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 ESEC/FSE reproducibility and open-science evidence, covering the Data Availability statement, anonymized-but-runnable artifacts, provenance pinning for…
Install Fse Reproducibility
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fse-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/FSE-Skills/skills/fse-reproducibility .claude/skills/fse-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 "fse-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-reproducibility into .claude/skills/fse-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-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/FSE-Skills/skills/fse-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 fse-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fse-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/FSE-Skills/skills/fse-reproducibility .agents/skills/fse-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 "fse-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-reproducibility into .agents/skills/fse-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-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 fse-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fse-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/FSE-Skills/skills/fse-reproducibility .cursor/skills/fse-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 "fse-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-reproducibility into .cursor/skills/fse-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-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 FSE-Skills/skills/fse-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 fse-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fse-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/FSE-Skills/skills/fse-reproducibility .gemini/skills/fse-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 "fse-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-reproducibility into .gemini/skills/fse-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-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 fse-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 fse-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/FSE-Skills/skills/fse-reproducibility .github/skills/fse-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 "fse-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-reproducibility into .github/skills/fse-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-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 fse-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 fse-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/FSE-Skills/skills/fse-reproducibility .opencode/skills/fse-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 "fse-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-reproducibility into .opencode/skills/fse-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-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
fse-reproducibility- GitHub stars
- 1.2k
- Token cost
- ~1.2k tokens
- SKILL.md length
- 496 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when strengthening ESEC/FSE reproducibility and open-science evidence, covering the Data Availability statement, anonymized-but-runnable artifacts, provenance pinning for…
- Strengthening ESEC/FSE reproducibility and open-science evidence
- SKILL.md covers Evidence map, Data Availability statement…, Provenance pinning and Degrees of reproducibility…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering the Data Availability statement
What it does
Fse Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening ESEC/FSE reproducibility and open-science evidence, covering the Data Availability statement, anonymized-but-runnable artifacts, provenance pinning for mining and LLM studies, claim-to-evidence mapping, honest degrees of reproducibility, and consistency between what the paper says and what the artifact contains.
Its SKILL.md is about 1.2k 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 ESEC/FSE reproducibility and open-science evidence
- Covering the Data Availability statement
- Anonymized-but-runnable artifacts
- Provenance pinning for mining and LLM studies
Example prompts
- “/fse-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
Fse Reproducibility loads about 1.2k tokens when it runs. Until then it costs about 89 tokens; SKILL.md has 496 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). 496 words, ~1,209 tokens.
Download SKILL.mdSave it as
.claude/skills/fse-reproducibility/SKILL.md (or your agent's skills folder).- name
- fse-reproducibility
- description
- Use when strengthening ESEC/FSE reproducibility and open-science evidence, covering the Data Availability statement, anonymized-but-runnable artifacts, provenance pinning for mining and LLM studies, claim-to-evidence mapping, honest degrees of reproducibility, and consistency between what the paper says and what the artifact contains.
FSE Reproducibility
Use this before submission and again before camera-ready. FSE's open-science posture makes reproducibility a scored dimension, not a courtesy: the double-anonymous review already expects an inspectable artifact, and the PACMSE camera-ready expects a permanent one. The goal is that a competent reader could rebuild your evidence and reach your conclusions.
Evidence map
- Map each research-question answer, technique claim, and reported number to a verifiable location — a paper section, a table generated from logged data, or a script in the artifact.
- For techniques, give enough of the algorithm, parameters, and environment that a reader could re-implement or re-run it.
- For empirical studies, report subjects and their selection, data collection, preprocessing, metrics, statistics, and the analysis scripts.
- Keep the Data Availability statement truthful and specific: what is shared, where it will live after acceptance, and — if something cannot be shared — exactly why.
- Keep the paper and the artifact consistent: a number in the PDF that no script in the artifact produces is the contradiction reviewers read as carelessness.
Data Availability statement audit
| Claim in the paper | Weak availability answer | FSE-ready answer |
|---|---|---|
| "We study N projects" | "Dataset available on request" | Anonymized archive of the exact project list + extraction scripts |
| "Our tool detects X" | "Code will be released" | Anonymized, runnable tool with a README and a small demo input |
| "We interviewed P developers" | Nothing (privacy cited vaguely) | Anonymized codebook, protocol, and aggregate data; stated ethics limits |
| "The model produced Y" | Live API described | Cached prompts and raw responses, model IDs and dates |
"Available on request" is treated as not available at FSE; convert every such line into a concrete, anonymized artifact or an explicit, justified exception.
Provenance pinning
[Mining] pin repository SHAs; record corpus extraction date; archive the extracted dataset,
not just the query; document fork/duplicate/bot handling
[LLM] record exact model identifiers + access dates; cache raw inputs and outputs; report
sampling settings; prefer post-training-cutoff subjects to bound contamination
[Compute] state hardware, runtime, and number of runs so a reader can size a reproduction
[Randomness] log seeds for any stochastic step; say what is and is not deterministicShow full SKILL.md (220 more words)Show less
Degrees of reproducibility (state the one you achieved)
- Turnkey: one documented command regenerates each table/figure from logged data.
- Scripted: scripts exist but require documented manual steps or external data access.
- Descriptive: prose detailed enough that a competent reader could rebuild the pipeline.
For FSE, aim turnkey for anything a reviewer might rerun quickly (a detection script on sample inputs, a plot from logged results); large mined corpora or industrial data may stay scripted with access clearly documented. Stating the achieved level honestly beats promising turnkey behavior that fails on a clean machine.
Vignette: a mixed-methods study
Consider a study combining mined pull-request data with a developer survey. Its reproducibility spine: the mining scripts with pinned SHAs and extraction date; the anonymized extracted dataset; the survey instrument and anonymized responses; the qualitative codebook with inter-rater agreement; and the analysis notebooks that turn all of it into the paper's tables — plus one honest sentence about the parts (raw identities, private repositories) that cannot be shared and why.
Consistency and camera-ready pass
- Before submission: every scored number traces to the artifact; the Data Availability statement matches reality; the artifact is anonymized (no owner strings, cluster paths, or lab names).
- Before camera-ready: swap anonymized links for permanent, DOI-issuing archives, and align the
statement with the ACM artifact badges you are pursuing (
fse-artifact-evaluation).
Output format
[Claim inventory] <claim -> evidence location>
[Data Availability] concrete / vague / missing
[Provenance gaps] <mining SHAs / LLM caching / seeds / compute>
[Reproducibility level] turnkey / scripted / descriptive, stated honestly
[Paper fixes] <must appear in the PDF>
[Artifact fixes] <additions before upload>© 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 FSE-Skills/skills/fse-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Fse 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 |
|---|---|---|---|---|---|---|
| Fse Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.2k | 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 |
Similar skills
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 Fse Reproducibility
What does Fse Reproducibility do?
A skill your agent uses when strengthening ESEC/FSE reproducibility and open-science evidence, covering the Data Availability statement, anonymized-but-runnable artifacts, provenance pinning for…. Fse Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when strengthening ESEC/FSE reproducibility and open-science evidence, covering the Data Availability statement, anonymized-but-runnable artifacts, provenance pinning for mining and LLM studies, claim-to-evidence mapping, honest degrees of reproducibility, and consistency between what the paper says and what the artifact contains.
When should I use Fse Reproducibility?
Fse Reproducibility fits situations like: strengthening ESEC/FSE reproducibility and open-science evidence; covering the Data Availability statement; anonymized-but-runnable artifacts; provenance pinning for mining and LLM studies.
How do I install Fse Reproducibility in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-reproducibility -a claude-code`. Or copy the skill folder (FSE-Skills/skills/fse-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/fse-reproducibility in your project. Claude Code loads it when a task matches its description.
How do I install Fse Reproducibility in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-reproducibility -a codex`. Or copy the skill folder (FSE-Skills/skills/fse-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/fse-reproducibility in your project. Codex loads it when a task matches its description.
Can I use Fse 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 fse-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/fse-reproducibility, .gemini/skills/fse-reproducibility, .github/skills/fse-reproducibility and .opencode/skills/fse-reproducibility in your project.
What does Fse Reproducibility need to run?
SKILL.md names no scripts, command-line tools or credentials: Fse Reproducibility is instructions for the agent only.
Does Fse 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 Fse 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 Fse Reproducibility use?
Fse 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 Fse Reproducibility use?
About 1.2k tokens (SKILL.md is roughly 4.8k 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 Fse Reproducibility?
Skills that share tags, products or a category with Fse 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 Fse 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.