Individual skill
pho-audit-solver
Solve Public Health Observatory algorithmic transportability audits using an HTTP portal API. Covers release resolution, cohort construction, and a catalog of reusable statistical modules (fixed-effects jackknife, ridge/elastic-net CV, wild cluster bootstrap, grouped conformal, PCA clustering, source perturbation, GMM mediation, and sensitivity surfaces) with exact output formatting against a provided answer template.
experiments/codex_skill_creator_comparison_deepseek_v4_pro_max/reports/skills/task_group_023/fewshot/naive/fewshot_attempt_01/SKILL.md
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- Last checked
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- Oct 29, 2026
- License
- Apache-2.0
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SKILL.mdcore · 16,772 bytesSKILL.mdcore · 16,772 bytesSKILL.mdcore · 16,772 bytesSKILL.mdcore · 16,772 bytesreferences/conformal_inference.mddoc · 2,732 bytesreferences/elastic_net.mddoc · 2,088 bytesreferences/fixed_effects_jackknife.mddoc · 2,009 bytesreferences/gmm_mediation.mddoc · 3,377 bytesreferences/linear_algebra.mddoc · 1,688 bytesreferences/pca_clustering.mddoc · 5,055 bytesreferences/portal_api.mddoc · 4,839 bytesreferences/release_and_cohorts.mddoc · 3,425 bytesreferences/ridge_regression.mddoc · 2,643 bytesreferences/sensitivity_surface.mddoc · 1,853 bytesreferences/source_perturbation.mddoc · 4,046 bytesreferences/wild_cluster_bootstrap.mddoc · 3,552 bytes
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# PHO Audit Solver
This skill equips you to solve Public Health Observatory (PHO) algorithmic audit tasks. Every task follows the same core pattern: read a prompt plus `analysis_request.json` and `answer_template.json`, gather evidence from the PHO web portal, run the declared statistical modules, and return one JSON object conforming to the template.
**What this skill covers**
- Navigating the PHO portal API to pull publication records
- Resolving final releases with revision and timestamp precedence
- Building analytic cohorts (complete, balanced, broad, dual-source, machine-learning)
- Implementing every statistical module that appears across the PHO audit family: fixed-effects OLS with jackknife inference, ridge and elastic-net nested cross-validation, PCG32/xorshift wild cluster bootstrap, grouped split conformal calibration, covariance PCA with farthest-first k-means, exhaustive source/time perturbation with Shapley attribution, difference GMM mediation, and partial-R2 sensitivity surfaces
- Formatting output exactly to an answer template, applying controlled decision rules
**What this skill does not cover**