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 WACV paper, covering the recipe ledger for constraint-aware systems, benchmark and split hygiene, seed and session honesty, device…
Install Wacv Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wacv-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wacv-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/WACV-Skills/skills/wacv-reproducibility .claude/skills/wacv-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 "wacv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-reproducibility into .claude/skills/wacv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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/WACV-Skills/skills/wacv-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 wacv-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wacv-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/WACV-Skills/skills/wacv-reproducibility .agents/skills/wacv-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 "wacv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-reproducibility into .agents/skills/wacv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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 wacv-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wacv-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/WACV-Skills/skills/wacv-reproducibility .cursor/skills/wacv-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 "wacv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-reproducibility into .cursor/skills/wacv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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 WACV-Skills/skills/wacv-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 wacv-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills wacv-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/WACV-Skills/skills/wacv-reproducibility .gemini/skills/wacv-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 "wacv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-reproducibility into .gemini/skills/wacv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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 wacv-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 wacv-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/WACV-Skills/skills/wacv-reproducibility .github/skills/wacv-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 "wacv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-reproducibility into .github/skills/wacv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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 wacv-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 wacv-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/WACV-Skills/skills/wacv-reproducibility .opencode/skills/wacv-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 "wacv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/WACV-Skills/skills/wacv-reproducibility into .opencode/skills/wacv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wacv-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
wacv-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~936 tokens
- SKILL.md length
- 379 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 WACV paper, covering the recipe ledger for constraint-aware systems, benchmark and split hygiene, seed and session honesty, device…
- Strengthening the reproducibility of a WACV paper
- SKILL.md covers The recipe ledger, Constraint-aware reproducibility, Seed and session honesty and Sync across the two rounds, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering the recipe ledger for constraint-aware systems
What it does
Wacv Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening the reproducibility of a WACV paper, covering the recipe ledger for constraint-aware systems, benchmark and split hygiene, seed and session honesty, device and power reporting for applications claims, and keeping the reproduction package in sync with the paper across the two-round Revise-and-Resubmit lap.
Its SKILL.md is about 940 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 WACV paper
- Covering the recipe ledger for constraint-aware systems
- Benchmark and split hygiene
- Seed and session honesty
Example prompts
- “/wacv-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.
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
Wacv Reproducibility loads about 936 tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 379 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). 379 words, ~936 tokens.
Download SKILL.mdSave it as
.claude/skills/wacv-reproducibility/SKILL.md (or your agent's skills folder).- name
- wacv-reproducibility
- description
- Use when strengthening the reproducibility of a WACV paper, covering the recipe ledger for constraint-aware systems, benchmark and split hygiene, seed and session honesty, device and power reporting for applications claims, and keeping the reproduction package in sync with the paper across the two-round Revise-and-Resubmit lap.
WACV Reproducibility
Use this to make a WACV result checkable — by a reviewer now and by you at the Round 2 resubmission. WACV's applications framing raises the bar in one direction (a systems claim must be reproducible as deployed), and the two-round model adds a second (paper and artifact must not drift between rounds). Facts are the WACV 2026/2027 cycles as read on 2026-07-09.
The recipe ledger
Keep one ledger that regenerates every reported number, so the body, the supplement, and the artifact cannot diverge:
| Ledger entry | Why WACV cares |
|---|---|
| Exact data splits and preprocessing | Applications datasets are often custom; a hidden split invalidates a comparison |
| Seeds (and sessions/devices for field work) | Reviewers distrust single hero runs |
| Hyperparameters per reported row | Lets a reviewer see the comparison was matched |
| Device, power meter, and measurement method | An applications latency/wattage claim is only reproducible if the rig is named |
| Baseline re-tuning under your constraint | Proves the comparison was fair, not defaults-vs-yours |
| Script → figure/table mapping | So a Round 2 reviewer confirms nothing changed silently |
Constraint-aware reproducibility
An Applications-track claim ("2 W, sub-10-lux, on device D") is not reproducible from accuracy alone. Record how the constraint was measured — the meter, the device firmware, the ambient condition — so a reviewer or a future reader can reproduce the constraint, not just the metric. A number without its measurement rig is a claim, not evidence.
Repro smoke check before submission (and again before the R2 resubmission):
1. Fresh checkout → run the pipeline for one reported row end to end.
2. Confirm the produced number matches the paper within the stated spread.
3. Diff the artifact's claims against the current paper's claims — zero drift allowed.
4. Strip identity from the anonymous package (see wacv-artifact-evaluation).Show full SKILL.md (151 more words)Show less
Seed and session honesty
Report variance over seeds, and for deployed/field systems over repeated sessions or devices. Do not report the best of many runs as "the" result. If a gap sits within the spread, say so — an honest small margin survives review better than an inflated one that a reviewer's own reproduction contradicts.
Sync across the two rounds
The Revise-and-Resubmit lap is where reproducibility quietly breaks: authors change an experiment in the paper but not in the artifact, or vice versa. After every revision, re-run the smoke check and re-diff the artifact against the paper. A Round 2 reviewer re-reading a revised submission should find the package and the paper telling one story.
Reverify each cycle
- Whether the current guidelines request a reproducibility statement or checklist.
- Data-release and licensing rules for any dataset used as evidence.
- Supplementary size/format caps that constrain what you can ship (待核实 for 2026).
Output format
[Recipe ledger] regenerates every reported number: yes/no
[Constraint rig] device/meter/condition recorded for systems claims: yes/no
[Seeds/sessions] variance reported honestly: yes/no
[Baselines] re-tuned under your constraint and logged: yes/no
[Round sync] artifact matches current paper (zero drift): yes/no
[Gap] <the number a reviewer could not currently reproduce>© 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 WACV-Skills/skills/wacv-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Wacv 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 |
|---|---|---|---|---|---|---|
| Wacv Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~936 | 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
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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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Categories
Questions about Wacv Reproducibility
What does Wacv Reproducibility do?
A skill your agent uses when strengthening the reproducibility of a WACV paper, covering the recipe ledger for constraint-aware systems, benchmark and split hygiene, seed and session honesty, device…. Wacv Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening the reproducibility of a WACV paper, covering the recipe ledger for constraint-aware systems, benchmark and split hygiene, seed and session honesty, device and power reporting for applications claims, and keeping the reproduction package in sync with the paper across the two-round Revise-and-Resubmit lap.
When should I use Wacv Reproducibility?
Wacv Reproducibility fits situations like: strengthening the reproducibility of a WACV paper; covering the recipe ledger for constraint-aware systems; benchmark and split hygiene; seed and session honesty.
How do I install Wacv Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wacv-reproducibility -a claude-code`. Or copy the skill folder (WACV-Skills/skills/wacv-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/wacv-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Wacv Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill wacv-reproducibility -a codex`. Or copy the skill folder (WACV-Skills/skills/wacv-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/wacv-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Wacv 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 wacv-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/wacv-reproducibility, .gemini/skills/wacv-reproducibility, .github/skills/wacv-reproducibility and .opencode/skills/wacv-reproducibility in your project.
What does Wacv Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Wacv Reproducibility is instructions for the agent only.
Does Wacv 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 Wacv 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 Wacv Reproducibility use?
Wacv 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 Wacv Reproducibility use?
About 936 tokens (SKILL.md is roughly 3.7k 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 Wacv Reproducibility?
Skills that share tags, products or a category with Wacv 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 Wacv 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.