Scikit Learn
zLanqing/codex-claude-academic-skills
Machine learning in Python with scikit-learn. An agent skill from zLanqing/codex-claude-academic-skills.
Fit, summarize, plot, and interpret a chosen CausalPy experiment.
Install Running Causalpy Experiments
$ npx skills add pymc-labs/CausalPy --skill running-causalpy-experiments -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install pymc-labs/CausalPy running-causalpy-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/pymc-labs/CausalPy.git skills-src && mkdir -p .claude/skills && cp -r skills-src/causalpy/skills/running-causalpy-experiments .claude/skills/running-causalpy-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 "running-causalpy-experiments" agent skill from https://github.com/pymc-labs/CausalPy/tree/main/causalpy/skills/running-causalpy-experiments into .claude/skills/running-causalpy-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "running-causalpy-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/pymc-labs/CausalPy/tree/main/causalpy/skills/running-causalpy-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 pymc-labs/CausalPy --skill running-causalpy-experiments -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install pymc-labs/CausalPy running-causalpy-experiments --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pymc-labs/CausalPy.git skills-src && mkdir -p .agents/skills && cp -r skills-src/causalpy/skills/running-causalpy-experiments .agents/skills/running-causalpy-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 "running-causalpy-experiments" agent skill from https://github.com/pymc-labs/CausalPy/tree/main/causalpy/skills/running-causalpy-experiments into .agents/skills/running-causalpy-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "running-causalpy-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 pymc-labs/CausalPy --skill running-causalpy-experiments -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install pymc-labs/CausalPy running-causalpy-experiments --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pymc-labs/CausalPy.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/causalpy/skills/running-causalpy-experiments .cursor/skills/running-causalpy-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 "running-causalpy-experiments" agent skill from https://github.com/pymc-labs/CausalPy/tree/main/causalpy/skills/running-causalpy-experiments into .cursor/skills/running-causalpy-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "running-causalpy-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/pymc-labs/CausalPy.git --path causalpy/skills/running-causalpy-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 pymc-labs/CausalPy --skill running-causalpy-experiments -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install pymc-labs/CausalPy running-causalpy-experiments --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pymc-labs/CausalPy.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/causalpy/skills/running-causalpy-experiments .gemini/skills/running-causalpy-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 "running-causalpy-experiments" agent skill from https://github.com/pymc-labs/CausalPy/tree/main/causalpy/skills/running-causalpy-experiments into .gemini/skills/running-causalpy-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "running-causalpy-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 pymc-labs/CausalPy running-causalpy-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 pymc-labs/CausalPy --skill running-causalpy-experiments -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/pymc-labs/CausalPy.git skills-src && mkdir -p .github/skills && cp -r skills-src/causalpy/skills/running-causalpy-experiments .github/skills/running-causalpy-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 "running-causalpy-experiments" agent skill from https://github.com/pymc-labs/CausalPy/tree/main/causalpy/skills/running-causalpy-experiments into .github/skills/running-causalpy-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "running-causalpy-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 pymc-labs/CausalPy --skill running-causalpy-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 pymc-labs/CausalPy running-causalpy-experiments --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pymc-labs/CausalPy.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/causalpy/skills/running-causalpy-experiments .opencode/skills/running-causalpy-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 "running-causalpy-experiments" agent skill from https://github.com/pymc-labs/CausalPy/tree/main/causalpy/skills/running-causalpy-experiments into .opencode/skills/running-causalpy-experiments/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "running-causalpy-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
running-causalpy-experiments- GitHub stars
- 1.2k
- Used in
- 1 other repo
- Token cost
- ~1.3k tokens
- SKILL.md length
- 480 words
- Files
- 14
- Skills in repo
- 12
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Fit, summarize, plot, and interpret a chosen CausalPy experiment.
- Works in 7 steps: Load and validate a pandas DataFrame… → Choose a backend: PyMC models for… → Configure the model before construction.… → …
- Tasks that involve Machine learning
- SKILL.md covers Workflow, Model And Prior Guardrails, Common Output Methods and Important Exceptions, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Running Causalpy Experiments is an agent skill from pymc-labs/CausalPy. Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files (for example `reference/custom_priors.md`, `reference/diff_in_diff.md` and `reference/instrumental_variable.md`).
It sits in Data & Analytics, covering Machine learning. It works with PyMC and scikit-learn. The repository describes itself as: A Python package for causal inference in quasi-experimental settings. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Machine learning
Example prompts
- “/running-causalpy-experiments”
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- Load and validate a pandas DataFrame with the data layout required by the chosen experiment.
- Choose a backend: PyMC models for posterior uncertainty and priors, or sklearn-compatible regressors where the experiment supports…
- Configure the model before construction. For PyMC, set sample_kwargs, optional prior_sample_kwargs, and scale-aware priors when predictors…
- Instantiate the experiment. Construction is lazy: nothing is sampled until you call fit(), which returns the fitted experiment (exp =…
- Optionally run prior predictive checks before paying for MCMC: exp.sample_prior_predictive() (uses prior_sample_kwargs from the model…
- Inspect outputs with summary(), effect_summary(), print_coefficients(), and plot() only after fit(); results live on exp.result and…
- Run relevant sensitivity checks through cp.Pipeline, cp.EstimateEffect, and cp.SensitivityAnalysis when robustness matters.
What it can do on your machine
Read from SKILL.md and the folder at commit f17b30f. 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
Running Causalpy Experiments loads about 1.3k tokens when it runs. Until then it costs about 55 tokens; SKILL.md has 480 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 pymc-labs/CausalPy at commit f17b30f, republished under its Apache-2.0 licence (© pymc-labs). 480 words, ~1,289 tokens.
Download SKILL.mdSave it as
.claude/skills/running-causalpy-experiments/SKILL.md (or your agent's skills folder). This skill also uses 13 other files; get the full folder from GitHub.- name
- running-causalpy-experiments
- description
- Fit, summarize, plot, and interpret a chosen CausalPy experiment. Use after the causal method has been selected, including when configuring PyMC/sklearn models and scale-aware custom priors.
Running CausalPy Experiments
Use this skill when the CausalPy experiment class is already known or has just been selected by choosing-causalpy-methods. This skill is for execution: preparing data, instantiating the experiment, choosing a model backend, setting sane priors, inspecting outputs, plotting, and communicating results.
Workflow
- Load and validate a pandas
DataFramewith the data layout required by the chosen experiment. - Choose a backend: PyMC models for posterior uncertainty and priors, or sklearn-compatible regressors where the experiment supports OLS/sklearn.
- Configure the model before construction. For PyMC, set
sample_kwargs, optionalprior_sample_kwargs, and scale-awarepriorswhen predictors or outcomes are not standardized. - Instantiate the experiment. Construction is lazy: nothing is sampled until you call
fit(), which returns the fitted experiment (exp = cp.InterruptedTimeSeries(...).fit()). - Optionally run prior predictive checks before paying for MCMC:
exp.sample_prior_predictive()(usesprior_sample_kwargsfrom the model, default 500 draws), thenexp.plot(group="prior")andexp.effect_summary(group="prior")— under a neutral prior,P(effect > 0)should sit near 0.5. Revise priors by assigning a fresh model (exp.model = cp.pymc_models.LinearRegression(priors={...})); assignment resets all results. - Inspect outputs with
summary(),effect_summary(),print_coefficients(), andplot()only afterfit(); results live onexp.resultandexp.prior_result, and read methods raiseGroupNotSampledExceptionnaming the missing call when a phase has not run. - Run relevant sensitivity checks through
cp.Pipeline,cp.EstimateEffect, andcp.SensitivityAnalysiswhen robustness matters.
Model And Prior Guardrails
- Do not blindly accept diffuse default priors when predictors and outcomes are on very different scales. Either standardize the modeling variables or pass scale-aware priors to the PyMC model.
- For
cp.pymc_models.LinearRegression, configure priors forbetaand the observation noise insidey_hat. - For synthetic-control weight models, priors control donor-weight regularization and outcome noise; see
WeightedSumFitter,SoftmaxWeightedSumFitter, andSyntheticDifferenceInDifferencesWeightFitter. - For
PropensityScore, standardize continuous confounders or use coefficient priors that imply plausible log-odds shifts. - For
InstrumentalVariableRegression, priors are passed at the experiment level throughpriors=...and should reflect the scale of both the treatment-stage and outcome-stage regressions. - Always check posterior diagnostics, prior predictive plausibility when available, coefficient magnitudes, counterfactual fit in the pre-period, and whether effect summaries are stable under reasonable prior alternatives.
Show full SKILL.md (141 more words)Show less
Common Output Methods
experiment.summary(): Prints a method-specific summary where implemented.experiment.effect_summary(): Returns a decision-ready structured effect summary where implemented.experiment.plot(): Visualizes fitted values, counterfactuals, effects, or diagnostics where implemented.experiment.print_coefficients(): Shows model coefficients for model-backed experiments.result = cp.Pipeline(...).run(): Runs estimation, sensitivity checks, and report generation as a reproducible workflow.
Important Exceptions
InversePropensityWeighting.plot()is intentionally a stub. Useplot_ate()andplot_balance_ecdf()instead.InversePropensityWeighting.effect_summary()is not implemented. Inspect ATE draws, overlap, balance, and weight stability instead.InstrumentalVariable.plot(),summary(), andeffect_summary()are not implemented, so inspect model outputs and first-stage/second-stage diagnostics directly.PanelRegression.effect_summary()is not implemented because panel fixed-effects models report coefficient-level estimates rather than time-window impacts. Usesummary(),print_coefficients(), andplot()orplot_coefficients().
References
- Scale-aware custom priors
- Difference-in-Differences
- Interrupted Time Series
- Piecewise Interrupted Time Series
- Synthetic Control
- Synthetic Difference-in-Differences
- Panel Regression
- PrePostNEGD
- Regression Discontinuity
- Regression Kink
- Staggered Difference-in-Differences
- Instrumental Variable
- Inverse Propensity Weighting
© pymc-labs, Apache-2.0. 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 13 other files in causalpy/skills/running-causalpy-experiments of pymc-labs/CausalPy.
- SKILL.md
- reference/custom_priors.md
- reference/diff_in_diff.md
- reference/instrumental_variable.md
- reference/interrupted_time_series.md
- reference/inverse_propensity_weighting.md
- reference/panel_regression.md
- reference/piecewise_its.md
- reference/prepostnegd.md
- reference/regression_discontinuity.md
- reference/regression_kink.md
- reference/staggered_did.md
- reference/synthetic_control.md
- reference/synthetic_difference_in_differences.md
Open the folder on GitHubat commit f17b30f
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 pymc-labs/CausalPy, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Running Causalpy 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 |
|---|---|---|---|---|---|---|
| Running Causalpy Experiments this skillpymc-labs/CausalPy | 1.2k | 1 repos | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Scikit LearnzLanqing/codex-claude-academic-skills | 4.6k | 17 repos | ~3.9k | Automated safety check: Pass | BSD-3-Clause | |
| Senior Data ScientistRaidriar7170/hermes-skilleval | 125 | 6 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Time Series Analytics Useropen-edge-platform/edge-ai-libraries | 169 | — | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Estimate Online Covariancemicroprediction/precise | 336 | — | ~535 | Automated safety check: Pass | MIT | |
| Aeon Time Series Machine Learningdavila7/claude-code-templates | 32k | 14 repos | ~2.6k | Automated safety check: Pass | MIT |
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Load built-in CausalPy example datasets for demos, tutorials, tests, and quick causal-analysis prototypes.
Review PR
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Review CausalPy pull requests end-to-end by classifying PR type, checking branch freshness, mergeability, remote CI, correctness, security, tests, docs, and maintainer concerns.
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Challenge causal claims through structured threat assessment, counterfactual reasoning, and CausalPy falsification checks.
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Detect, configure, and use the project's Python environment (uv by default, conda-compatible tool as a fallback).
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Related
Works with
Categories
Questions about Running Causalpy Experiments
What does Running Causalpy Experiments do?
Fit, summarize, plot, and interpret a chosen CausalPy experiment. Running Causalpy Experiments is an agent skill from pymc-labs/CausalPy. Fit, summarize, plot, and interpret a chosen CausalPy experiment.
When should I use Running Causalpy Experiments?
Running Causalpy Experiments fits situations like: tasks that involve Machine learning.
How do I install Running Causalpy Experiments in Claude Code?
Run `npx skills add pymc-labs/CausalPy --skill running-causalpy-experiments -a claude-code`. Or copy the skill folder (causalpy/skills/running-causalpy-experiments in pymc-labs/CausalPy) into .claude/skills/running-causalpy-experiments in your project. Claude Code loads it when a task matches its description.
How do I install Running Causalpy Experiments in Codex?
Run `npx skills add pymc-labs/CausalPy --skill running-causalpy-experiments -a codex`. Or copy the skill folder (causalpy/skills/running-causalpy-experiments in pymc-labs/CausalPy) into .agents/skills/running-causalpy-experiments in your project. Codex loads it when a task matches its description.
Can I use Running Causalpy 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 pymc-labs/CausalPy --skill running-causalpy-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/running-causalpy-experiments, .gemini/skills/running-causalpy-experiments, .github/skills/running-causalpy-experiments and .opencode/skills/running-causalpy-experiments in your project.
What does Running Causalpy Experiments need to run?
SKILL.md names no scripts, command-line tools or credentials: Running Causalpy Experiments is instructions for the agent only.
Does Running Causalpy 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 Running Causalpy 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 Running Causalpy Experiments use?
Running Causalpy Experiments is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Running Causalpy Experiments use?
About 1.3k tokens (SKILL.md is roughly 5.2k 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 Running Causalpy Experiments?
Skills that share tags, products or a category with Running Causalpy Experiments: Scikit Learn (zLanqing/codex-claude-academic-skills, 4.6k stars), Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars), Time Series Analytics User (open-edge-platform/edge-ai-libraries, 169 stars) and Estimate Online Covariance (microprediction/precise, 336 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Running Causalpy Experiments?
pymc-labs (a GitHub organization) maintains it in pymc-labs/CausalPy, which has 1,201 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 7, 2026.
Source: pymc-labs/CausalPy on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.