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 engineering reproducibility into a VLDB paper before submission, covering hardware and configuration disclosure, dataset and workload provenance, run-to-run variance in…
Install Vldb Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vldb-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills vldb-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/VLDB-Skills/skills/vldb-reproducibility .claude/skills/vldb-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 "vldb-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VLDB-Skills/skills/vldb-reproducibility into .claude/skills/vldb-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vldb-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/VLDB-Skills/skills/vldb-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 vldb-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills vldb-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/VLDB-Skills/skills/vldb-reproducibility .agents/skills/vldb-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 "vldb-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VLDB-Skills/skills/vldb-reproducibility into .agents/skills/vldb-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vldb-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 vldb-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills vldb-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/VLDB-Skills/skills/vldb-reproducibility .cursor/skills/vldb-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 "vldb-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VLDB-Skills/skills/vldb-reproducibility into .cursor/skills/vldb-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vldb-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 VLDB-Skills/skills/vldb-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 vldb-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills vldb-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/VLDB-Skills/skills/vldb-reproducibility .gemini/skills/vldb-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 "vldb-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VLDB-Skills/skills/vldb-reproducibility into .gemini/skills/vldb-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vldb-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 vldb-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 vldb-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/VLDB-Skills/skills/vldb-reproducibility .github/skills/vldb-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 "vldb-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VLDB-Skills/skills/vldb-reproducibility into .github/skills/vldb-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vldb-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 vldb-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 vldb-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/VLDB-Skills/skills/vldb-reproducibility .opencode/skills/vldb-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 "vldb-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/VLDB-Skills/skills/vldb-reproducibility into .opencode/skills/vldb-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "vldb-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
vldb-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~951 tokens
- SKILL.md length
- 393 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when engineering reproducibility into a VLDB paper before submission, covering hardware and configuration disclosure, dataset and workload provenance, run-to-run variance in…
- Engineering reproducibility into a VLDB paper before submission
- SKILL.md covers The disclosure floor, Variance is a systems problem,…, Competitor fairness ledger and Traceability from figure to…, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering hardware and configuration disclosure
What it does
Vldb Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when engineering reproducibility into a VLDB paper before submission, covering hardware and configuration disclosure, dataset and workload provenance, run-to-run variance in systems measurements, competitor-version pinning, figure-to-raw-data traceability, and the disclosure floor PVLDB reviewers apply to performance claims.
Its SKILL.md is about 950 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
- Engineering reproducibility into a VLDB paper before submission
- Covering hardware and configuration disclosure
- Dataset and workload provenance
- Run-to-run variance in systems measurements
Example prompts
- “/vldb-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
Vldb Reproducibility loads about 951 tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 393 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). 393 words, ~951 tokens.
Download SKILL.mdSave it as
.claude/skills/vldb-reproducibility/SKILL.md (or your agent's skills folder).- name
- vldb-reproducibility
- description
- Use when engineering reproducibility into a VLDB paper before submission, covering hardware and configuration disclosure, dataset and workload provenance, run-to-run variance in systems measurements, competitor-version pinning, figure-to-raw-data traceability, and the disclosure floor PVLDB reviewers apply to performance claims.
VLDB Reproducibility
Use this while experiments are still running — reproducibility at a systems venue is an experimental-design property, not a packaging step. The question a PVLDB reviewer silently asks of every performance figure: could a competent lab, given this paper alone, land within noise of these curves?
The disclosure floor
Every performance claim needs its context recoverable from the paper (or its cited artifact):
- Hardware: CPU model and count, memory, storage class and interface, network fabric, node count. "A commodity server" reproduces nothing.
- Software: OS, kernel where it matters (I/O experiments), compiler and flags, and the exact versions and configurations of every system measured — including yours.
- Data: source, size, skew characteristics; for generated data, the generator, its parameters, and its seed.
- Workload: query mix, arrival pattern, client counts, warm-up protocol, and run duration.
- Measurement: what was timed, from where, and what was excluded.
Variance is a systems problem, not a seed problem
ML papers randomize over seeds; systems papers fight nondeterminism from caches, compaction timing, JIT warm-up, thermal throttling, and noisy neighbors. The floor:
| Practice | Rule of thumb |
|---|---|
| Repetitions | ≥3-5 runs per point; state the count |
| Reported statistic | Median or mean — say which; show spread when curves are close |
| Cache state | Declare warm or cold, and how you got there |
| Cloud runs | Same instance placement across systems; note the epoch |
| Background work | Disable or document (compaction, checkpoints, GC) |
A speedup smaller than the run-to-run spread is not a result; either tighten the measurement or drop the claim.
Show full SKILL.md (146 more words)Show less
Competitor fairness ledger
Reviewers here often built the systems you compare against. For each baseline record: version or commit, configuration changes from defaults, tuning effort spent, and any feature disabled — then disclose that ledger in the paper. An untuned competitor found by its author on the program committee is a one-review rejection.
Traceability from figure to raw data
paper figure N
<- plots/make_fig_N.py
<- results/expN/*.csv (raw, one file per run)
<- run.sh expN --config configs/expN.yaml
<- git tag paper-vN + Dockerfile digestBuild this chain during the project, not after acceptance. It is what makes the revision window survivable — a reviewer-requested variation becomes a config edit instead of archaeology — and it is exactly what the pVLDB Reproducibility Committee will walk if you enter the evaluation.
The repro-honesty paragraph
State in the paper what is not reproducible and why: proprietary traces, production-only scale, licensed competitors. PVLDB's culture (availability badges, mandatory EA&B evaluation) rewards declared limits and punishes discovered ones. One honest paragraph outperforms a broken promise of full reproducibility.
Output format
[Disclosure floor] met / gaps (hardware/software/data/workload/measurement)
[Variance handling] reps, statistic, spread shown — weak points
[Competitor ledger] complete / untuned or unpinned baselines listed
[Trace chain] figure->script->raw->tag intact / broken links
[Declared limits] <what is stated as non-reproducible and why>
[Highest-risk claim] <claim whose evidence would not survive a rerun>© 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 VLDB-Skills/skills/vldb-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Vldb 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 |
|---|---|---|---|---|---|---|
| Vldb Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~951 | 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
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/.
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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 Vldb Reproducibility
What does Vldb Reproducibility do?
A skill your agent uses when engineering reproducibility into a VLDB paper before submission, covering hardware and configuration disclosure, dataset and workload provenance, run-to-run variance in…. Vldb Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when engineering reproducibility into a VLDB paper before submission, covering hardware and configuration disclosure, dataset and workload provenance, run-to-run variance in systems measurements, competitor-version pinning, figure-to-raw-data traceability, and the disclosure floor PVLDB reviewers apply to performance claims.
When should I use Vldb Reproducibility?
Vldb Reproducibility fits situations like: engineering reproducibility into a VLDB paper before submission; covering hardware and configuration disclosure; dataset and workload provenance; run-to-run variance in systems measurements.
How do I install Vldb Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vldb-reproducibility -a claude-code`. Or copy the skill folder (VLDB-Skills/skills/vldb-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/vldb-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Vldb Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill vldb-reproducibility -a codex`. Or copy the skill folder (VLDB-Skills/skills/vldb-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/vldb-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Vldb 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 vldb-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/vldb-reproducibility, .gemini/skills/vldb-reproducibility, .github/skills/vldb-reproducibility and .opencode/skills/vldb-reproducibility in your project.
What does Vldb Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Vldb Reproducibility is instructions for the agent only.
Does Vldb 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 Vldb 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 Vldb Reproducibility use?
Vldb 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 Vldb Reproducibility use?
About 951 tokens (SKILL.md is roughly 3.8k 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 Vldb Reproducibility?
Skills that share tags, products or a category with Vldb 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 Vldb 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.