Find agent skills by outcome

│ → Use max_iter=10

random_state=42 for reproducibility │

│ → Creates: colwasmissing = 1 if NaN

else 0 │

For each column with missing values

evaluate all three branches simultaneously:

For each flagged column

fill in this analysis card:

│ │ Signs: Missingness correlates with OTHER columns

│

missingreport = missingreport.sortvalues('Missing%'

ascending=False)

DISGUISED_NULLS = [?

N/A

df.replace(DISGUISED_NULLS

np.nan

- e.g.

CustomerID must be unique and non-null

- e.g.

Age cannot be 0 or negative

- e.g.

Price is the target — rows missing it are unusable

- Watch for: ?

N/A

- Boolean → binary flags (0/1

True/False)

MODEL_TYPE = [e.g.

XGBoost / LinearRegression / Neural Network]

Productivity

ML_TASK = [e.g.

Binary Classification]

TARGET_COL = [e.g.

price]

Data

Your job is to analyze DATA() and produce a fully reproducible

explainable missing value treatment plan.

PromptBeginner5 minmarkdownQuality: 27

| PROMPT() | This master template — governs all reasoning

rules

Coding & Debugging

You are a Senior Data Scientist and ML Pipeline Engineer specializing in data quality

feature engineering

MISSING VALUES HANDLER

PROMPT() — UNIVERSAL MISSING VALUES HANDLER

- Architectural Constraints: Ensuring that strict structural rules

DevSecOps guidelines

- Modular References: Linking to secondary markdowns (like PRDs

sprint_todo.md

Coding & Debugging

Provide structured updates to PROGRESS.md to keep the context usage under 40%. Do not make direct co...

accurate

- State Tracking: Accurately updating the Progress/Status section with [x] Done

[ ] Current