Experimentation Analytics
rampstackco/claude-skills
How to read experiment results without fooling yourself. An agent skill from rampstackco/claude-skills.
Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes.
Install Statistical Analyst
$ npx skills add alirezarezvani/claude-skills --skill statistical-analyst -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install alirezarezvani/claude-skills statistical-analyst --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/alirezarezvani/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering/statistical-analyst/skills/statistical-analyst .claude/skills/statistical-analyst && 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 "statistical-analyst" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/statistical-analyst/skills/statistical-analyst into .claude/skills/statistical-analyst/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "statistical-analyst", 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/alirezarezvani/claude-skills/tree/main/engineering/statistical-analyst/skills/statistical-analystType 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 alirezarezvani/claude-skills --skill statistical-analyst -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install alirezarezvani/claude-skills statistical-analyst --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/engineering/statistical-analyst/skills/statistical-analyst .agents/skills/statistical-analyst && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "statistical-analyst" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/statistical-analyst/skills/statistical-analyst into .agents/skills/statistical-analyst/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "statistical-analyst", 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 alirezarezvani/claude-skills --skill statistical-analyst -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install alirezarezvani/claude-skills statistical-analyst --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/engineering/statistical-analyst/skills/statistical-analyst .cursor/skills/statistical-analyst && 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 "statistical-analyst" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/statistical-analyst/skills/statistical-analyst into .cursor/skills/statistical-analyst/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "statistical-analyst", 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/alirezarezvani/claude-skills.git --path engineering/statistical-analyst/skills/statistical-analyst--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 alirezarezvani/claude-skills --skill statistical-analyst -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install alirezarezvani/claude-skills statistical-analyst --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/engineering/statistical-analyst/skills/statistical-analyst .gemini/skills/statistical-analyst && 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 "statistical-analyst" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/statistical-analyst/skills/statistical-analyst into .gemini/skills/statistical-analyst/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "statistical-analyst", 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 alirezarezvani/claude-skills statistical-analystInstalls 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 alirezarezvani/claude-skills --skill statistical-analyst -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/engineering/statistical-analyst/skills/statistical-analyst .github/skills/statistical-analyst && 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 "statistical-analyst" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/statistical-analyst/skills/statistical-analyst into .github/skills/statistical-analyst/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "statistical-analyst", 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 alirezarezvani/claude-skills --skill statistical-analyst -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install alirezarezvani/claude-skills statistical-analyst --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/engineering/statistical-analyst/skills/statistical-analyst .opencode/skills/statistical-analyst && 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 "statistical-analyst" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/statistical-analyst/skills/statistical-analyst into .opencode/skills/statistical-analyst/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "statistical-analyst", 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
statistical-analyst- GitHub stars
- 28k
- Used in
- 1 other repo
- Token cost
- ~2.5k tokens
- SKILL.md length
- 952 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 342
- Repo updated
- First seen
- Licence
- MIT
At a glance
Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes.
- Works in 5 steps: Clarify — Confirm metric type… → Choose test — Proportions → Z-test;… → Run — Execute hypothesis_tester.py with… → …
- You need to validate whether observed differences are real
- SKILL.md covers Entry Points, Tools, Test Selection Guide and Decision Framework…, plus 7 more sections
- Runs Python scripts from its folder; calls python3
What it does
Statistical Analyst is an agent skill from alirezarezvani/claude-skills. Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes. Use when you need to validate whether observed differences are real, size an experiment correctly before launch, or interpret test results with confidence.
Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/statistical-testing-concepts.md`, `scripts/confidence_interval.py` and `scripts/hypothesis_tester.py`).
It sits in Data & Analytics, covering Statistics, Experimental design and A/B testing. The repository describes itself as: 380 Claude Code skills & agent skills & plugins (30+ Agents, 70+ custom commands, 380+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8… The licence is MIT.
When your agent uses it
- You need to validate whether observed differences are real
- Size an experiment correctly before launch
- Interpret test results with confidence
Example prompts
- “/statistical-analyst”
Requirements
- Python 3
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Clarify — Confirm metric type (conversion rate, mean, count), sample sizes, and observed values
- Choose test — Proportions → Z-test; Continuous means → t-test; Categorical → Chi-square
- Run — Execute hypothesis_tester.py with appropriate method
- Interpret — Report p-value, confidence interval, effect size (Cohen's d / Cohen's h / Cramér's V)
- Decide — Ship / hold / extend using the decision framework below
What it can do on your machine
Read from SKILL.md and the folder at commit 19392f7. 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
Ships 3 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3
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
Statistical Analyst loads about 2.5k tokens when it runs, and up to ~4.1k if it reads all its reference files. Until then it costs about 78 tokens; SKILL.md has 952 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); the scripts in this folder are not scanned.
SKILL.md
The full file from alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 952 words, ~2,495 tokens.
Download SKILL.mdSave it as
.claude/skills/statistical-analyst/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- statistical-analyst
- description
- Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes. Use when you need to validate whether observed differences are real, size an experiment correctly before launch, or interpret test results with confidence.
You are an expert statistician and data scientist. Your goal is to help teams make decisions grounded in statistical evidence — not gut feel. You distinguish signal from noise, size experiments correctly before they start, and interpret results with full context: significance, effect size, power, and practical impact.
You treat "statistically significant" and "practically significant" as separate questions and always answer both.
Entry Points
Mode 1 — Analyze Experiment Results (A/B Test)
Use when an experiment has already run and you have result data.
- Clarify — Confirm metric type (conversion rate, mean, count), sample sizes, and observed values
- Choose test — Proportions → Z-test; Continuous means → t-test; Categorical → Chi-square
- Run — Execute
hypothesis_tester.pywith appropriate method - Interpret — Report p-value, confidence interval, effect size (Cohen's d / Cohen's h / Cramér's V)
- Decide — Ship / hold / extend using the decision framework below
Mode 2 — Size an Experiment (Pre-Launch)
Use before launching a test to ensure it will be conclusive.
- Define — Baseline rate, minimum detectable effect (MDE), significance level (α), power (1−β)
- Calculate — Run
sample_size_calculator.pyto get required N per variant - Sanity-check — Confirm traffic volume can deliver N within acceptable time window
- Document — Lock the stopping rule before launch to prevent p-hacking
Mode 3 — Interpret Existing Numbers
Use when someone shares a result and asks "is this significant?" or "what does this mean?"
- Ask for: sample sizes, observed values, baseline, and what decision depends on the result
- Run the appropriate test
- Report using the Bottom Line → What → Why → How to Act structure
- Flag any validity threats (peeking, multiple comparisons, SUTVA violations)
Tools
scripts/hypothesis_tester.py
Run Z-test (proportions), two-sample t-test (means), or Chi-square test (categorical). Returns p-value, confidence interval, effect size, and a plain-English verdict.
# Z-test for two proportions (A/B conversion rates)
python3 scripts/hypothesis_tester.py --test ztest \
--control-n 5000 --control-x 250 \
--treatment-n 5000 --treatment-x 310
# Two-sample t-test (comparing means, e.g. revenue per user)
python3 scripts/hypothesis_tester.py --test ttest \
--control-mean 42.3 --control-std 18.1 --control-n 800 \
--treatment-mean 46.1 --treatment-std 19.4 --treatment-n 820
# Chi-square test (multi-category outcomes)
python3 scripts/hypothesis_tester.py --test chi2 \
--observed "120,80,50" --expected "100,100,50"
# Output JSON for downstream use
python3 scripts/hypothesis_tester.py --test ztest \
--control-n 5000 --control-x 250 \
--treatment-n 5000 --treatment-x 310 \
--format jsonscripts/sample_size_calculator.py
Calculate required sample size per variant before launching an experiment.
# Proportion test (conversion rate experiment)
python3 scripts/sample_size_calculator.py --test proportion \
--baseline 0.05 --mde 0.20 --alpha 0.05 --power 0.80
# Mean test (continuous metric experiment)
python3 scripts/sample_size_calculator.py --test mean \
--baseline-mean 42.3 --baseline-std 18.1 --mde 0.10 \
--alpha 0.05 --power 0.80
# Show tradeoff table across power levels
python3 scripts/sample_size_calculator.py --test proportion \
--baseline 0.05 --mde 0.20 --table
# Output JSON
python3 scripts/sample_size_calculator.py --test proportion \
--baseline 0.05 --mde 0.20 --format jsonscripts/confidence_interval.py
Compute confidence intervals for a proportion or mean. Use for reporting observed metrics with uncertainty bounds.
# CI for a proportion
python3 scripts/confidence_interval.py --type proportion \
--n 1200 --x 96
# CI for a mean
python3 scripts/confidence_interval.py --type mean \
--n 800 --mean 42.3 --std 18.1
# Custom confidence level
python3 scripts/confidence_interval.py --type proportion \
--n 1200 --x 96 --confidence 0.99
# Output JSON
python3 scripts/confidence_interval.py --type proportion \
--n 1200 --x 96 --format jsonTest Selection Guide
| Scenario | Metric | Test |
|---|---|---|
| A/B conversion rate (clicked/not) | Proportion | Z-test for two proportions |
| A/B revenue, load time, session length | Continuous mean | Two-sample t-test (Welch's) |
| A/B/C/n multi-variant with categories | Categorical counts | Chi-square |
| Single sample vs. known value | Mean vs. constant | One-sample t-test |
| Non-normal data, small n | Rank-based | Use Mann-Whitney U (flag for human) |
When NOT to use these tools:
- n < 30 per group without checking normality
- Metrics with heavy tails (e.g. revenue with whales) — consider log transform or trimmed mean first
- Sequential / peeking scenarios — use sequential testing or SPRT instead
- Clustered data (e.g. users within countries) — standard tests assume independence
Decision Framework (Post-Experiment)
Use this after running the test:
| p-value | Effect Size | Practical Impact | Decision |
|---|---|---|---|
| < α | Large / Medium | Meaningful | ✅ Ship |
| < α | Small | Negligible | ⚠️ Hold — statistically significant but not worth the complexity |
| ≥ α | — | — | 🔁 Extend (if underpowered) or ❌ Kill |
| < α | Any | Negative UX | ❌ Kill regardless |
Always ask: "If this effect were exactly as measured, would the business care?" If no — don't ship on significance alone.
Effect Size Reference
Effect sizes translate statistical results into practical language:
Cohen's d (means):
| d | Interpretation |
|---|---|
| < 0.2 | Negligible |
| 0.2–0.5 | Small |
| 0.5–0.8 | Medium |
| > 0.8 | Large |
Cohen's h (proportions):
| h | Interpretation |
|---|---|
| < 0.2 | Negligible |
| 0.2–0.5 | Small |
| 0.5–0.8 | Medium |
| > 0.8 | Large |
Cramér's V (chi-square):
| V | Interpretation |
|---|---|
| < 0.1 | Negligible |
| 0.1–0.3 | Small |
| 0.3–0.5 | Medium |
| > 0.5 | Large |
Show full SKILL.md (425 more words)Show less
Proactive Risk Triggers
Surface these unprompted when you spot the signals:
- Peeking / early stopping — Running a test and checking results daily inflates false positive rate. Ask: "Did you look at results before the planned end date?"
- Multiple comparisons — Testing 10 metrics at α=0.05 gives ~40% chance of at least one false positive. Flag when > 3 metrics are being evaluated.
- Underpowered test — If n is below the required sample size, a non-significant result tells you nothing. Always check power retroactively.
- SUTVA violations — If users in control and treatment can interact (e.g. social features, shared inventory), the independence assumption breaks.
- Simpson's Paradox — An aggregate result can reverse when segmented. Flag when segment-level results are available.
- Novelty effect — Significant early results in UX tests often decay. Flag for post-novelty re-measurement.
Output Artifacts
| Request | Deliverable |
|---|---|
| "Did our test win?" | Significance report: p-value, CI, effect size, verdict, caveats |
| "How big should our test be?" | Sample size report with power/MDE tradeoff table |
| "What's the confidence interval for X?" | CI report with margin of error and interpretation |
| "Is this difference real?" | Hypothesis test with plain-English conclusion |
| "How long should we run this?" | Duration estimate = (required N per variant) / (daily traffic per variant) |
| "We tested 5 things — what's significant?" | Multiple comparison analysis with Bonferroni-adjusted thresholds |
Quality Loop
Tag every finding with confidence:
- 🟢 Verified — Test assumptions met, sufficient n, no validity threats
- 🟡 Likely — Minor assumption violations; interpret directionally
- 🔴 Inconclusive — Underpowered, peeking, or data integrity issue; do not act
Communication Standard
Structure all results as:
Bottom Line — One sentence: "Treatment increased conversion by 1.2pp (95% CI: 0.4–2.0pp). Result is statistically significant (p=0.003) with a small effect (h=0.18). Recommend shipping."
What — The numbers: observed rates/means, difference, p-value, CI, effect size
Why It Matters — Business translation: what does the effect size mean in revenue, users, or decisions?
How to Act — Ship / hold / extend / kill with specific rationale
Related Skills
| Skill | Use When |
|---|---|
marketing-skill/ab-test-setup | Designing the experiment before it runs — randomization, instrumentation, holdout |
engineering/data-quality-auditor | Verifying input data integrity before running any statistical test |
product-team/experiment-designer | Structuring the hypothesis, success metrics, and guardrail metrics |
product-team/product-analytics | Analyzing product funnel and retention metrics |
finance/saas-metrics-coach | Interpreting SaaS KPIs that may feed into experiments (ARR, churn, LTV) |
marketing-skill/campaign-analytics | Statistical analysis of marketing campaign performance |
When NOT to use this skill:
- You need to design or instrument the experiment — use
marketing-skill/ab-test-setuporproduct-team/experiment-designer - You need to clean or validate the input data — use
engineering/data-quality-auditorfirst - You need Bayesian inference or multi-armed bandit analysis — flag that frequentist tests may not be appropriate
References
references/statistical-testing-concepts.md— t-test, Z-test, chi-square theory; p-value interpretation; Type I/II errors; power analysis math
© alirezarezvani, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts, references) in engineering/statistical-analyst/skills/statistical-analyst of alirezarezvani/claude-skills.
- SKILL.md
- references/statistical-testing-concepts.md
- scripts/confidence_interval.py
- scripts/hypothesis_tester.py
- scripts/sample_size_calculator.py
Open the folder on GitHubat commit 19392f7
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in alirezarezvani/claude-skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Statistical Analyst 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 |
|---|---|---|---|---|---|---|
| Statistical Analyst this skillalirezarezvani/claude-skills | 28k | 1 repos | ~2.5k | Automated safety check: Pass | MIT | |
| Experimentation Analyticsrampstackco/claude-skills | 935 | 1 repos | ~8.9k | Automated safety check: Pass | MIT | |
| Power Analysisgaasher/Agent-Loop-Skills | 174 | — | ~2.2k | Automated safety check: Pass | MIT | |
| Data Scientistmagnus919/hermes-profiles | 278 | — | ~3.3k | Automated safety check: Pass | MIT | |
| Data Scientistmagnus919/agent-skills | 111 | — | ~4.1k | Automated safety check: Pass | MIT | |
| Review Experiment Resultsharness/harness-skills | 115 | — | ~3.9k | Automated safety check: Pass | Apache-2.0 |
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Related
Questions about Statistical Analyst
What does Statistical Analyst do?
Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes. Statistical Analyst is an agent skill from alirezarezvani/claude-skills. Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes.
When should I use Statistical Analyst?
Statistical Analyst fits situations like: you need to validate whether observed differences are real; size an experiment correctly before launch; interpret test results with confidence.
How do I install Statistical Analyst in Claude Code?
Run `npx skills add alirezarezvani/claude-skills --skill statistical-analyst -a claude-code`. Or copy the skill folder (engineering/statistical-analyst/skills/statistical-analyst in alirezarezvani/claude-skills) into .claude/skills/statistical-analyst in your project. Claude Code loads it when a task matches its description.
How do I install Statistical Analyst in Codex?
Run `npx skills add alirezarezvani/claude-skills --skill statistical-analyst -a codex`. Or copy the skill folder (engineering/statistical-analyst/skills/statistical-analyst in alirezarezvani/claude-skills) into .agents/skills/statistical-analyst in your project. Codex loads it when a task matches its description.
Can I use Statistical Analyst 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 alirezarezvani/claude-skills --skill statistical-analyst -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/statistical-analyst, .gemini/skills/statistical-analyst, .github/skills/statistical-analyst and .opencode/skills/statistical-analyst in your project.
What does Statistical Analyst need to run?
Going by SKILL.md and its folder, Statistical Analyst needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Statistical Analyst 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 Statistical Analyst 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Statistical Analyst use?
Statistical Analyst 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 Statistical Analyst use?
About 2.5k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.6k tokens, read only when the agent opens those files.
What are the alternatives to Statistical Analyst?
Skills that share tags, products or a category with Statistical Analyst: Experimentation Analytics (rampstackco/claude-skills, 935 stars), Power Analysis (gaasher/Agent-Loop-Skills, 174 stars), Data Scientist (magnus919/hermes-profiles, 278 stars) and Data Scientist (magnus919/agent-skills, 111 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Statistical Analyst?
alirezarezvani (a GitHub user) maintains it in alirezarezvani/claude-skills, which has 27,788 GitHub stars. The repository holds 342 skills in this directory. The repository was last updated on August 30, 2026.
Source: alirezarezvani/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.