Senior Data Scientist
Raidriar7170/hermes-skilleval
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics.
This skill should be used when designing, running, validating, or auditing statistical experiments on personal or observational time-series data (health metrics, speech/text corpora, behavioral…
Install Rigorous Experiments
$ npx skills add glebis/claude-skills --skill rigorous-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install glebis/claude-skills rigorous-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/glebis/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/rigorous-experiments .claude/skills/rigorous-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 "rigorous-experiments" agent skill from https://github.com/glebis/claude-skills/tree/main/rigorous-experiments into .claude/skills/rigorous-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rigorous-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/glebis/claude-skills/tree/main/rigorous-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 glebis/claude-skills --skill rigorous-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install glebis/claude-skills rigorous-experiments --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/glebis/claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/rigorous-experiments .agents/skills/rigorous-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 "rigorous-experiments" agent skill from https://github.com/glebis/claude-skills/tree/main/rigorous-experiments into .agents/skills/rigorous-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rigorous-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 glebis/claude-skills --skill rigorous-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install glebis/claude-skills rigorous-experiments --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/glebis/claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/rigorous-experiments .cursor/skills/rigorous-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 "rigorous-experiments" agent skill from https://github.com/glebis/claude-skills/tree/main/rigorous-experiments into .cursor/skills/rigorous-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rigorous-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/glebis/claude-skills.git --path rigorous-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 glebis/claude-skills --skill rigorous-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install glebis/claude-skills rigorous-experiments --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/glebis/claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/rigorous-experiments .gemini/skills/rigorous-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 "rigorous-experiments" agent skill from https://github.com/glebis/claude-skills/tree/main/rigorous-experiments into .gemini/skills/rigorous-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rigorous-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 glebis/claude-skills rigorous-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 glebis/claude-skills --skill rigorous-experiments -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/glebis/claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/rigorous-experiments .github/skills/rigorous-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 "rigorous-experiments" agent skill from https://github.com/glebis/claude-skills/tree/main/rigorous-experiments into .github/skills/rigorous-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rigorous-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 glebis/claude-skills --skill rigorous-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 glebis/claude-skills rigorous-experiments --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/glebis/claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/rigorous-experiments .opencode/skills/rigorous-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 "rigorous-experiments" agent skill from https://github.com/glebis/claude-skills/tree/main/rigorous-experiments into .opencode/skills/rigorous-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "rigorous-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
rigorous-experiments- GitHub stars
- 388
- Token cost
- ~1.7k tokens
- SKILL.md length
- 780 words
- Files
- 16 (incl. scripts, references)
- Skills in repo
- 91
- Repo updated
- First seen
- Licence
- MIT
At a glance
This skill should be used when designing, running, validating, or auditing statistical experiments on personal or observational time-series data (health metrics, speech/text corpora, behavioral…
- Works in 8 steps: Pre-register before computing.… → Exact permutation, never sampled, on… → Permute over the full calendar, not the… → …
- Design an experiment
- SKILL.md covers Modes, Non-negotiable core (all modes), Workflow (full study) and Viewing results, plus 1 more section
- Runs Python scripts from its folder; calls python3
What it does
Rigorous Experiments is an agent skill from glebis/claude-skills. This skill should be used when designing, running, validating, or auditing statistical experiments on personal or observational time-series data (health metrics, speech/text corpora, behavioral logs, diaries, n-of-1 self-tracking). It enforces pre-registration, exact permutation tests, FDR discipline, data-validation gates, adversarial code review, and cross-validation with external models. Triggers on "design an experiment", "test this hypothesis on my data", "is this correlation real", "audit these findings"…
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 19 other files, including scripts and reference files (for example `evals/cases/bad_exp.py`, `evals/cases/bad_results.json` and `evals/cases/cases.json`).
It sits in Data & Analytics, covering Machine learning, Forecasting and time series and Journaling and reflection. The repository describes itself as: Collection of Claude Code skills for enhanced AI workflows. The licence is MIT.
When your agent uses it
- Design an experiment
- Test this hypothesis on my data
- Is this correlation real
- Audit these findings
Example prompts
- “design an experiment”
- “test this hypothesis on my data”
- “is this correlation real”
- “/rigorous-experiments”
Requirements
- Python 3
Workflow steps
8 steps, taken from the first numbered list in SKILL.md.
- Pre-register before computing. Hypotheses, exact tests, family size
- Exact permutation, never sampled, on small n. A session sequence of
- Permute over the full calendar, not the compressed series. Shifting
- BH with FIXED family size m, a LITERAL CONSTANT declared at design
- Stationarity check before correlating trending series. Exact
- Stratify before pooling (Simpson check): within group (e.g.
- Controls can re-describe a finding, not just kill it. When a control
- Honest statuses: confirmed (q<0.10 exact) ≠ lead (p<0.06) ≠ null ≠
What it can do on your machine
Read from SKILL.md and the folder at commit 7524dff. 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
Rigorous Experiments loads about 1.7k tokens when it runs, and up to ~6.2k if it reads all its reference files. Until then it costs about 157 tokens; SKILL.md has 780 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 glebis/claude-skills at commit 7524dff, republished under its MIT licence (© glebis). 780 words, ~1,731 tokens.
Download SKILL.mdSave it as
.claude/skills/rigorous-experiments/SKILL.md (or your agent's skills folder). This skill also uses 15 other files; get the full folder from GitHub.- name
- rigorous-experiments
- description
- This skill should be used when designing, running, validating, or auditing statistical experiments on personal or observational time-series data (health metrics, speech/text corpora, behavioral logs, diaries, n-of-1 self-tracking). It enforces pre-registration, exact permutation tests, FDR discipline, data-validation gates, adversarial code review, and cross-validation with external models. Triggers on "design an experiment", "test this hypothesis on my data", "is this correlation real", "audit these findings", "pre-register", "validate this dataset", or any n-of-1 / quantified-self analysis request.
Rigorous Experiments
Run statistical experiments on observational/personal time-series data that survive scrutiny. Distilled from a 54-experiment n-of-1 program in which sampled permutation tests, missing-data artifacts, app-categorization bugs and collinear mechanisms repeatedly manufactured — and then destroyed — "findings". Every rule here exists because its absence once produced a wrong conclusion.
Modes
Pick the mode matching the request; chain them for a full study.
| Mode | When | Reference |
|---|---|---|
| design | New hypothesis or study | references/design.md |
| conduct | Implementing + running the experiment | references/statistics.md |
| validate-data | Before trusting ANY new data source | references/data-validation.md |
| cross-validate | Findings worth defending; code review; external model review (e.g. GPT Pro) | references/cross-validation.md |
| investigate-leads | A sweep/run produced leads (p<0.06, not FDR-confirmed) | references/lead-investigation.md |
| audit | Re-examining past claims, registries of findings | references/statistics.md §Audit |
Non-negotiable core (all modes)
- Pre-register before computing. Hypotheses, exact tests, family size m, and the acceptance threshold go in the script docstring BEFORE the first run. Post-hoc tests are reported as descriptive, never promoted.
- Exact permutation, never sampled, on small n. A session sequence of
n=19 has 18 circular shifts: the minimum honest p is ~1/19≈0.05.
Sampling 2000 shifts with replacement fabricates precision (this killed
a flagship "q=0.028" finding). Use
scripts/perm_stats.py. - Permute over the full calendar, not the compressed series. Shifting a gap-compressed series breaks the timeline; keep missingness as NaN masks re-applied per shift. Event indicators must be pure 0/1 with no gaps — missingness lives only in the outcome series.
- BH with FIXED family size m, a LITERAL CONSTANT declared at design
time — never
len(tests)(that defeats pre-registration; the linter rejects it). Assert the run matches the declared m. Confirmatory families small and separate from exploratory sweeps; pooling everything into one BH buries true effects, cherry-picking families manufactures them. Plain BH assumes independent/positively-dependent tests; for strongly dependent lag families use BH-Yekutieli or maxT resampling. - Stationarity check before correlating trending series. Exact circular shift on a trending series is "exactly, reproducibly wrong": report prewhitened-r (AR1 residuals) and stationary bootstrap alongside.
- Stratify before pooling (Simpson check): within group (e.g. therapy/coaching) and within regime (pre/post known breaks). A pooled r=−0.25 once hid therapy −0.64 vs coaching +0.53.
- Controls can re-describe a finding, not just kill it. When a control collapses an effect, check collinearity of control and predictor — r(self-focus, session-length)=0.79 meant "mechanism ambiguous", not "effect fake". Report the decomposition.
- Honest statuses: confirmed (q<0.10 exact) ≠ lead (p<0.06) ≠ null ≠ descriptive. Status flips are recorded, never silently edited. Nulls with adequate power are findings. Robust ≠ significant: a lead surviving leave-one-out at small n is still underpowered — a candidate for prospective test, not a finding. 8b. Series scope is part of the test. A lagged "[t+1]" means the next unit in the series the hypothesis is about, not the next pooled row; define scope before lagging (it once flipped a sign). When recomputing a prior result, reproduce a stored artifact on that scope first.
- Privacy: raw text/audio never enters output files or external uploads — statistics, rates and embedding-derived scores only.
- Plain-language reporting: every statistic carries its practical meaning inline; define r/p/q/n once per report; no untranslated jargon calques. Narrative first, numbers as support.
Show full SKILL.md (268 more words)Show less
Workflow (full study)
validate-datagate on any new source (see reference — the checklist has caught: zero-vs-missing conflation, dedup semantics, substring category bugs, rolling purge windows, timezone conventions).design: pre-registered hypotheses + family + power sanity.conduct: implement withscripts/perm_stats.py; run; write results JSON with tests, statuses, and caveats including known limitations.cross-validate: adversarial code review (e.g. Codex read-only) BEFORE trusting results; fix findings; re-run. For major claims, external model review with a privacy-screened archive.investigate-leadson anything that surfaced as a lead (not at the same scale — the triage battery: LOO, directionality, detrend-vs-step, within-cycle, prewhiten+bootstrap; consolidate same-direction leads into one composite). Mark diagnostic runsdescriptive_only: true.- Verdicts in honest prose (mixed/rejected allowed); report; registry update with status provenance.
Viewing results
Launch the bundled explorer over any directory of results JSONs:
python3 scripts/explorer.py <results_dir> [--port 8799] [--pattern "exp*.json"] [--sort newest|oldest]Generates explorer.html in the directory, starts (or reuses) a loopback
http server on the port, and opens the browser: experiment list with
confirmed/lead badges, filter, sortable test tables color-coded by
status, verdicts, caveats, raw JSON. The page fetches result files live —
re-running experiments updates the view; re-run the script only when new
result files appear. Serve over localhost, never file:// (CDN fonts) and
never on a non-loopback interface (results may contain personal
statistics).
Evals
Run python3 evals/run_evals.py (from the skill directory) to lint an
experiment script/results pair against the standards (pre-registration
present, fixed literal m, exact perm usage, caveats, no raw text in
outputs). A diagnostic/triage run that intentionally mints no new tests
sets descriptive_only: true in its results JSON to satisfy the
"has tests" check. Eval cases in evals/cases/ document expected
pass/fail examples.
© glebis, 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 15 other files (scripts, references) in rigorous-experiments of glebis/claude-skills.
- SKILL.md
- dist.zip
- evals/cases/bad_exp.py
- evals/cases/bad_results.json
- evals/cases/cases.json
- evals/cases/good_exp.py
- evals/cases/good_results.json
- evals/run_evals.py
- references/cross-validation.md
- references/data-validation.md
- references/design.md
- references/lead-investigation.md
- references/statistics.md
- scripts/explorer.py
- scripts/perm_stats.py
- scripts/triage.py
Open the folder on GitHubat commit 7524dff
Compare with similar skills
Rigorous 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 |
|---|---|---|---|---|---|---|
| Rigorous Experiments this skillglebis/claude-skills | 388 | — | ~1.7k | Automated safety check: Pass | MIT | |
| Senior Data ScientistRaidriar7170/hermes-skilleval | 125 | 6 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Time Series Analytics Useropen-edge-platform/edge-ai-libraries | 168 | — | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Aeon Time Series Machine Learningdavila7/claude-code-templates | 32k | 14 repos | ~2.6k | Automated safety check: Pass | MIT | |
| Data Sciencetravisjneuman/.claude | 101 | 1 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Data Scientistdavila7/claude-code-templates | 32k | 8 repos | ~2.6k | Automated safety check: Pass | MIT |
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Categories
Questions about Rigorous Experiments
What does Rigorous Experiments do?
This skill should be used when designing, running, validating, or auditing statistical experiments on personal or observational time-series data (health metrics, speech/text corpora, behavioral…. Rigorous Experiments is an agent skill from glebis/claude-skills. This skill should be used when designing, running, validating, or auditing statistical experiments on personal or observational time-series data (health metrics, speech/text corpora, behavioral logs, diaries, n-of-1 self-tracking).
When should I use Rigorous Experiments?
Rigorous Experiments fits situations like: design an experiment; test this hypothesis on my data; is this correlation real; audit these findings.
How do I install Rigorous Experiments in Claude Code?
Run `npx skills add glebis/claude-skills --skill rigorous-experiments -a claude-code`. Or copy the skill folder (rigorous-experiments in glebis/claude-skills) into .claude/skills/rigorous-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Rigorous Experiments in Codex?
Run `npx skills add glebis/claude-skills --skill rigorous-experiments -a codex`. Or copy the skill folder (rigorous-experiments in glebis/claude-skills) into .agents/skills/rigorous-experiments in your project. Codex loads it when a task matches its description.
Can I use Rigorous 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 glebis/claude-skills --skill rigorous-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/rigorous-experiments, .gemini/skills/rigorous-experiments, .github/skills/rigorous-experiments and .opencode/skills/rigorous-experiments in your project.
What does Rigorous Experiments need to run?
Going by SKILL.md and its folder, Rigorous Experiments needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Rigorous 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 Rigorous 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Rigorous Experiments use?
Rigorous 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 Rigorous Experiments use?
About 1.7k tokens (SKILL.md is roughly 6.9k 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 4.5k tokens, read only when the agent opens those files.
What are the alternatives to Rigorous Experiments?
Skills that share tags, products or a category with Rigorous Experiments: Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars), Time Series Analytics User (open-edge-platform/edge-ai-libraries, 168 stars), Aeon Time Series Machine Learning (davila7/claude-code-templates, 32k stars) and Data Science (travisjneuman/.claude, 101 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Rigorous Experiments?
glebis (a GitHub user) maintains it in glebis/claude-skills, which has 388 GitHub stars. The repository holds 91 skills in this directory. The repository was last updated on September 26, 2026.
Source: glebis/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.