccv-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 721 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when hardening the reproducibility story of an ICCV paper, covering full recipe disclosure without a mandated compute form, protocol pinning for foundation-model and…
- Hardening the reproducibility story of an ICCV paper
- SKILL.md covers The two-year checkability test, Foundation-model era: pin the…, Recipe as a build artifact and Variance honesty at vision…, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering full recipe disclosure without a mandated compute form
What it does
Iccv Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening the reproducibility story of an ICCV paper, covering full recipe disclosure without a mandated compute form, protocol pinning for foundation-model and zero-shot evaluations, seed and variance honesty at vision training scale, and writing results that stay checkable across the two-year gap to the next ICCV.
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 the reproducibility story of an ICCV paper
- Covering full recipe disclosure without a mandated compute form
- Protocol pinning for foundation-model and zero-shot evaluations
- Seed and variance honesty at vision training scale
Example prompts
- “/iccv-reproducibility”
Requirements
- Python 3
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 toml).
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
Iccv Reproducibility loads about 1.6k tokens when it runs. Until then it costs about 87 tokens; SKILL.md has 721 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). 721 words, ~1,616 tokens.
Download SKILL.mdSave it as
.claude/skills/iccv-reproducibility/SKILL.md (or your agent's skills folder).- name
- iccv-reproducibility
- description
- Use when hardening the reproducibility story of an ICCV paper, covering full recipe disclosure without a mandated compute form, protocol pinning for foundation-model and zero-shot evaluations, seed and variance honesty at vision training scale, and writing results that stay checkable across the two-year gap to the next ICCV.
ICCV Reproducibility
ICCV 2025 imposed no compute-reporting form and no reproducibility checklist that could be verified at check time (2026-07-08) — which means the venue's reproducibility bar is enforced socially: by reviewers who re-implement things for a living, and by a two-year horizon in which your paper is the standing reference until the next ICCV. Absent a form, the paper itself must carry the full disclosure. This skill is the audit.
The two-year checkability test
A CVPR paper gets superseded in twelve months; an ICCV paper's numbers get re-quoted, re-run, and re-attacked for at least twenty-four. Write every result so that a stranger in the next odd year can adjudicate a discrepancy:
- Dataset version and split files named (not "standard split" — the standard moves), with checksums where licenses allow.
- Metric implementation cited by repo and version; identically named metrics differ across codebases by more than typical paper deltas.
- Pretraining corpus and checkpoint identified for every initialization; a gain that rides an undisclosed web-scale pretrain is a different claim than the paper makes.
- Evaluation resolution, crop policy, and test-time augmentation stated per table, since these silently absorb whole points.
Foundation-model era: pin the protocol, not just the seed
Much post-2023 ICCV work evaluates around large pretrained models, which adds reproducibility failure modes that classical training-recipe disclosure never covered:
| Moving part | What to pin in the paper |
|---|---|
| Backbone / VLM checkpoint | Exact identifier and revision hash, not the family name |
| Prompts and templates | Verbatim, in the supplement, including the ensemble if any |
| API-served models (if unavoidable) | Access dates + version string; state that decommissioning breaks exact reproduction |
| Zero-shot class lists / vocabularies | The literal list, since "the standard 80 classes" has variants |
| Retrieval corpora / support sets | Snapshot date and filtering rules |
A "zero-shot" table whose prompt engineering is unstated is not zero-anything; reviewers at ICCV increasingly ask.
Recipe as a build artifact
Maintain one machine-readable record per reported row, from the first experiment, and generate the implementation section from it rather than reconstructing memories in deadline week:
# ledger/tab2_row5.toml — the row is reproducible iff this file is complete
model = "ours-large"
init = "vitl14-<hash>, corpus: <name+version>"
data = { train = "co3d-v2@sha256:...", eval = "co3d-v2-test-list.txt" }
schedule = { optim = "adamw", lr = 3e-4, epochs = 60, batch = 512, warmup = 5 }
aug = ["rrc-336", "hflip"]
seeds = [0, 1, 2] # or [0] with flagged=true
hardware = "16xA100-40G, bf16"
eval = { resolution = 336, tta = false, metric_impl = "<repo>@<tag>" }
command = "python train.py -c configs/tab2_row5.toml"The ledger also answers rebuttal-week questions in minutes ("which schedule made Fig. 5?") — at ICCV those questions arrive in a seven-day window in May.
Variance honesty at vision budgets
Nobody multi-seeds a 16-GPU week ten times, and pretending otherwise persuades no one. The defensible pattern, stated in the paper's own words: cheap decisive experiments (the headline ablation, the small-backbone variant) run with ≥3 seeds and reported as mean ± std; the flagship run flagged explicitly as single; and no claim in the abstract resting on a margin smaller than the seed noise visible in your own tables. For stochastic evaluation (generation, sampling- based detection), repeat the evaluation pass and report its spread separately from training variance — the two get conflated constantly.
Show full SKILL.md (270 more words)Show less
Compute disclosure without a form
No mandated form means you choose the disclosure, and the cheap honest version is one paragraph: total GPU-hours for the flagship, per-experiment cost for the grid, hardware and precision, and wall-clock per training run. Two reasons to volunteer it. Reviewers calibrate "simple and effective" claims against what the method costs to obtain; and any efficiency or "real-time" adjective in your abstract is unfalsifiable without named hardware — an easy weakness for a reviewer to poke in a cycle where you get one page of rebuttal to answer.
Withheld test sets and server etiquette
Benchmarks with evaluation servers turn your test number into a receipt rather than a rerunnable command. Record submission IDs and dates in the ledger, stay inside per-week submission budgets (tuning on the server is the field's canonical sin and organizers publish shame lists), and always give readers the validation-set protocol whose numbers predict the server's — that is what they will actually reproduce.
Determinism paragraph, written once
State the posture instead of implying perfection: which RNGs were seeded, whether deterministic kernels were enabled (and the throughput cost if not), known nondeterminism sources (scatter atomics, multi-GPU reduction order, dataloader scheduling), and the reproduction tolerance you measured across identical-seed reruns. One measured tolerance sentence ("±0.15 mIoU across nodes") converts future "failed to reproduce" issues into calibration checks.
Reverify each cycle
- Whether 2027 introduces any reproducibility checklist, compute form, or code-submission expectation (none verified for 2025).
- Benchmark version churn since the last cycle — two years is long in dataset
time (
iccv-experimentscovers the drift audit). - Current supplement constraints that bound how much recipe detail ships.
Output format
[Checkability grade] two-year test: pass / gaps
[Ledger coverage] rows with complete recipes: n/m
[Foundation-model pins] checkpoints · prompts · vocabularies · API versions: pinned?
[Variance] multi-seeded: <list>; flagged single runs: <list>; claims vs noise: OK?
[Compute paragraph] present with hardware + GPU-hours: yes/no
[Fix list] <ordered by what a re-implementer hits 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 ICCV-Skills/skills/iccv-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Iccv 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 |
|---|---|---|---|---|---|---|
| Iccv 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
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 Iccv Reproducibility
What does Iccv Reproducibility do?
A skill your agent uses when hardening the reproducibility story of an ICCV paper, covering full recipe disclosure without a mandated compute form, protocol pinning for foundation-model and…. Iccv Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening the reproducibility story of an ICCV paper, covering full recipe disclosure without a mandated compute form, protocol pinning for foundation-model and zero-shot evaluations, seed and variance honesty at vision training scale, and writing results that stay checkable across the two-year gap to the next ICCV.
When should I use Iccv Reproducibility?
Iccv Reproducibility fits situations like: hardening the reproducibility story of an ICCV paper; covering full recipe disclosure without a mandated compute form; protocol pinning for foundation-model and zero-shot evaluations; seed and variance honesty at vision training scale.
How do I install Iccv Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iccv-reproducibility -a claude-code`. Or copy the skill folder (ICCV-Skills/skills/iccv-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/iccv-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Iccv Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iccv-reproducibility -a codex`. Or copy the skill folder (ICCV-Skills/skills/iccv-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/iccv-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Iccv 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 iccv-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/iccv-reproducibility, .gemini/skills/iccv-reproducibility, .github/skills/iccv-reproducibility and .opencode/skills/iccv-reproducibility in your project.
What does Iccv Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Iccv Reproducibility is instructions for the agent only. Our summary lists: Python 3.
Does Iccv 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 Iccv 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 Iccv Reproducibility use?
Iccv 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 Iccv 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 Iccv Reproducibility?
Skills that share tags, products or a category with Iccv 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 Iccv 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.