FSE Experiments is a Data skill for Claude Code, published by brycewang-stanford in Awesome-Journal-Skills.
Designs rigorous empirical evaluations for software engineering research papers, ensuring proper evidence matching, statistical analysis, and methodological transparency for conferences like ESEC/FSE.
FSE Experiments is cataloged under Data on DirSkills. FSE Experiments comes from a repository tagged academic-research, academic-writing, agent-skills, ai-agents and anthropic.
Documentation
Use this before submission when the empirical story is not yet locked. FSE reviewers are SE empiricists; the evaluation is where a good idea is won or lost. The organizing principle is evidence proportional to the claim — the study must test the thing the paper actually asserts, on subjects and baselines a skeptic would accept.
This is the opening of the README. Read the full README on GitHub.
FSE Experiments is one of 53 skills that DirSkills catalogs from brycewang-stanford/Awesome-Journal-Skills, the repository it ships in. Its siblings there include AAAI Artifact Evaluation, AAAI Author Response and AAAI Camera Ready. Each one is a separate skill with its own page in this directory, installs the same way FSE Experiments does, and is maintained by brycewang-stanford in that same repository. The rest of the collection is listed on the brycewang-stanford/Awesome-Journal-Skills page.
FSE Experiments ranks #518 by stars among the 812 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of FSE Experiments against them. Open each page to compare what they document and how they install.
FSE Experiments is one of 53 skills cataloged on DirSkills from brycewang-stanford/Awesome-Journal-Skills.
FSE Experiments is one of 812 Data skills cataloged on DirSkills, ranked #518 by stars among them.