New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Feature Exploration

by pymc-labs in pymc-labs/CausalPy

Explore unfamiliar APIs, libraries, or implementation behavior with minimal reproducible examples and documented findings.

Apache-2.0Auto-check passed

Install Feature Exploration

skills CLI
$ npx skills add pymc-labs/CausalPy --skill feature-exploration -a claude-code

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

GitHub CLI
$ gh skill install pymc-labs/CausalPy feature-exploration --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/.agents/skills/feature-exploration .claude/skills/feature-exploration && 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
feature-exploration
GitHub stars
1.2k
Token cost
~442 tokens
SKILL.md length
214 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
Apache-2.0

At a glance

Explore unfamiliar APIs, libraries, or implementation behavior with minimal reproducible examples and documented findings.

  • Works in 7 steps: State the uncertainty: name the API,… → Read the closest authoritative docs or… → Build the smallest reproducible example… → …
  • Implementation details are unclear and can be resolved by reading docs
  • SKILL.md covers Workflow, Guardrails and Output
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Feature Exploration is an agent skill from pymc-labs/CausalPy. Explore unfamiliar APIs, libraries, or implementation behavior with minimal reproducible examples and documented findings. Use when implementation details are unclear and can be resolved by reading docs, inspecting code, and running focused experiments.

Its SKILL.md is about 440 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

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

  • Implementation details are unclear and can be resolved by reading docs
  • Inspecting code
  • Running focused experiments

Example prompts

  • “/feature-exploration”

Workflow steps

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

  1. State the uncertainty: name the API, behavior, or integration detail that needs proof.
  2. Read the closest authoritative docs or source code before experimenting.
  3. Build the smallest reproducible example that answers the question.
  4. Run it in the project environment when it imports project code or dependencies.
  5. Iterate only until the behavior is understood.
  6. Record the finding in the final answer or in the durable project docs if it affects future work.
  7. Apply the production change using the confirmed behavior.

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

Feature Exploration loads about 442 tokens when it runs. Until then it costs about 68 tokens; SKILL.md has 214 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~68
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 f17b30f, republished under its Apache-2.0 licence (© pymc-labs). 214 words, ~442 tokens.

Download SKILL.mdSave it as .claude/skills/feature-exploration/SKILL.md (or your agent's skills folder).
name
feature-exploration
description
Explore unfamiliar APIs, libraries, or implementation behavior with minimal reproducible examples and documented findings. Use when implementation details are unclear and can be resolved by reading docs, inspecting code, and running focused experiments.

Feature Exploration

Use this developer skill when a task depends on API behavior that is unclear from memory or partially documented. The goal is to resolve uncertainty before changing production code.

Workflow

  1. State the uncertainty: name the API, behavior, or integration detail that needs proof.
  2. Read the closest authoritative docs or source code before experimenting.
  3. Build the smallest reproducible example that answers the question.
  4. Run it in the project environment when it imports project code or dependencies.
  5. Iterate only until the behavior is understood.
  6. Record the finding in the final answer or in the durable project docs if it affects future work.
  7. Apply the production change using the confirmed behavior.

Guardrails

  • Do not create throwaway files in tracked locations. If a temporary note is needed, use .scratch/.
  • Do not leave exploratory scripts behind unless they become real tests or documented examples.
  • Prefer adding a proper test under causalpy/tests/ when the discovered behavior is important to preserve.
  • Keep experiments narrow; avoid broad refactors while investigating.
  • Follow the repository environment rules in AGENTS.md for commands that import CausalPy, PyMC, PyTensor, matplotlib, or repo tooling.

Output

When using this skill, report:

  • The uncertainty investigated.
  • The evidence gathered.
  • The conclusion.
  • Any production change or test that now relies on that conclusion.

© 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

Just SKILL.md in .agents/skills/feature-exploration of pymc-labs/CausalPy.

Open the folder on GitHubat commit f17b30f

Compare with similar skills

Feature Exploration 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.

Feature Exploration compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Feature Exploration this skillpymc-labs/CausalPy1.2k—~442Automated safety check: PassApache-2.0
Statistical Analysisspacering-net/codeg3.9k3 repos~5kAutomated safety check: PassMIT
Bayesian Workflowbrycewang-stanford/Auto-Empirical-Research-Skills4.5k—~3.5kAutomated safety check: PassMIT
PyMC Bayesian Modelingdavila7/claude-code-templates32k11 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

Similar skills

  • 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.

    3.9k GitHub starsUsed in 3 repos~5k tokens
    Data & AnalyticsAuto-check passed

  • Bayesian Workflow

    brycewang-stanford/Auto-Empirical-Research-Skills

    Opinionated Bayesian modeling workflow with PyMC and ArviZ. An agent skill from brycewang-stanford/Auto-Empirical-Research-Skills.

    4.5k GitHub stars~3.5k tokensUpdated 4 days ago
    Data & AnalyticsAuto-check passed

  • PyMC Bayesian Modeling

    davila7/claude-code-templates

    Builds, fits, checks and compares Bayesian models in PyMC, from priors and NUTS sampling to variational inference, LOO and WAIC comparison, and diagnostics.

    32k GitHub starsUsed in 11 repos~3.9k tokens
    Data & AnalyticsAuto-check passed

  • Pymc

    K-Dense-AI/scientific-agent-skills

    Builds and checks Bayesian models with PyMC, including hierarchical models, NUTS MCMC, variational inference, mutable-data predictions, posterior predictive checks, diagnostics, and PSIS-LOO model…

    48k GitHub starsUsed in 1 repo~2.7k tokens
    Research & ScienceAuto-check: notes

  • Pathmc

    pymc-labs/pathmc

    Bayesian path analysis (observed-variable SEM) in PyMC. An agent skill from pymc-labs/pathmc.

    132 GitHub stars~4k tokensUpdated 6 days ago
    Auto-check passed

  • Bayesian Estimation

    brycewang-stanford/Auto-Empirical-Research-Skills

    This skill covers Bayesian estimation and inference in quantitative social science.

    4.5k GitHub stars~3.4k tokensUpdated 4 days ago
    Data & AnalyticsAuto-check passed

More from pymc-labs/CausalPy

All 12 skills in this repo

  • Example Datasets

    pymc-labs/CausalPy

    Load built-in CausalPy example datasets for demos, tutorials, tests, and quick causal-analysis prototypes.

    1.2k GitHub stars~587 tokensUpdated today
    Auto-check passed

  • Review PR

    pymc-labs/CausalPy

    Review CausalPy pull requests end-to-end by classifying PR type, checking branch freshness, mergeability, remote CI, correctness, security, tests, docs, and maintainer concerns.

    1.2k GitHub stars~2.2k tokensUpdated today
    Auto-check passed

  • Causal Detective

    pymc-labs/CausalPy

    Challenge causal claims through structured threat assessment, counterfactual reasoning, and CausalPy falsification checks.

    1.2k GitHub stars~809 tokensUpdated today
    Auto-check passed

  • Running Placebo Analysis

    pymc-labs/CausalPy

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

    1.2k GitHub starsUsed in 1 repo~442 tokens
    Auto-check passed

  • Python Environment

    pymc-labs/CausalPy

    Detect, configure, and use the project's Python environment (uv by default, conda-compatible tool as a fallback).

    1.2k GitHub stars~1.1k tokensUpdated today
    Auto-check passed

  • GitHub Issues

    pymc-labs/CausalPy

    Create, evaluate, and triage GitHub issues for CausalPy. An agent skill from pymc-labs/CausalPy.

    1.2k GitHub stars~254 tokensUpdated today
    Auto-check passed

Works with

Questions about Feature Exploration

What does Feature Exploration do?

Explore unfamiliar APIs, libraries, or implementation behavior with minimal reproducible examples and documented findings. Feature Exploration is an agent skill from pymc-labs/CausalPy. Explore unfamiliar APIs, libraries, or implementation behavior with minimal reproducible examples and documented findings.

When should I use Feature Exploration?

Feature Exploration fits situations like: implementation details are unclear and can be resolved by reading docs; inspecting code; running focused experiments.

How do I install Feature Exploration in Claude Code?

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

How do I install Feature Exploration in Codex?

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

Can I use Feature Exploration 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 feature-exploration -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/feature-exploration, .gemini/skills/feature-exploration, .github/skills/feature-exploration and .opencode/skills/feature-exploration in your project.

What does Feature Exploration need to run?

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

Does Feature Exploration 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 Feature Exploration 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 Feature Exploration use?

Feature Exploration 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 Feature Exploration 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 Feature Exploration?

Skills that share tags, products or a category with Feature Exploration: Statistical Analysis (spacering-net/codeg, 3.9k stars), Bayesian Workflow (brycewang-stanford/Auto-Empirical-Research-Skills, 4.5k stars), PyMC Bayesian Modeling (davila7/claude-code-templates, 32k 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 Feature Exploration?

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 8, 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.