fse-reproducibility
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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-reproducibility --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Evidence map
- Data Availability statement audit
- Provenance pinning
- Degrees of reproducibility (state the one you achieved)
- Vignette: a mixed-methods study
- Consistency and camera-ready pass
- Output format
What does the fse-reproducibility skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-reproducibility --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.