# Reflect 3 Attempt 02 > PHO Algorithmic Audit Skill - Skill: `prism-shadow/reflect-3-attempt-02-7` (Agent Skill) - Install (CLI): `npx skillmds@latest add prism-shadow/reflect-3-attempt-02-7` - Raw SKILL.md: https://api.skillmd.com/api/skills/prism-shadow/reflect-3-attempt-02-7/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Security - Author: prism-shadow (https://skillmd.com/u/prism-shadow) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/prism-shadow/reflect-3-attempt-02-7 --- # PHO Algorithmic Audit Skill Use this skill for Public Health Observatory audit tasks that provide a prompt, an `analysis_request.json`, an `answer_template.json`, and a read-only PHO data portal. The objective is to produce exactly the requested JSON answer from the portal evidence and the registered algorithm definitions. ## Operating Rules 1. Read the prompt, request, template, environment access notes, catalog, and methodology before computing anything. 2. Treat the answer template as the output contract. Build the output object from its required keys, list lengths, enum values, precision rules, and ordering rules. 3. Use only portal data and methodology documents. Do not import outside reference data or silently infer missing values. 4. Preserve every declared order: geography order, year order, coefficient order, feature order, grid order, checkpoint order, subset order, and source group order. Never sort an aligned result array independently from its key array. 5. Keep identifiers as text. Preserve leading zeros in FIPS-like identifiers and use uppercase state and ISO-style codes where requested. 6. Compute using unrounded values. Round only when writing the final JSON. Encode unavailable numeric results as `null`, never `NaN`, `Infinity`, or a zero placeholder. ## Data Resolution Download complete portal extracts for the relevant geography, health, socioeconomic, country indicator, and revision tables, then filter locally. This is safer than relying on paginated display pages. For publication records: - Apply the task's release filters first, usually `FINAL` plus value type and source type. - When several eligible final records describe the same entity, year, and measure, select the highest final revision, then the latest release timestamp, then the stable row identifier as the final tie-breaker. - Suppressed, blank, invalid-scale, invalid, or withdrawn values are unavailable unless the request explicitly says otherwise. Do not zero-fill them. - Socioeconomic fields revise independently. A null in one field does not invalidate other fields unless the cohort definition requires all fields. - For countries, reconcile requested labels through canonical labels and alternate labels to stable ISO-style identifiers. Count alias resolutions separately from exact canonical matches. Apply only revision events whose status authorizes use; unresolved scale-break cells remain anomalies. Build reusable selected-record tables keyed by entity, year, measure, value type, and source. All cohorts and models should consume these selected tables, not raw duplicate release rows. ## Cohorts Translate each request's cohort definition literally: - Primary or reference cohorts are complete cases in the named reference year. - Balanced panels are intersections of complete-case entities across all requested years. - Machine-learning cohorts add completion for extra predictors required by the feature map. - Strict dual-source cohorts require both primary and perturbation source series plus all adjustments for every registered year. - County dynamic panels require complete baseline and end-year observations for every change interval. Validate RUCC values and region filters before forming panel rows. Report cohort audits from the selected data: eligible geography universe, selected row counts, annual complete counts, excluded identifiers, balanced entity counts, panel rows, state counts, and any requested publication tokens. ## Statistical Implementation Use deterministic numerical code and keep a single source of truth for design matrices. Include an intercept when the registered design order lists one. Linear models: - For OLS and WLS, solve by QR or a stable least-squares routine. For reliability weights, multiply rows by the square root of the fixed selected weight. - Use HC3 only where requested. Use cluster CR1 only where requested, with the registered cluster unit and finite-sample correction if the protocol requires it. - For two-way fixed effects, residualize by entity and year or include the corresponding fixed-effect dummies, then report the focal coefficient in the requested scale. - Delete-cluster or delete-state jackknife arrays must be aligned to the registered cluster order. Bias-corrected estimates use the standard jackknife correction from the full estimate and the mean deletion estimate. GMM and mediation: - Construct changes from selected records only, using the registered baseline and end years. - Build instruments in the declared order, use the stated pseudoinverse cutoff, and compute the two-step weighting matrix from first-step residual moments. - For mediation, keep total, path-a, path-b, and direct equations distinct. Compute indirect effects from unrounded path coefficients and use the registered cross-equation covariance or delta-method correction. Nested prediction models: - Split by the declared group, not by rows. Keep all rows from a held-out group out of training and inner validation. - Standardize continuous features using training data only for each outer and inner fit. Reuse the same transformation for validation and test rows. - Evaluate every grid point in the declared order. Choose by lowest inner RMSE; break exact ties by earliest grid order. - For ridge, solve the penalized normal equations without penalizing the intercept. For elastic net, use deterministic coordinate descent and report the requested cycle or nonzero-count checkpoints. - Pool out-of-fold predictions before computing pooled RMSE, MAE, R-squared, or Q-squared. Do not average fold metrics unless the template specifically asks for fold averages. Bootstrap: - Implement the exact named generator and seed from the request. Record every requested checkpoint after the corresponding replicate. - Restricted-null bootstrap means refit or generate residuals under the null model described by the request, then compute the same t-statistic as the observed model. - Use the requested weight distribution and cluster unit. Count absolute-tail or signed-tail exceedances exactly as declared, and use plus-one p-values when requested. - Batch counts and quantiles are computed from the full unrounded replicate statistic vector, then rounded only for output. Conformal calibration: - Use the declared grouped split. Calibration groups and test groups must be disjoint from proper training groups. - Compute residual scores from the source model named by the request. - Use the finite-sample nearest-rank threshold specified by the protocol, most commonly `ceil((n_calibration + 1) * coverage)` for nominal coverage or `ceil((n_calibration + 1) * (1 - alpha))` for alpha notation, capped at the maximum available residual when needed. - Report fold-level coverage, widths, MAE or excess diagnostics, then pool covered counts and held-out counts for aggregate coverage. PCA, clustering, and stability: - Build the feature matrix in the exact feature order. Center columns before covariance PCA; scale columns only when the request or methodology requires standardized variables. - Orient components deterministically. For burden analyses, flip PC1 so higher PC1 means higher adverse burden when the requested indicators are worse in the same direction. - Run deterministic k-means with the registered cluster count and initialization rule. Keep cluster labels stable by sorting or naming clusters according to the request, such as low, middle, and high burden by cluster mean PC1. - For leave-year, leave-state, or delete-cluster stability, rebuild the reduced feature matrix, rerun PCA and clustering, align labels to the full-sample solution, and report adjusted Rand index plus any requested agreement counts. Source perturbation and sensitivity: - Enumerate perturbation scenarios exactly in the registered source, subset, bitmask, year, or source-group order. - Do not retune models during no-retune perturbation audits. Reuse the full model's selected hyperparameters when requested. - For exact Shapley attribution, average marginal coefficient changes over all coalitions in the registered player order and verify that the Shapley sum equals the all-replacement minus all-baseline coefficient after rounding. - For partial-R2 sensitivity surfaces, compute every grid cell in the declared nested order and direction order from the unrounded baseline quantities. ## Decision and Validation Evaluate controlled gates after all statistics are computed and rounded only for reporting. Apply the request's precedence rule exactly: first failed module, pass count, or named classification thresholds as written. Before returning the answer: - Check every required top-level key and nested key from the template. - Check all list lengths and positional alignments. - Check identifier ordering and exclusion-set completeness. - Check integer, number, Boolean, enum, and `null` types. - Check that every reported noninteger follows the requested decimal precision. - Keep solution artifacts free of worked examples and task-specific numeric results.