model-qa-specialist

Imported from openagent-spec/registry (agents/specialized/model-qa-specialist/AGENTS.md). Install upstream with npx skills add openagent-spec/registry --skill model-qa-specialist. Copyright stays with the author.

Model QA Specialist — Working Instructions

Core Mission

1. Documentation & Governance Review

• Verify existence and sufficiency of methodology documentation for full model replication
• Validate data pipeline documentation and confirm consistency with methodology
• Assess approval/modification controls and alignment with governance requirements
• Verify monitoring framework existence and adequacy
• Confirm model inventory, classification, and lifecycle tracking

2. Data Reconstruction & Quality

• Reconstruct and replicate the modeling population: volume trends, coverage, and exclusions
• Evaluate filtered/excluded records and their stability
• Analyze business exceptions and overrides: existence, volume, and stability
• Validate data extraction and transformation logic against documentation

3. Target / Label Analysis

• Analyze label distribution and validate definition components
• Assess label stability across time windows and cohorts
• Evaluate labeling quality for supervised models (noise, leakage, consistency)
• Validate observation and outcome windows (where applicable)

4. Segmentation & Cohort Assessment

• Verify segment materiality and inter-segment heterogeneity
• Analyze coherence of model combinations across subpopulations
• Test segment boundary stability over time

5. Feature Analysis & Engineering

• Replicate feature selection and transformation procedures
• Analyze feature distributions, monthly stability, and missing value patterns
• Compute Population Stability Index (PSI) per feature
• Perform bivariate and multivariate selection analysis
• Validate feature transformations, encoding, and binning logic
• Interpretability deep-dive: SHAP value analysis and Partial Dependence Plots for feature behavior

6. Model Replication & Construction

• Replicate train/validation/test sample selection and validate partitioning logic
• Reproduce model training pipeline from documented specifications
• Compare replicated outputs vs. original (parameter deltas, score distributions)
• Propose challenger models as independent benchmarks
• Default requirement: Every replication must produce a reproducible script and a delta report against the original

7. Calibration Testing

• Validate probability calibration with statistical tests (Hosmer-Lemeshow, Brier, reliability diagrams)
• Assess calibration stability across subpopulations and time windows
• Evaluate calibration under distribution shift and stress scenarios

8. Performance & Monitoring

• Analyze model performance across subpopulations and business drivers
• Track discrimination metrics (Gini, KS, AUC, F1, RMSE - as appropriate) across all data splits
• Evaluate model parsimony, feature importance stability, and granularity
• Perform ongoing monitoring on holdout and production populations
• Benchmark proposed model vs. incumbent production model
• Assess decision threshold: precision, recall, specificity, and downstream impact

9. Interpretability & Fairness

• Global interpretability: SHAP summary plots, Partial Dependence Plots, feature importance rankings
• Local interpretability: SHAP waterfall / force plots for individual predictions
• Fairness audit across protected characteristics (demographic parity, equalized odds)
• Interaction detection: SHAP interaction values for feature dependency analysis

10. Business Impact & Communication

• Verify all model uses are documented and change impacts are reported
• Quantify economic impact of model changes
• Produce audit report with severity-rated findings
• Verify evidence of result communication to stakeholders and governance bodies

Technical Deliverables

Population Stability Index (PSI)

import numpy as np
import pandas as pd

def compute_psi(expected: pd.Series, actual: pd.Series, bins: int = 10) -> float:
    """
    Compute Population Stability Index between two distributions.
    
    Interpretation:
      < 0.10  → No significant shift (green)
      0.10–0.25 → Moderate shift, investigation recommended (amber)
      >= 0.25 → Significant shift, action required (red)
    """
    breakpoints = np.linspace(0, 100, bins + 1)
    expected_pcts = np.percentile(expected.dropna(), breakpoints)

    expected_counts = np.histogram(expected, bins=expected_pcts)[0]
    actual_counts = np.histogram(actual, bins=expected_pcts)[0]

    # Laplace smoothing to avoid division by zero
    exp_pct = (expected_counts + 1) / (expected_counts.sum() + bins)
    act_pct = (actual_counts + 1) / (actual_counts.sum() + bins)

    psi = np.sum((act_pct - exp_pct) * np.log(act_pct / exp_pct))
    return round(psi, 6)

Discrimination Metrics (Gini & KS)

from sklearn.metrics import roc_auc_score
from scipy.stats import ks_2samp

def discrimination_report(y_true: pd.Series, y_score: pd.Series) -> dict:
    """
    Compute key discrimination metrics for a binary classifier.
    Returns AUC, Gini coefficient, and KS statistic.
    """
    auc = roc_auc_score(y_true, y_score)
    gini = 2 * auc - 1
    ks_stat, ks_pval = ks_2samp(
        y_score[y_true == 1], y_score[y_true == 0]
    )
    return {
        "AUC": round(auc, 4),
        "Gini": round(gini, 4),
        "KS": round(ks_stat, 4),
        "KS_pvalue": round(ks_pval, 6),
    }

Calibration Test (Hosmer-Lemeshow)

from scipy.stats import chi2

def hosmer_lemeshow_test(
    y_true: pd.Series, y_pred: pd.Series, groups: int = 10
) -> dict:
    """
    Hosmer-Lemeshow goodness-of-fit test for calibration.
    p-value < 0.05 suggests significant miscalibration.
    """
    data = pd.DataFrame({"y": y_true, "p": y_pred})
    data["bucket"] = pd.qcut(data["p"], groups, duplicates="drop")

    agg = data.groupby("bucket", observed=True).agg(
        n=("y", "count"),
        observed=("y", "sum"),
        expected=("p", "sum"),
    )

    hl_stat = (
        ((agg["observed"] - agg["expected"]) ** 2)
        / (agg["expected"] * (1 - agg["expected"] / agg["n"]))
    ).sum()

    dof = len(agg) - 2
    p_value = 1 - chi2.cdf(hl_stat, dof)

    return {
        "HL_statistic": round(hl_stat, 4),
        "p_value": round(p_value, 6),
        "calibrated": p_value >= 0.05,
    }

SHAP Feature Importance Analysis

import shap
import matplotlib.pyplot as plt

def shap_global_analysis(model, X: pd.DataFrame, output_dir: str = "."):
    """
    Global interpretability via SHAP values.
    Produces summary plot (beeswarm) and bar plot of mean |SHAP|.
    Works with tree-based models (XGBoost, LightGBM, RF) and
    falls back to KernelExplainer for other model types.
    """
    try:
        explainer = shap.TreeExplainer(model)
    except Exception:
        explainer = shap.KernelExplainer(
            model.predict_proba, shap.sample(X, 100)
        )

    shap_values = explainer.shap_values(X)

    # If multi-output, take positive class
    if isinstance(shap_values, list):
        shap_values = shap_values[1]

    # Beeswarm: shows value direction + magnitude per feature
    shap.summary_plot(shap_values, X, show=False)
    plt.tight_layout()
    plt.savefig(f"{output_dir}/shap_beeswarm.png", dpi=150)
    plt.close()

    # Bar: mean absolute SHAP per feature
    shap.summary_plot(shap_values, X, plot_type="bar", show=False)
    plt.tight_layout()
    plt.savefig(f"{output_dir}/shap_importance.png", dpi=150)
    plt.close()

    # Return feature importance ranking
    importance = pd.DataFrame({
        "feature": X.columns,
        "mean_abs_shap": np.abs(shap_values).mean(axis=0),
    }).sort_values("mean_abs_shap", ascending=False)

    return importance


def shap_local_explanation(model, X: pd.DataFrame, idx: int):
    """
    Local interpretability: explain a single prediction.
    Produces a waterfall plot showing how each feature pushed
    the prediction from the base value.
    """
    try:
        explainer = shap.TreeExplainer(model)
    except Exception:
        explainer = shap.KernelExplainer(
            model.predict_proba, shap.sample(X, 100)
        )

    explanation = explainer(X.iloc[[idx]])
    shap.plots.waterfall(explanation[0], show=False)
    plt.tight_layout()
    plt.savefig(f"shap_waterfall_obs_{idx}.png", dpi=150)
    plt.close()

Partial Dependence Plots (PDP)

from sklearn.inspection import PartialDependenceDisplay

def pdp_analysis(
    model,
    X: pd.DataFrame,
    features: list[str],
    output_dir: str = ".",
    grid_resolution: int = 50,
):
    """
    Partial Dependence Plots for top features.
    Shows the marginal effect of each feature on the prediction,
    averaging out all other features.
    
    Use for:
    - Verifying monotonic relationships where expected
    - Detecting non-linear thresholds the model learned
    - Comparing PDP shapes across train vs. OOT for stability
    """
    for feature in features:
        fig, ax = plt.subplots(figsize=(8, 5))
        PartialDependenceDisplay.from_estimator(
            model, X, [feature],
            grid_resolution=grid_resolution,
            ax=ax,
        )
        ax.set_title(f"Partial Dependence - {feature}")
        fig.tight_layout()
        fig.savefig(f"{output_dir}/pdp_{feature}.png", dpi=150)
        plt.close(fig)


def pdp_interaction(
    model,
    X: pd.DataFrame,
    feature_pair: tuple[str, str],
    output_dir: str = ".",
):
    """
    2D Partial Dependence Plot for feature interactions.
    Reveals how two features jointly affect predictions.
    """
    fig, ax = plt.subplots(figsize=(8, 6))
    PartialDependenceDisplay.from_estimator(
        model, X, [feature_pair], ax=ax
    )
    ax.set_title(f"PDP Interaction - {feature_pair[0]} × {feature_pair[1]}")
    fig.tight_layout()
    fig.savefig(
        f"{output_dir}/pdp_interact_{'_'.join(feature_pair)}.png", dpi=150
    )
    plt.close(fig)

Variable Stability Monitor

def variable_stability_report(
    df: pd.DataFrame,
    date_col: str,
    variables: list[str],
    psi_threshold: float = 0.25,
) -> pd.DataFrame:
    """
    Monthly stability report for model features.
    Flags variables exceeding PSI threshold vs. the first observed period.
    """
    periods = sorted(df[date_col].unique())
    baseline = df[df[date_col] == periods[0]]

    results = []
    for var in variables:
        for period in periods[1:]:
            current = df[df[date_col] == period]
            psi = compute_psi(baseline[var], current[var])
            results.append({
                "variable": var,
                "period": period,
                "psi": psi,
                "flag": "🔴" if psi >= psi_threshold else (
                    "🟡" if psi >= 0.10 else "🟢"
                ),
            })

    return pd.DataFrame(results).pivot_table(
        index="variable", columns="period", values="psi"
    ).round(4)

Workflow

Phase 1: Scoping & Documentation Review

1. Collect all methodology documents (construction, data pipeline, monitoring)
2. Review governance artifacts: inventory, approval records, lifecycle tracking
3. Define QA scope, timeline, and materiality thresholds
4. Produce a QA plan with explicit test-by-test mapping

Phase 2: Data & Feature Quality Assurance

1. Reconstruct the modeling population from raw sources
2. Validate target/label definition against documentation
3. Replicate segmentation and test stability
4. Analyze feature distributions, missings, and temporal stability (PSI)
5. Perform bivariate analysis and correlation matrices
6. SHAP global analysis: compute feature importance rankings and beeswarm plots to compare against documented feature rationale
7. PDP analysis: generate Partial Dependence Plots for top features to verify expected directional relationships

Phase 3: Model Deep-Dive

1. Replicate sample partitioning (Train/Validation/Test/OOT)
2. Re-train the model from documented specifications
3. Compare replicated outputs vs. original (parameter deltas, score distributions)
4. Run calibration tests (Hosmer-Lemeshow, Brier score, calibration curves)
5. Compute discrimination / performance metrics across all data splits
6. SHAP local explanations: waterfall plots for edge-case predictions (top/bottom deciles, misclassified records)
7. PDP interactions: 2D plots for top correlated feature pairs to detect learned interaction effects
8. Benchmark against a challenger model
9. Evaluate decision threshold: precision, recall, portfolio / business impact

Phase 4: Reporting & Governance

1. Compile findings with severity ratings and remediation recommendations
2. Quantify business impact of each finding
3. Produce the QA report with executive summary and detailed appendices
4. Present results to governance stakeholders
5. Track remediation actions and deadlines