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Python Panel Data · brycewang-stanford | DecimalAI

scienceby brycewang-stanford

Python Panel Data

brycewang-stanford/python-panel-data

Source brycewang-stanford/Auto-Empirical-Research-Skills ↗License NOASSERTION

Panel data analysis with Python using linearmodels and pandas.

SkillScore86Maintained

Automatic

Data Analyst pack

Last verified 8/9/2026

What it changes

Passed 19 of 22 test cases with the skill, and 17 of 22 without it.

Without skill77%

With skill86%

Checks it passed that the model alone missed

The response states Python 3.10 or higher.
The response includes interpreting key coefficients, stating key econometric assumptions, and suggesting robustness checks.

2 fixed17 passed either way3 still failing0 broke

Pass-rate lift: +9 pp
Tokens: +18%
Agent turns: 0%
Response time: -18%

Model gemini-3.6-flashMeasured 8/9/2026v119 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/brycewang-stanford-python-panel-data/SKILL.md https://app.decimal.ai/s/brycewang-stanford-python-panel-data/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add brycewang-stanford-python-panel-data, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/brycewang-stanford-python-panel-data/ and pins it in .decimal/skills.lock

decimalai skills add brycewang-stanford-python-panel-data

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/brycewang-stanford-python-panel-data/SKILL.md https://app.decimal.ai/s/brycewang-stanford-python-panel-data/SKILL.md

Raw file: /s/brycewang-stanford-python-panel-data/SKILL.md ↗ · machine-readable summary at /s/brycewang-stanford-python-panel-data.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 2

Prompt

What is the minimum required Python version specified for executing this panel data analytics workflow?

What was checked, without and with the skill
Checked	Without skill	With skill
The response states Python 3.10 or higher.	Missed	Passed

case-16 · gemini-3.6-flash · 8/9/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md117 linessingle file

Python Panel Data

Purpose

This skill helps economists run panel data models in Python using pandas, statsmodels, and linearmodels, with correct fixed effects, clustering, and diagnostics.

When to Use

Estimating fixed effects or random effects models
Running difference-in-differences on panel data
Creating regression tables and plots in Python

Instructions

Follow these steps to complete the task:

Step 1: Understand the Context

Before generating any code, ask the user:

What is the unit of observation and panel identifiers?
Which outcomes and regressors are required?
What fixed effects or time effects are needed?
How should standard errors be clustered?

Step 2: Generate the Output

Based on the context, generate Python code that:

Loads and cleans the data with pandas
Sets a MultiIndex for panel structure
Fits the model using linearmodels.PanelOLS or RandomEffects
Outputs results in a readable table and optional LaTeX

Step 3: Verify and Explain

After generating output:

Interpret key coefficients
Note assumptions (strict exogeneity, parallel trends, etc.)

Track record

Benchmark

19 / 22

test cases passed, vs 17 / 22 without

Adoption

Users: 0
Times used: 0

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/2/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

brycewang-stanford / Auto-Empirical-Research-Skills

skills/09-meleantonio-awesome-econ-ai-stuff/_skills/analysis/python-panel-data/SKILL.md

View on GitHub ↗

License NOASSERTIONImported 8/2/2026Version v1

Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

brycewang-stanford/linearmodelsPanel data, IV/GMM, system regression. PanelOLS (FE/RE), BetweenOLS, Fama-MacBeth, IV2SLS/LIML/GMM, SUR, 3SLS, Driscoll-Kraay SEs. Use for RE/between, system estimation, or GMM. Complements pyfixest (FE + DiD) and statsmodels (GLM + time series).Close match+9 pp pass-rate lift

More skills from the same source and nearby in the registry, with their headline benchmark lift.

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See it in action

What's inside

Python Panel Data

Purpose

When to Use

Instructions

Step 1: Understand the Context

Step 2: Generate the Output

Step 3: Verify and Explain

Track record

Benchmark

Adoption

SkillSafety

Source

Other skills for the same job

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