Full Empirical Analysis — Classical Python Workflow Agent Skill

Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels +…

Context cost
58.5k tokensestimated from the bundle, loaded when it triggers
Bundle
10 files · 233.8 kBtext throughout, nothing executable
Licence
Source-availablethe author’s own terms
Last change
—no release on file
Servers it uses
Noneruns standalone

What it does

Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures.

What this skill changes about the agent has not been set out here as a list. Its SKILL.md, shown below, says it in the author’s own words.

data sciencepython

The skill itself

A skill is instructions the model reads, so you can see what you are getting: below is the start of its SKILL.md. The front matter loads with every session, the body loads when the skill triggers, and the whole file comes with the install.

SKILL.md114.9 kB · preview
--- name: Full-empirical-analysis-skill triggers: - full empirical analysis in Python - classical econometrics pipeline - traditional Python econometrics - end-to-end empirical workflow - pandas statsmodels linearmodels workflow - replicate an applied economics paper - data cleaning empirical - winsorize and standardize - variable construction - Table 1 summary statistics - balance table - correlation matrix - normality test - heteroskedasticity test - autocorrelation test - stationarity test - multicollinearity VIF - endogeneity test - baseline regression - panel fixed effects - DID workflow Python - event study - instrumental variables regression - regression discontinuity - propensity score matching - synthetic control python - double machine learning - robustness checks - placebo test - specification curve - alternative clustering - heterogeneity analysis - mechanism analysis - mediation analysis - moderation analysis - publication-ready regression table - coefplot - binscatter - event study plot # Mode A — Epidemiology / public health - epidemiology pipeline python - public health causal inference python - target trial emulation python - g-formula python - IPTW marginal structural model python - TMLE doubly robust python - HAL-TMLE python - Mendelian randomization python - MR-Egger weighted median python - STROBE TRIPOD reporting python - E-value sensitivity python - Kaplan-Meier AFT survival python - lifelines survival python - zepid epidemiology - 流行病学 python - 公共健康 python # Mode B — ML causal inference - ML causal inference python - double machine learning DML python - econml DoubleML - meta-learner S T X R DR python - causal forest GRF python - causalml meta-learner - Dragonnet TARNet CEVAE python - Bayesian causal forest BCF python - CATE distribution python - policy tree python - off-policy evaluation python - conformal causal prediction python - mapie conformal - fairness audit python - fairlearn audit - causal discovery PC NOTEARS python - causal-learn cdt - 因果机器学习 python ---
82# Full Empirical Analysis — Classical Python Workflow
83
84This skill is the *canonical* 8-step pipeline an applied economist runs on every empirical paper, written in the **traditional Python ecosystem** — no opinionated one-stop wrapper. Every step calls libraries directly (pandas, numpy, scipy, statsmodels, linearmodels, pyfixest, rdrobust, econml, causalml, matplotlib, seaborn), so the agent — or the user reading the agent's code — has full visibility and can swap any component.
85
86**Companion skill**: if the user prefers a single-import agent-native DSL (import statspai as sp), route to 00-StatsPAI_skill instead. **This skill is the opposite philosophy**: everything explicit, everything inspectable, every diagnostic run by hand, every plot shaped by the user.
87
88## Philosophy
89
901. **Traditional stack, no magic.** Agents should be able to read every line and know exactly which library / estimator / standard error family is at work.
912. **Full pipeline, not just estimation.** 80% of the time on a real paper is steps 1–4 and 6–8. This skill treats them as first-class, not an afterthought.
923. **Rich outputs.** Every step produces at least one table or figure — never a single point estimate in isolation.
934. **Progressive disclosure.** SKILL.md gives the canonical call at each step; references/ holds variant-specific depth (dozens of tests, estimator-specific diagnostics, plot recipes).
945. **Reproducible.** Every code block is runnable after pip install -r requirements.txt and df = pd.read_csv(...).
95
96## SkillOpt-style execution gate
97
98Use this long playbook as a seed skill, not as a script to exhaustively apply. SkillOpt discipline: treat each local analysis-code change as a candidate patch that must beat a selection check and survive a held-out check before it becomes reusable boilerplate. Before writing or revising analysis code, compress the user's request into a task-local best_skill card:
99
100```text
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md13,829 words
Files10
LicenceSource-available
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈1,300
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
57,150
on trigger
The instruction body and 9 supporting files, read only when the skill fires.
29.2%
of a 200k window
Ten skills this size would not fit in the window together.
050k100k150k200k context window

58.5k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Heavy. Teams tend to install this one per project rather than globally, and load it only when the job comes up.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

The procedure it runs

The procedure has not been set out here step by step. It is in the body of its SKILL.md: the start of it is shown above, and the whole file comes with the install.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

Its SKILL.md does not declare which tools it asks the agent for, so what it touches is whatever the instructions themselves ask for — read them before you install it.

What it asks for
Writes filesnot declared
Network accessnot declared

This SKILL.md carries no allowed-tools line, so neither is declared. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

10 files, 233.8 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md114.9 kB
  • README.md12.1 kB
  • references/01-data-cleaning.md12.2 kB
  • references/02-data-transformation.md12.0 kB
  • references/03-descriptive-stats.md10.7 kB
  • references/04-statistical-tests.md12.1 kB
  • references/05-modeling.md17.9 kB
  • references/06-robustness.md12.7 kB
  • references/07-further-analysis.md12.9 kB
  • references/08-tables-plots.md16.3 kB
What is not in it

No dependencies and nothing executable: a skill is text the agent reads, so the bundle is 10 files you can review in full before installing. The Source-available licence covers the templates and examples as well as the instructions.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

# Full Empirical Analysis — Classical Python Workflow · 58.5k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/full-empirical-analysis-classical-python-workflow/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/full-empirical-analysis-classical-python-workflow" ] && { [ ! -e .claude/skills/full-empirical-analysis-classical-python-workflow ] || { mv .claude/skills/full-empirical-analysis-classical-python-workflow "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/full-empirical-analysis-classical-python-workflow" .claude/skills/ && echo "Installed .claude/skills/full-empirical-analysis-classical-python-workflow"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add brycewang-stanford/full-empirical-analysis-classical-python-workflow

Windows PowerShell says 'running scripts is disabled on this system'? Run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned once, or type npx.cmd instead of npx (codex.cmd, gemini.cmd, grok.cmd likewise). PowerShell 7 needs neither. Writes to .claude/skills/full-empirical-analysis-classical-python-workflow/ in the current project. For every project instead, run mkdir -p ~/.claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/full-empirical-analysis-classical-python-workflow/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/full-empirical-analysis-classical-python-workflow" ] && { [ ! -e ~/.claude/skills/full-empirical-analysis-classical-python-workflow ] || { mv ~/.claude/skills/full-empirical-analysis-classical-python-workflow "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/full-empirical-analysis-classical-python-workflow" ~/.claude/skills/ && echo "Installed ~/.claude/skills/full-empirical-analysis-classical-python-workflow" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/full-empirical-analysis-classical-python-workflow/ only after the download succeeds, so files left there by another skill of the same name, or by an older release, do not mix in. A copy already there is moved aside, not deleted, and the line prints where. It appears as Full-empirical-analysis-skill in Gemini CLI, Codex, Copilot and Grok (its own name); Claude Code lists it by the folder name, full-empirical-analysis-classical-python-workflow.

Which clients pick it up on their own

A skill is a folder of text. Most of these read a skills folder and pick it up without being told; the rest take the same folder another way — an upload in the app, a parameter on an API call — and then choose it by its description just the same.

Claude Code.claude/skills/
Claude DesktopCustomize › Skills
ChatGPTSkills › Create › Upload
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok Build.grok/skills/
Microsoft CopilotSkills › Upload a skill
PerplexityComputer › Skills › Upload a skill
DeepSeek Harness.dsh/skills/
Zed.agents/skills/
Devin Desktop.devin/skills/
Windsurf (legacy).windsurf/skills/
OpenAI Agents SDKInstructions or sandbox Skills
HTTP / APIa request parameter
This release
Versionnot versioned
Publishedno release date on file
PriceFree
Referencebrycewang-stanford/full-empirical-analysis-classical-python-workflow

Versions

Its author publishes no version number, so there is nothing here to pin to: what you install is the folder as it stands today. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

Pinning

Nothing to pin to: this skill carries no version number of its own. What you install is what the folder holds on the day you install it.

Reviews

—no reviews yet

Nobody has reviewed this skill yet. The rating is the mean of the reviews written here, so there is none until somebody writes the first.

Sign in to review it

A review is tied to an account so it can be answered, edited and taken down by the person who wrote it.

Sign in
Who can post

Anybody with an account, one review each. It carries the version that was current when you wrote it, and the publisher may reply once.

Publisher
Servers0
Claim this skill