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Agent skill

Running Placebo Analysis

by pymc-labs in pymc-labs/CausalPy

Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance.

Apache-2.0Auto-check passed

Install Running Placebo Analysis

skills CLI
$ npx skills add pymc-labs/CausalPy --skill running-placebo-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install pymc-labs/CausalPy running-placebo-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/pymc-labs/CausalPy.git skills-src && mkdir -p .claude/skills && cp -r skills-src/causalpy/skills/running-placebo-analysis .claude/skills/running-placebo-analysis && rm -rf skills-src

Use ~/.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/

Facts

Skill name
running-placebo-analysis
GitHub stars
1.2k
Used in
1 other repo
Token cost
~442 tokens
SKILL.md length
174 words
Files
2
Skills in repo
12
Repo updated
First seen
Licence
Apache-2.0

At a glance

Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance.

  • Works in 4 steps: Fit your experiment: Run a CausalPy… → Configure the check: Create a… → Run: Call .run(experiment) (standalone)… → …
  • Checking model robustness
  • SKILL.md covers Workflow, Key Concepts and References
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Running Placebo Analysis is an agent skill from pymc-labs/CausalPy. Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.

Its SKILL.md is about 440 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `reference/placebo_in_time.md`).

It works with PyMC. 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

  • Checking model robustness
  • Verifying lack of pre-intervention effects
  • Estimating study power

Example prompts

  • “Use the running-placebo-analysis skill to perform placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance”
  • “/running-placebo-analysis”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Fit your experiment: Run a CausalPy experiment (ITS, SC) with a PyMC model.
  2. Configure the check: Create a PlaceboInTime with n_folds, optional experiment_factory, and optional assurance parameters.
  3. Run: Call .run(experiment) (standalone) or use within a Pipeline + SensitivityAnalysis.
  4. Evaluate: Inspect the null distribution (theta_new), p_effect_outside_null, and optional assurance results.

What it can do on your machine

Read from SKILL.md and the folder at commit 7882153. 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 Placebo Analysis loads about 442 tokens when it runs. Until then it costs about 60 tokens; SKILL.md has 174 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~60
When it runs · the whole SKILL.md, loaded when a task matches
~442

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 7882153, republished under its Apache-2.0 licence (© pymc-labs). 174 words, ~442 tokens.

Download SKILL.mdSave it as .claude/skills/running-placebo-analysis/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
running-placebo-analysis
description
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.

Running Placebo Analysis

Executes placebo-in-time sensitivity analysis using the core PlaceboInTime check. Builds a hierarchical Bayesian model of the "status quo" (no-effect) distribution, then compares the actual intervention effect against that learned null. Optionally computes Bayesian assurance (operating characteristics).

Workflow

  1. Fit your experiment: Run a CausalPy experiment (ITS, SC) with a PyMC model.
  2. Configure the check: Create a PlaceboInTime with n_folds, optional experiment_factory, and optional assurance parameters.
  3. Run: Call .run(experiment) (standalone) or use within a Pipeline + SensitivityAnalysis.
  4. Evaluate: Inspect the null distribution (theta_new), p_effect_outside_null, and optional assurance results.

Key Concepts

  • Placebo-in-time: Simulating an intervention at a time when none occurred to check if the model falsely detects an effect.
  • Hierarchical null model: A Bayesian model fitted on fold-level summaries that characterises the distribution of effects under no intervention.
  • Assurance: Bayesian operating characteristics — the probability of correctly detecting a real effect given your expected-effect prior and ROPE.
  • Factory Pattern: Decouples the placebo logic from the specific CausalPy experiment type.

References

© 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 1 other file in causalpy/skills/running-placebo-analysis of pymc-labs/CausalPy.

  • SKILL.md
  • reference/placebo_in_time.md

Open the folder on GitHubat commit 7882153

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 Placebo Analysis 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.

Running Placebo Analysis compared with similar skills
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Running Placebo Analysis this skillpymc-labs/CausalPy1.2k1 repos~442Automated safety check: PassApache-2.0
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Bayesian Workflowbrycewang-stanford/Auto-Empirical-Research-Skills4.6k—~3.5kAutomated safety check: PassMIT
PyMC Bayesian Modelingdavila7/claude-code-templates33k11 repos~3.9kAutomated safety check: PassMIT
PymcK-Dense-AI/scientific-agent-skills48k1 repos~2.7kAutomated safety check: NotesApache-2.0
Pathmcpymc-labs/pathmc132—~4kAutomated safety check: PassMIT

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Works with

Questions about Running Placebo Analysis

What does Running Placebo Analysis do?

Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Running Placebo Analysis is an agent skill from pymc-labs/CausalPy. Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance.

When should I use Running Placebo Analysis?

Running Placebo Analysis fits situations like: checking model robustness; verifying lack of pre-intervention effects; estimating study power.

How do I install Running Placebo Analysis in Claude Code?

Run `npx skills add pymc-labs/CausalPy --skill running-placebo-analysis -a claude-code`. Or copy the skill folder (causalpy/skills/running-placebo-analysis in pymc-labs/CausalPy) into .claude/skills/running-placebo-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Running Placebo Analysis in Codex?

Run `npx skills add pymc-labs/CausalPy --skill running-placebo-analysis -a codex`. Or copy the skill folder (causalpy/skills/running-placebo-analysis in pymc-labs/CausalPy) into .agents/skills/running-placebo-analysis in your project. Codex loads it when a task matches its description.

Can I use Running Placebo Analysis 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-placebo-analysis -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-placebo-analysis, .gemini/skills/running-placebo-analysis, .github/skills/running-placebo-analysis and .opencode/skills/running-placebo-analysis in your project.

What does Running Placebo Analysis need to run?

SKILL.md names no scripts, command-line tools or credentials: Running Placebo Analysis is instructions for the agent only.

Does Running Placebo Analysis 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 Placebo Analysis 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 Placebo Analysis use?

Running Placebo Analysis 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 Placebo Analysis use?

About 442 tokens (SKILL.md is roughly 1.8k 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 Placebo Analysis?

Skills that share tags, products or a category with Running Placebo Analysis: Statistical Analysis (spacering-net/codeg, 3.9k stars), Bayesian Workflow (brycewang-stanford/Auto-Empirical-Research-Skills, 4.6k stars), PyMC Bayesian Modeling (davila7/claude-code-templates, 33k stars) and Pymc (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Running Placebo Analysis?

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 9, 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.