A skill your agent uses when building the verifiability story of an ICSE research-track paper, covering the open-science policy's sharing-by-default expectation, the Data Availability section…
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icse-reproducibility -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icse-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/ICSE-Skills/skills/icse-reproducibility .claude/skills/icse-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 "icse-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICSE-Skills/skills/icse-reproducibility into .claude/skills/icse-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icse-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/ICSE-Skills/skills/icse-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 icse-reproducibility -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icse-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/ICSE-Skills/skills/icse-reproducibility .agents/skills/icse-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 "icse-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICSE-Skills/skills/icse-reproducibility into .agents/skills/icse-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icse-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 icse-reproducibility -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icse-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/ICSE-Skills/skills/icse-reproducibility .cursor/skills/icse-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 "icse-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICSE-Skills/skills/icse-reproducibility into .cursor/skills/icse-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icse-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 ICSE-Skills/skills/icse-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 icse-reproducibility -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icse-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/ICSE-Skills/skills/icse-reproducibility .gemini/skills/icse-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 "icse-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICSE-Skills/skills/icse-reproducibility into .gemini/skills/icse-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icse-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 icse-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 icse-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/ICSE-Skills/skills/icse-reproducibility .github/skills/icse-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 "icse-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICSE-Skills/skills/icse-reproducibility into .github/skills/icse-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icse-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 icse-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 icse-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/ICSE-Skills/skills/icse-reproducibility .opencode/skills/icse-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 "icse-reproducibility" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICSE-Skills/skills/icse-reproducibility into .opencode/skills/icse-reproducibility/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icse-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.
icse-reproducibilityA skill your agent uses when building the verifiability story of an ICSE research-track paper, covering the open-science policy's sharing-by-default expectation, the Data Availability section…
Icse Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when building the verifiability story of an ICSE research-track paper, covering the open-science policy's sharing-by-default expectation, the Data Availability section, anonymized replication packages at review time, provenance pinning for mining and LLM studies, and honest non-sharing justifications.
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 and Econometrics and empirical 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.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
gitFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Icse Reproducibility loads about 1.6k tokens when it runs. Until then it costs about 82 tokens; SKILL.md has 685 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.
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.
The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 685 words, ~1,591 tokens.
.claude/skills/icse-reproducibility/SKILL.md (or your agent's skills folder).At ICSE, reproducibility is not an optional badge chase bolted on after
acceptance — it is one of the four scored review criteria. The 2027 call
(read 2026-07-08) scores Verifiability and Transparency: whether the paper
gives enough information to understand how the innovation works, how data was
obtained, analyzed, and interpreted, and whether independent verification or
replication is supported. Build for that score at review time; the
post-acceptance badge process (icse-artifact-evaluation) then becomes cheap.
The research track is governed by the ICSE Open Science policy. Its verified 2027 shape:
% Full sharing
\section*{Data Availability}
Our replication package -- tool source, the 17-project benchmark with
version pins, all scripts, and raw result CSVs -- is archived anonymously
at <anonymized-link>. Post-acceptance it moves to a DOI-issuing archive.
% Partial sharing, justified
\section*{Data Availability}
Scripts, codebooks, and aggregated results are at <anonymized-link>.
Interview recordings and transcripts cannot be shared: our IRB protocol
and consent forms promise participants non-disclosure. We include the
interview guide and the full codebook so the analysis can be audited
and the study re-run in another organization.The failing pattern is the vague middle: "data available upon reasonable request" — reviewers read it as no. Name exactly what is in the package, exactly what is withheld, and the specific reason (IRB terms, NDA, licensing), plus what you provide instead so partial verification remains possible.
| Study type | What the package must pin down |
|---|---|
| Tool + benchmark evaluation | Source, build recipe, exact benchmark versions, run scripts, seeds, timeouts, raw outputs, analysis notebooks |
| Repository mining | Corpus construction queries, repo list with SHAs, mining date, filtering code, intermediate datasets |
| LLM-based technique | Prompts verbatim, model identifiers with versions/dates, decoding parameters, cached raw responses, cost logs |
| Controlled experiment / survey | Instruments, task materials, anonymized responses, analysis scripts, power/sampling notes |
| Qualitative study | Interview guide, codebook with definitions, agreement computation, anonymized excerpts as consent allows |
Two SE-specific provenance rules. Mining decays: repositories get force-pushed, deleted, and relicensed, so archive the extracted dataset, not just the query. LLM outputs decay faster: hosted models change silently, so cached raw responses are the only durable record — a package that requires re-querying a hosted API cannot reproduce your numbers, only re-sample them, and should say so explicitly.
Before the evaluation runs at scale, freeze and record: random seeds and where they enter; dependency lockfiles and container digest; hardware and OS; timeout and memory limits; any nondeterminism you could not remove (thread scheduling, hosted-API sampling) with its measured impact across repeated runs. Ten minutes of ledger discipline in May prevents the September review comment "results could not be understood well enough to assess" — a verifiability score you cannot response your way out of.
The package must be double-anonymous like the paper. Use an anonymizing host
or a scrubbed archive; strip git history (git archive, never a .git
clone), notebook author fields, container labels, hard-coded home paths, and
lab-server hostnames in configs. Do not let anonymization destroy usefulness:
replace identifying strings with placeholders, but keep the code runnable —
an artifact that fails to run because anonymization broke imports scores as
absent.
Run this on the eve of submission, answering as a hostile stranger:
Any "no" maps to a specific review sentence you can predict — and preempt.
The policy's mechanics (HotCRP supplementary option, statement placement, badge linkage) are cycle-set; the sharing-by-default principle has held across recent editions but its enforcement wording moves. Whether reviewers are required to examine supplementary material was not verifiable for 2027 — 待核实 — so keep every decision-critical fact in the 10 pages and treat the package as evidence, not overflow.
[Verifiability score forecast] can a stranger re-derive each headline number? per-claim y/n
[Availability statement] full / partial-justified / missing; vague-middle phrases found
[Package audit] study-type row above -> items present / absent
[Provenance] pins recorded (SHAs, model versions, dates); decay risks named
[Anonymity] scrub pass results; runnability after scrubbing confirmed© 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
Just SKILL.md in ICSE-Skills/skills/icse-reproducibility of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Icse 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 |
|---|---|---|---|---|---|---|
| Icse Reproducibility this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Replication Packagepedrohcgs/claude-code-my-workflow | 1.7k | — | ~2.8k | Automated safety check: Notes | MIT | |
| Audit Reproducibilitypedrohcgs/claude-code-my-workflow | 1.7k | — | ~6.4k | Automated safety check: Notes | MIT | |
| Reproducible Pipelinesbrycewang-stanford/Auto-Empirical-Research-Skills | 4.6k | — | ~3.3k | Automated safety check: Pass | Custom licence | |
| Audit Replicationbrycewang-stanford/Auto-Empirical-Research-Skills | 4.6k | — | ~984 | Automated safety check: Notes | Custom licence | |
| Scholar Openjoshzyj/open-scholar-skill | 168 | — | ~14k | Automated safety check: Pass | Custom licence |
pedrohcgs/claude-code-my-workflow
Assemble a submission-ready replication package to the AEA Data and Code Availability Standard (DCAS) / openICPSR / Social Science Reproduction Platform expectations — standard replication README…
pedrohcgs/claude-code-my-workflow
Enforce the replication-protocol.md rule by cross-checking numeric claims in a manuscript against the actual R / Stata / Python outputs.
brycewang-stanford/Auto-Empirical-Research-Skills
This skill covers reproducible research pipelines and replication packages.
brycewang-stanford/Auto-Empirical-Research-Skills
Validate the replication package for the sewage-house-prices project.
joshzyj/open-scholar-skill
Implement open science practices for a social science study.
brycewang-stanford/Auto-Empirical-Research-Skills
Audit manuscript and replication package against FAIR open-science principles.
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running and reporting the analysis for an Annals of the American Association of Geographers manuscript — spatial statistics and modeling, remote-sensing accuracy, or…
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when positioning an Annals of the American Association of Geographers manuscript in the literature — engaging geographic scholarship across the relevant area and the…
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when responding to an Annals of the American Association of Geographers decision letter (major/minor revision) — building a point-by-point response to the subject editor and…
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when defending the research design of an Annals of the American Association of Geographers manuscript — spatial/quantitative analysis and GIScience, remote-sensing and…
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when you need to understand how the Annals of the American Association of Geographers evaluates a manuscript — double-anonymous review routed through a subject editor by…
brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when running the final pre-submission preflight for the Annals of the American Association of Geographers via ScholarOne Manuscripts — area/article-type selection…
Categories
A skill your agent uses when building the verifiability story of an ICSE research-track paper, covering the open-science policy's sharing-by-default expectation, the Data Availability section…. Icse Reproducibility is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when building the verifiability story of an ICSE research-track paper, covering the open-science policy's sharing-by-default expectation, the Data Availability section, anonymized replication packages at review time, provenance pinning for mining and LLM studies, and honest non-sharing justifications.
Icse Reproducibility fits situations like: building the verifiability story of an ICSE research-track paper; covering the open-science policys sharing-by-default expectation; the Data Availability section; anonymized replication packages at review time.
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icse-reproducibility -a claude-code`. Or copy the skill folder (ICSE-Skills/skills/icse-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/icse-reproducibility in your project. Claude Code loads it when a task matches its description.
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icse-reproducibility -a codex`. Or copy the skill folder (ICSE-Skills/skills/icse-reproducibility in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/icse-reproducibility in your project. Codex loads it when a task matches its description.
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 icse-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/icse-reproducibility, .gemini/skills/icse-reproducibility, .github/skills/icse-reproducibility and .opencode/skills/icse-reproducibility in your project.
Going by SKILL.md and its folder, Icse Reproducibility needs the command-line tools its instructions call (git).
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
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.
Icse Reproducibility is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.6k tokens (SKILL.md is roughly 6.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Icse Reproducibility: Replication Package (pedrohcgs/claude-code-my-workflow, 1.7k stars), Audit Reproducibility (pedrohcgs/claude-code-my-workflow, 1.7k stars), Reproducible Pipelines (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k stars) and Audit Replication (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
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.
