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A skill your agent uses when designing or auditing ESEC/FSE empirical evaluations, covering real subject systems, fair baselines, SE-standard statistics and effect sizes, qualitative and…
Install Fse Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fse-experiments --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-experiments .claude/skills/fse-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-experiments into .claude/skills/fse-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-experiments", 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-experimentsType 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-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fse-experiments --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-experiments .agents/skills/fse-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-experiments into .agents/skills/fse-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-experiments", 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-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fse-experiments --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-experiments .cursor/skills/fse-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-experiments into .cursor/skills/fse-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-experiments", 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-experiments--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-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills fse-experiments --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-experiments .gemini/skills/fse-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-experiments into .gemini/skills/fse-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-experiments", 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-experimentsInstalls 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-experiments -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-experiments .github/skills/fse-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-experiments into .github/skills/fse-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-experiments", 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-experiments -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-experiments --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-experiments .opencode/skills/fse-experiments && 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-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/FSE-Skills/skills/fse-experiments into .opencode/skills/fse-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fse-experiments", 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-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.3k tokens
- SKILL.md length
- 521 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when designing or auditing ESEC/FSE empirical evaluations, covering real subject systems, fair baselines, SE-standard statistics and effect sizes, qualitative and…
- Auditing ESEC/FSE empirical evaluations
- SKILL.md covers Evaluation audit, Claim-to-evidence design table, Contamination-aware LLM… and Provenance floor for mining…, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering real subject systems
What it does
Fse Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing ESEC/FSE empirical evaluations, covering real subject systems, fair baselines, SE-standard statistics and effect sizes, qualitative and mixed-methods rigor, contamination-aware LLM ablations, provenance for mining studies, and matching evidence to the shape of each software-engineering claim.
Its SKILL.md is about 1.3k 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 Data & Analytics, covering Statistics. 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
- Auditing ESEC/FSE empirical evaluations
- Covering real subject systems
- SE-standard statistics and effect sizes
- Qualitative and mixed-methods rigor
Example prompts
- “/fse-experiments”
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 Experiments loads about 1.3k tokens when it runs. Until then it costs about 85 tokens; SKILL.md has 521 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). 521 words, ~1,283 tokens.
Download SKILL.mdSave it as
.claude/skills/fse-experiments/SKILL.md (or your agent's skills folder).- name
- fse-experiments
- description
- Use when designing or auditing ESEC/FSE empirical evaluations, covering real subject systems, fair baselines, SE-standard statistics and effect sizes, qualitative and mixed-methods rigor, contamination-aware LLM ablations, provenance for mining studies, and matching evidence to the shape of each software-engineering claim.
FSE Experiments
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.
Evaluation audit
- Match evidence to the claim shape. A claim about developer behavior needs behavior data; a claim about detection needs a labeled ground truth; a claim about scalability needs runtime on realistically sized inputs. Accuracy against a proxy label is not evidence for a practice claim.
- Use real subject systems, sampled by a stated criterion, and list them in the artifact. Toy benchmarks invite the "does this hold on real code?" reject.
- Choose fair baselines, including the strongest prior technique and a simple-but-reasonable alternative, tuned with a documented, equal budget. An untuned baseline is a scored weakness.
- Report SE-standard statistics: effect sizes (not just p-values), confidence intervals, appropriate tests, and corrections for multiple comparisons. Say what variance and how many runs.
- Hold qualitative work to method: coding schemes, inter-rater agreement, saturation, and an audit trail — mixed-methods rigor is native to FSE, not a second-class option.
- Pin provenance for mining and LLM studies (see the code block) so the evaluation reproduces rather than re-samples.
- Design threats in, not on: know before you run which confounds and generalization limits the study will have, and instrument to bound them.
Claim-to-evidence design table
| SE claim | Matching evidence | Reject pattern avoided |
|---|---|---|
| "Technique detects more real defects" | Labeled real faults, precision/recall with CIs vs. tuned baseline | "Evaluated on injected/toy faults only" |
| "Developers act on the output" | Behavioral outcome data on real projects | "Plausibility rating stands in for usefulness" |
| "Scales to large systems" | Runtime/memory across realistic sizes | "Only small inputs tested" |
| "Finding generalizes" | Diverse subject sample + explicit external-validity limits | "One ecosystem, claimed universal" |
| "The model adds the value" | Ablation removing the model vs. lexical/heuristic features | "Model's marginal contribution never isolated" |
Show full SKILL.md (190 more words)Show less
Contamination-aware LLM evaluation
When an LLM is in the loop, the reviewer's first questions are about leakage and reproducibility:
[Contamination] are test subjects plausibly in the model's training data? report cutoff vs.
project dates; prefer post-cutoff or held-out subjects
[Determinism] fix temperature/seed where possible; report the sampling settings
[Caching] store raw prompts and raw responses in the artifact; a live-API-only study
cannot be reproduced, only re-sampled
[Versioning] record exact model identifiers and access dates; models change under a name
[Ablation] isolate the model's marginal value against a non-LLM baselineProvenance floor for mining studies
- Pin repository SHAs and record the corpus extraction date; archive the extracted dataset, not just the query.
- State inclusion/exclusion criteria and the resulting sample size, with the filtering script in the artifact.
- Report how duplicates, forks, and bot activity were handled — silent inclusion skews every downstream number.
Vignette: evaluating a repair technique
Suppose the paper claims a program-repair technique fixes more real bugs than the prior tool. The matching plan: draw bugs from a real, dated fault dataset; run both tools with an equal, documented time budget; report plausible-and-correct patch counts with confidence intervals and an effect size; manually assess a sample of patches for over-fitting; and state external validity (languages, bug kinds) as a bounded threat — every number traceable to a logged run in the artifact.
Statistical reporting floor
- Effect sizes and confidence intervals for every quantitative comparison; say what the intervals represent.
- Number of runs and the source of variance for any stochastic component.
- The compute actually consumed, not vague feasibility language.
Output format
[Evaluation readiness] strong / adequate / weak
[Claim -> evidence map] <claim: subjects/metric/statistic>
[Baseline fairness] <baseline -> tuned? equal budget? documented?>
[Contamination/provenance] <LLM leakage + mining provenance handled? yes/no>
[Threats-by-design] <confound/generalization -> instrumentation to bound it>
[Decision-critical next run] <one experiment or study extension>© 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-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Fse Experiments 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 Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Sandbox Benchvercel/next.js | 143k | — | ~4.1k | Automated safety check: Pass | MIT | |
| Statistical Analysisspacering-net/codeg | 3.8k | 4 repos | ~5k | Automated safety check: Pass | MIT | |
| StatsmodelszLanqing/codex-claude-academic-skills | 4.6k | 16 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| Statistical Powerspacering-net/codeg | 3.8k | 2 repos | ~3.6k | Automated safety check: Notes | MIT | |
| AI Daily DigestvigorX777/ai-daily-digest | 1.6k | — | ~1.3k | Automated safety check: Pass | None |
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Statistical Power
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Sample-size and statistical power calculations for planning studies.
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Questions about Fse Experiments
What does Fse Experiments do?
A skill your agent uses when designing or auditing ESEC/FSE empirical evaluations, covering real subject systems, fair baselines, SE-standard statistics and effect sizes, qualitative and…. Fse Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing ESEC/FSE empirical evaluations, covering real subject systems, fair baselines, SE-standard statistics and effect sizes, qualitative and mixed-methods rigor, contamination-aware LLM ablations, provenance for mining studies, and matching evidence to the shape of each software-engineering claim.
When should I use Fse Experiments?
Fse Experiments fits situations like: auditing ESEC/FSE empirical evaluations; covering real subject systems; SE-standard statistics and effect sizes; qualitative and mixed-methods rigor.
How do I install Fse Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-experiments -a claude-code`. Or copy the skill folder (FSE-Skills/skills/fse-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/fse-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Fse Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill fse-experiments -a codex`. Or copy the skill folder (FSE-Skills/skills/fse-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/fse-experiments in your project. Codex loads it when a task matches its description.
Can I use Fse Experiments 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-experiments -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-experiments, .gemini/skills/fse-experiments, .github/skills/fse-experiments and .opencode/skills/fse-experiments in your project.
What does Fse Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Fse Experiments is instructions for the agent only.
Does Fse Experiments 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 Experiments 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 Experiments use?
Fse Experiments 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 Experiments use?
About 1.3k tokens (SKILL.md is roughly 5.1k 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 Experiments?
Skills that share tags, products or a category with Fse Experiments: Sandbox Bench (vercel/next.js, 143k stars), Statistical Analysis (spacering-net/codeg, 3.8k stars), Statsmodels (zLanqing/codex-claude-academic-skills, 4.6k stars) and Statistical Power (spacering-net/codeg, 3.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Fse Experiments?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,219 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.