Statistical Analysis
spacering-net/codeg
Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting.
A skill your agent uses when designing or auditing the evaluation of an ICSE research-track paper, covering subject and benchmark selection, baseline fairness, statistical tests and effect sizes as…
Install Icse Experiments
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icse-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icse-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/ICSE-Skills/skills/icse-experiments .claude/skills/icse-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 "icse-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICSE-Skills/skills/icse-experiments into .claude/skills/icse-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icse-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/ICSE-Skills/skills/icse-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 icse-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icse-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/ICSE-Skills/skills/icse-experiments .agents/skills/icse-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 "icse-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICSE-Skills/skills/icse-experiments into .agents/skills/icse-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icse-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 icse-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icse-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/ICSE-Skills/skills/icse-experiments .cursor/skills/icse-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 "icse-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICSE-Skills/skills/icse-experiments into .cursor/skills/icse-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icse-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 ICSE-Skills/skills/icse-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 icse-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills icse-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/ICSE-Skills/skills/icse-experiments .gemini/skills/icse-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 "icse-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICSE-Skills/skills/icse-experiments into .gemini/skills/icse-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icse-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 icse-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 icse-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/ICSE-Skills/skills/icse-experiments .github/skills/icse-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 "icse-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICSE-Skills/skills/icse-experiments into .github/skills/icse-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icse-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 icse-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 icse-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/ICSE-Skills/skills/icse-experiments .opencode/skills/icse-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 "icse-experiments" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/ICSE-Skills/skills/icse-experiments into .opencode/skills/icse-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "icse-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
icse-experiments- GitHub stars
- 1.2k
- Token cost
- ~1.6k tokens
- SKILL.md length
- 713 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 the evaluation of an ICSE research-track paper, covering subject and benchmark selection, baseline fairness, statistical tests and effect sizes as…
- Auditing the evaluation of an ICSE research-track paper
- SKILL.md covers Evidence proportional to claim, Subjects and benchmarks, Baselines and fairness and Statistics as the SE community…, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Covering subject and benchmark selection
What it does
Icse Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the evaluation of an ICSE research-track paper, covering subject and benchmark selection, baseline fairness, statistical tests and effect sizes as SE reviewers expect them, qualitative-methods rigor, ablations for AI-based techniques, and threats-driven study design.
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 Data & Analytics, covering Statistics and Experimental design. 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 the evaluation of an ICSE research-track paper
- Covering subject and benchmark selection
- Baseline fairness
- Statistical tests and effect sizes as SE reviewers expect them
Example prompts
- “/icse-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
Icse Experiments loads about 1.6k tokens when it runs. Until then it costs about 79 tokens; SKILL.md has 713 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). 713 words, ~1,600 tokens.
Download SKILL.mdSave it as
.claude/skills/icse-experiments/SKILL.md (or your agent's skills folder).- name
- icse-experiments
- description
- Use when designing or auditing the evaluation of an ICSE research-track paper, covering subject and benchmark selection, baseline fairness, statistical tests and effect sizes as SE reviewers expect them, qualitative-methods rigor, ablations for AI-based techniques, and threats-driven study design.
ICSE Experiments
Design the study to survive an empiricist's audit, because that is who reviews it. ICSE's rigor criterion scores "thoroughness and completeness of an evaluation" (2027 call wording, read 2026-07-08), and the community has strong default expectations per study type that function as unwritten checklists.
Evidence proportional to claim
| Claim shape | Minimum evidence ICSE reviewers expect |
|---|---|
| "Technique X finds more bugs than Y" | Real subject programs, Y actually run (not quoted from its paper), same budget/timeout, statistics + effect size |
| "Developers struggle with Z" | Systematic observation: survey with stated sampling, interviews to saturation, or instrumented behavior — not anecdote |
| "LLM-based tool solves task T" | Ablations over prompts/models, contamination discussion, cost reporting, non-LLM baseline where one exists |
| "Metric M predicts defects" | Multiple projects, time-aware splits, comparison against trivial baselines (size, churn) |
| "Our benchmark/dataset enables research" | Construction protocol, quality validation sample, license clarity, comparison with existing sets |
The recurring failure is claim-evidence mismatch: a general claim ("improves program repair") evaluated on one narrow slice (single-hunk Java bugs from one dataset). Either widen the evidence or narrow the claim before reviewers do.
Subjects and benchmarks
- Real programs, stated selection. Say how subjects were chosen — "all Defects4J v2 projects" beats "10 popular GitHub projects" because the latter invites cherry-picking suspicion. Report scale (kLOC, stars, age) so readers can judge representativeness.
- Version-pin everything. Repository SHAs, dataset versions, dependency locks; mining studies live or die on whether the corpus can be rebuilt.
- Guard against benchmark leakage in AI-for-SE work: if the model's training data plausibly contains your benchmark (most public code pre-2024 does), you must address contamination — held-out post-cutoff bugs, mutation of subjects, or explicit caveats. Reviewers now ask unprompted.
Baselines and fairness
Rerun baselines in your environment with tuned-in-good-faith configurations and identical budgets. Where rerunning is impossible (unavailable code, proprietary systems), say so and downgrade the comparison's claims. A suspiciously weak baseline is the fastest way to lose a rigor score: reviewers know these tools' published numbers.
Statistics as the SE community practices them
SE data are typically non-normal, so community convention favors non-parametric machinery: Mann-Whitney/Wilcoxon tests paired with an effect size — Vargha-Delaney Â12 or Cliff's delta — rather than bare p-values, with multiple-comparison correction when many hypotheses are tested. For stochastic techniques (search-based SE, LLM sampling), repeat runs (community folklore says ≥10, more is better), and report distributions, not single bests. These are norms, not posted rules — but a paper missing them collects the same review sentence every time.
Per comparison, report all four:
central tendency -> median across repetitions
dispersion -> IQR or min-max across seeds/runs
significance -> Mann-Whitney U (corrected if many tests)
effect size -> Â12 or Cliff's delta, with magnitude label
Plus: exact repetition count, budget/timeouts, hardware, and total compute.Show full SKILL.md (310 more words)Show less
Qualitative rigor
For interviews, surveys, and coding studies the checklist changes shape: sampling strategy and saturation argument; codebook development story; a second coder with inter-rater agreement (Cohen's kappa or Krippendorff's alpha) on at least a sample; quotes traceable to anonymized participant IDs; instruments in the replication package. Mixed-methods papers are welcome at ICSE precisely when each half meets its own bar.
Threats-driven design
Write the threats-to-validity section before running the study, as a design
instrument: every internal-validity threat you can name pre-hoc (selection
bias, implementation bugs in your own tooling, evaluation-metric gaming) is
one you can still mitigate cheaply — a random audit sample, a sanity
experiment, an independent reimplementation of the metric. Threats discovered
during writing week can only be confessed, not fixed. This inversion is the
single highest-leverage habit in the ICSE evidence culture; see
icse-writing-style for how the section is then written.
Sanity experiments that catch your own bugs
Before believing any headline number, run the cheap falsifiers: a null-technique control (does a random or trivial variant score suspiciously close to your tool? then the metric, not the technique, is doing the work); a known-answer audit (hand-verify a random sample of your tool's outputs — SE evaluation pipelines mislabel more often than techniques fail); and a metric-implementation cross-check (compute one cell of the results table with an independent script). Papers retracted or majorly revised over evaluation bugs almost always lacked one of these three runs, each of which costs an afternoon.
Ablations for AI-era SE papers
When the technique wraps a model, reviewers want the wrapper separated from the model: fix the model and vary your components; fix your components and vary the model (at least one open-weights option so others can reproduce); report token/dollar/time costs; state decoding parameters and exact model versions with dates — "GPT-4" is not a reproducible identifier and will be flagged under verifiability.
Output format
[Claim inventory] each headline claim -> evidence row above -> met / gap
[Subjects] selection rule, scale, version pins, contamination status
[Baselines] rerun? tuned? budget-matched? which comparisons are downgraded
[Statistics] tests, effect sizes, repetitions, corrections present?
[Qualitative] sampling, codebook, agreement stats (if applicable)
[Pre-hoc threats] threats named at design time -> mitigation experiments queued© 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 ICSE-Skills/skills/icse-experiments of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Icse 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 |
|---|---|---|---|---|---|---|
| Icse Experiments this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Statistical Analysisspacering-net/codeg | 3.8k | 4 repos | ~5k | Automated safety check: Pass | MIT | |
| Statistical Powerspacering-net/codeg | 3.8k | 2 repos | ~3.6k | Automated safety check: Notes | MIT | |
| Data Scientistdavila7/claude-code-templates | 32k | 9 repos | ~2.6k | Automated safety check: Pass | MIT | |
| Statistical Analystalirezarezvani/claude-skills | 28k | 1 repos | ~2.5k | Automated safety check: Pass | MIT | |
| Experimentation Analyticsrampstackco/claude-skills | 940 | 1 repos | ~8.9k | Automated safety check: Pass | MIT |
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Statistical Power
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Sample-size and statistical power calculations for planning studies.
Data Scientist
davila7/claude-code-templates
Expert data scientist for advanced analytics, machine learning, and statistical modeling.
Statistical Analyst
alirezarezvani/claude-skills
Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes.
Experimentation Analytics
rampstackco/claude-skills
How to read experiment results without fooling yourself. An agent skill from rampstackco/claude-skills.
Quantitative Analysis
poemswe/co-researcher
You must use this when selecting statistical tests, interpreting effect sizes, or conducting power analysis.
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Questions about Icse Experiments
What does Icse Experiments do?
A skill your agent uses when designing or auditing the evaluation of an ICSE research-track paper, covering subject and benchmark selection, baseline fairness, statistical tests and effect sizes as…. Icse Experiments is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when designing or auditing the evaluation of an ICSE research-track paper, covering subject and benchmark selection, baseline fairness, statistical tests and effect sizes as SE reviewers expect them, qualitative-methods rigor, ablations for AI-based techniques, and threats-driven study design.
When should I use Icse Experiments?
Icse Experiments fits situations like: auditing the evaluation of an ICSE research-track paper; covering subject and benchmark selection; baseline fairness; statistical tests and effect sizes as SE reviewers expect them.
How do I install Icse Experiments in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icse-experiments -a claude-code`. Or copy the skill folder (ICSE-Skills/skills/icse-experiments in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/icse-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Icse Experiments in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill icse-experiments -a codex`. Or copy the skill folder (ICSE-Skills/skills/icse-experiments in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/icse-experiments in your project. Codex loads it when a task matches its description.
Can I use Icse 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 icse-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/icse-experiments, .gemini/skills/icse-experiments, .github/skills/icse-experiments and .opencode/skills/icse-experiments in your project.
What does Icse Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Icse Experiments is instructions for the agent only.
Does Icse 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 Icse 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 Icse Experiments use?
Icse 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 Icse Experiments use?
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.
What are the alternatives to Icse Experiments?
Skills that share tags, products or a category with Icse Experiments: Statistical Analysis (spacering-net/codeg, 3.8k stars), Statistical Power (spacering-net/codeg, 3.8k stars), Data Scientist (davila7/claude-code-templates, 32k stars) and Statistical Analyst (alirezarezvani/claude-skills, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Icse 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.