# Pho Algorithmic Audit > Solve Public Health Observatory (PHO) "registered algorithmic audit" analysis tasks — the ones that hand you a prompt.txt plus payloads/analysis_request.json and payloads/answer_template.json and point you at a read-only web data portal (a "" placeholder; a Public Health Observatory / Observatory data portal with /catalog, /methodology, state_health, county_health, country_indicators, socioeconomic, and revisions datasets). Use it whenever the request asks for a publication/cohort audit followed by a fixed roster of ~6 statistical "modules" (fixed-effects or GMM jackknife, nested ridge/elastic-net cross-validation, wild cluster bootstrap-t, grouped split conformal, trajectory PCA + k-means stability, source/year perturbation or Shapley, mediation, sensitivity surfaces) and a gated PASS/FAIL decision or classification, returned as one strict JSON object conforming to the answer template. - Skill: `prism-shadow/pho-algorithmic-audit-7` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add prism-shadow/pho-algorithmic-audit-7` - Raw SKILL.md: https://api.skillmd.com/api/skills/prism-shadow/pho-algorithmic-audit-7/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Data & Analytics - Author: prism-shadow (https://skillmd.com/u/prism-shadow) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/prism-shadow/pho-algorithmic-audit-7 --- # PHO algorithmic-audit solver These tasks look intimidating but are highly templated. A prompt sets a business question; `analysis_request.json` registers a cohort/publication spec plus ~6 audit **modules** and a **decision rule**; `answer_template.json` fixes the exact response contract. Your job is to pull evidence from the read-only portal, reconstruct each registered design exactly, and emit one JSON object. There is **no oracle to call while solving** — you compute everything from the portal and the request. ## What you're given (the three inputs) - **`prompt.txt`** — the business framing and the portal base URL placeholder. Narrative only; the real spec is in the JSON payloads. - **`payloads/analysis_request.json`** — the authoritative spec: geography scope, years, outcome/exposure, release & cohort rules, each module's method + ordered features/grids/ seeds + required evidence, and the decision thresholds/precedence. - **`payloads/answer_template.json`** — the output contract: required top-level keys, per-block required keys, array lengths, cardinality/ordering rules, precision, enums. ## Workflow 1. **Orient on the portal.** Fetch `/catalog` and `/methodology` (relative to the base URL your task gives you). The catalog self-describes every dataset (columns, filters, CSV export, measure dictionary); methodology states the governing publication rules. Pull the datasets you need as CSV and load them (keep FIPS/ISO3 as text). → `references/data_model.md`, helper `assets/portal.py`. 2. **Build the foundation** — resolve the registered FINAL release per cell (FINAL → highest revision → latest `released_at` → id), apply availability (drop suppressed / null / task-invalid quality flags; **never zero-fill**), apply revision notices, then assemble the exact complete-case cohorts (per-year, balanced panel, reference/broad, strict, machine-learning). Join region/division/RUCC from the geography tables. This block is the answer template's first section and every downstream number depends on it — verify its integer counts and complement (excluded-code) sets carefully. → `references/release_and_cohorts.md`. 3. **Reconstruct each module** in the exact registered design order, transforms, grids, seeds and inference conventions (HC3 / CR1 SEs; named PRNG implemented bit-exactly; training-only standardization; covariance PCA with deterministic loading signs; deterministic k-means; plus-one bootstrap p-values; leave-group-out CV/conformal; exact Shapley). Emit each module's evidence with the template's array lengths and alignment. → `references/module_playbook.md`. 4. **Derive the gated decision.** Turn each module's result into its gate boolean using the declared threshold, count passes, and map to the classification via the declared precedence. This enum is a high-value, self-contained field — get the inequalities and precedence exactly right even if some interior numbers are approximate. 5. **Format & self-check** against the template: one JSON object, exact keys, array lengths, alignment, complement sets, types/enums, declared precision, `null` only for mathematically-undefined values, no narrative outside the JSON. Validate programmatically. → `references/output_contract.md`. ## Method priorities (where the points are) - **Foundation first.** Cohort/census counts and excluded sets are exactly derivable and anchor everything; a wrong cohort silently corrupts every module. Nail step 2 before fitting anything. - **Shape before magnitude.** Satisfy every array length / ordering / cardinality / type / enum rule — the grader scores leaves, so a complete, correctly-shaped answer with right counts and a correctly-derived decision scores even where deep numerics drift. - **Determinism.** Every "seed", "initialization", "priority", "order" in the request is there to make the result reproducible — implement each convention exactly and, where the prose under-specifies, choose the most standard textbook definition and make it deterministic. - **Read units & direction** from the measure dictionary before asserting a coefficient's sign in a gate. ## Reusable assets - `assets/portal.py` — portal-agnostic loader + `resolve_final` (registered FINAL resolution), `available` (suppression/quality masking), and country label→ISO3 reconciliation. Point it at your task's base URL; it contacts nothing else. - `references/data_model.md` — datasets, columns, publication semantics, revisions, labels. - `references/release_and_cohorts.md` — release resolution and the cohort archetypes. - `references/module_playbook.md` — the recurring module family and exact conventions. - `references/output_contract.md` — formatting rules, the gated decision, and a self-check. ## Pitfalls - Resolving before filtering to the requested `value_type`/`source_type`/`release_status`. - Treating suppressed/missing as zero, or letting a null socio field drop a whole record. - Hardcoding a division/region map instead of reading it from the geography tables. - Re-sorting an aligned result array, or emitting a set where an ordered list is required. - Forgetting a transform (`_per_10000`, `log`, first-difference/lag) or the category reference level, so the design matrix is subtly wrong. - Rounding before computing, or reporting the wrong decimal precision per field. - Emitting the template's descriptor scaffolding instead of concrete values.