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 ECCV paper — training recipes and schedules readers can re-run, dataset versioning and split provenance, pinned…
Install Eccv Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eccv-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills eccv-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/ECCV-Skills/skills/eccv-reproducibility .claude/skills/eccv-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 "eccv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECCV-Skills/skills/eccv-reproducibility into .claude/skills/eccv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eccv-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/ECCV-Skills/skills/eccv-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 eccv-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills eccv-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/ECCV-Skills/skills/eccv-reproducibility .agents/skills/eccv-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 "eccv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECCV-Skills/skills/eccv-reproducibility into .agents/skills/eccv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eccv-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 eccv-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills eccv-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/ECCV-Skills/skills/eccv-reproducibility .cursor/skills/eccv-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 "eccv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECCV-Skills/skills/eccv-reproducibility into .cursor/skills/eccv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eccv-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 ECCV-Skills/skills/eccv-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 eccv-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills eccv-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/ECCV-Skills/skills/eccv-reproducibility .gemini/skills/eccv-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 "eccv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECCV-Skills/skills/eccv-reproducibility into .gemini/skills/eccv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eccv-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 eccv-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 eccv-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/ECCV-Skills/skills/eccv-reproducibility .github/skills/eccv-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 "eccv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECCV-Skills/skills/eccv-reproducibility into .github/skills/eccv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eccv-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 eccv-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 eccv-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/ECCV-Skills/skills/eccv-reproducibility .opencode/skills/eccv-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 "eccv-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ECCV-Skills/skills/eccv-reproducibility into .opencode/skills/eccv-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "eccv-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
eccv-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1k tokens
- SKILL.md length
- 430 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 ECCV paper — training recipes and schedules readers can re-run, dataset versioning and split provenance, pinned…
- Hardening the reproducibility story of an ECCV paper — training recipes and schedules readers can re-run
- SKILL.md covers The two-year checkability…, Recipe ledger (goes in paper…, Foundation-model era pinning and Variance honesty on benchmark…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Dataset versioning and split provenance
What it does
Eccv Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening the reproducibility story of an ECCV paper — training recipes and schedules readers can re-run, dataset versioning and split provenance, pinned foundation-model dependencies, compute disclosure, and seed/variance honesty for benchmark deltas, sized for the 14-page LNCS body plus supplement.
Its SKILL.md is about 1k 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 ECCV paper — training recipes and schedules readers can re-run
- Dataset versioning and split provenance
- Pinned foundation-model dependencies
- Compute disclosure
Example prompts
- “/eccv-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
Eccv Reproducibility loads about 1k tokens when it runs. Until then it costs about 83 tokens; SKILL.md has 430 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). 430 words, ~1,034 tokens.
Download SKILL.mdSave it as
.claude/skills/eccv-reproducibility/SKILL.md (or your agent's skills folder).- name
- eccv-reproducibility
- description
- Use when hardening the reproducibility story of an ECCV paper — training recipes and schedules readers can re-run, dataset versioning and split provenance, pinned foundation-model dependencies, compute disclosure, and seed/variance honesty for benchmark deltas, sized for the 14-page LNCS body plus supplement.
ECCV Reproducibility
Use this before the ECCV paper freeze. ECCV publishes through Springer LNCS with no standing mandatory reproducibility checklist across cycles (whether the current cycle adds one: 待核实 against the live author guidelines), so the reproducibility bar is enforced socially: by reviewers who try to match your numbers, and by the two-year gap before you could publish a correction at the same venue.
The two-year checkability horizon
A CVPR paper's errors are challenged within a year; an ECCV paper sits as the venue's latest word on the topic until the next even year. Write the paper so a lab starting from only the PDF plus supplement in 2027 can rebuild the result — that is the horizon reviewers implicitly price in.
Recipe ledger (goes in paper or supplement, never nowhere)
| Ingredient | Minimum disclosure | Common ECCV-draft omission |
|---|---|---|
| Training schedule | Optimizer, LR schedule, epochs/iterations, batch size, augmentations | Augmentation list "standard" with no definition |
| Initialization | Pretrained checkpoint identity + source | "ImageNet-pretrained" without which checkpoint |
| Data | Dataset version, split definition, filtering rules | Custom val split described only as "held out" |
| Evaluation | Metric implementation source, input resolution, TTA on/off | Resolution mismatch between method and baselines |
| Compute | GPU type, count, wall-clock, total runs behind the paper | Only the final run's cost reported |
Foundation-model era pinning
Modern ECCV pipelines sit on moving substrates. Pin all of them by exact identity, because "CLIP features" is not reproducible information:
# pinned-substrate block for the supplement
backbone: dinov2-vitl14, weights sha256:<hash>, source: <url>
vlm: <model name + exact release tag>, accessed 2026-02
sam_variant: <checkpoint id>
inference: fp16, single-crop, resolution 518x518
api_models: none # if any API model is used, record date + version stringAn API-served model that silently updates invalidates comparisons; record access dates and version strings, and prefer frozen open-weight substrates for headline tables.
Show full SKILL.md (179 more words)Show less
Variance honesty on benchmark deltas
- A +0.3 mAP or +0.2 mIoU headline delta needs seed evidence: report mean ± std over ≥3 seeds for your method and your strongest baseline, or scope the claim down.
- State which numbers are your re-runs versus quoted from prior papers — mixed provenance inside one table is a classic silent irreproducibility.
- If full re-training is too expensive to repeat, say so and report seeds on the cheapest deciding component (e.g., the head, not the backbone).
Split the story across the 14 pages and the supplement
- Body: enough recipe to judge plausibility — schedule summary, data versions, compute order-of-magnitude.
- Supplement: the full ledger, per-experiment configs, the pinned-substrate block, and negative-result notes ("we tried X at lr=1e-3, diverged").
- Code archive: configs as files, not prose; the paper should never be the only serialization of a hyperparameter.
Honest-failure statement
One paragraph reviewers reward at this venue: name the regime where the method breaks (small objects, low light, out-of-distribution categories), with a pointer to a supplement figure showing it. It signals the numbers were probed rather than curated.
Output format
[Repro grade] rebuildable-from-paper / rebuildable-with-code / not-rebuildable
[Ledger gaps] <schedule / init / data / eval / compute rows missing>
[Substrate pinning] <unpinned dependency -> exact identity to record>
[Variance status] <headline delta -> seed evidence present?>
[Placement plan] <body vs supplement vs code archive>© 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 ECCV-Skills/skills/eccv-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Eccv 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 |
|---|---|---|---|---|---|---|
| Eccv Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1k | 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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Compute Environment Setup
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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/.
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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 Eccv Reproducibility
What does Eccv Reproducibility do?
A skill your agent uses when hardening the reproducibility story of an ECCV paper — training recipes and schedules readers can re-run, dataset versioning and split provenance, pinned…. Eccv Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when hardening the reproducibility story of an ECCV paper — training recipes and schedules readers can re-run, dataset versioning and split provenance, pinned foundation-model dependencies, compute disclosure, and seed/variance honesty for benchmark deltas, sized for the 14-page LNCS body plus supplement.
When should I use Eccv Reproducibility?
Eccv Reproducibility fits situations like: hardening the reproducibility story of an ECCV paper — training recipes and schedules readers can re-run; dataset versioning and split provenance; pinned foundation-model dependencies; compute disclosure.
How do I install Eccv Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eccv-reproducibility -a claude-code`. Or copy the skill folder (ECCV-Skills/skills/eccv-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/eccv-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Eccv Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill eccv-reproducibility -a codex`. Or copy the skill folder (ECCV-Skills/skills/eccv-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/eccv-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Eccv 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 eccv-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/eccv-reproducibility, .gemini/skills/eccv-reproducibility, .github/skills/eccv-reproducibility and .opencode/skills/eccv-reproducibility in your project.
What does Eccv Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Eccv Reproducibility is instructions for the agent only.
Does Eccv 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 Eccv 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 Eccv Reproducibility use?
Eccv 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 Eccv Reproducibility use?
About 1k tokens (SKILL.md is roughly 4.1k 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 Eccv Reproducibility?
Skills that share tags, products or a category with Eccv 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 Eccv 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.