# Pho Registered Audit > Solve "Public Health Observatory" (PHO) registered algorithmic-audit tasks: a business prompt plus a formal analysis_request.json (protocol: cohorts, statistical modules, seeds/grids, robustness gates, decision rule) and a strict answer_template.json (output contract), backed by a read-only public-health data portal. Use this whenever a task hands you an analysis_request.json / answer_template.json pair that references a health-observatory Web portal and asks for one JSON object of resolved releases, cohorts, multi-module diagnostics, and a controlled decision. Covers state / county / country geographies and the recurring six-module audit anatomy (fixed-effects & jackknife, nested ridge / elastic-net CV, wild cluster bootstrap-t, grouped conformal, trajectory PCA + k-means + ARI, source/year perturbation & Shapley). - Skill: `prism-shadow/pho-registered-audit-4` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add prism-shadow/pho-registered-audit-4` - Raw SKILL.md: https://api.skillmd.com/api/skills/prism-shadow/pho-registered-audit-4/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: prism-shadow (https://skillmd.com/u/prism-shadow) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/prism-shadow/pho-registered-audit-4 --- # Public Health Observatory — registered algorithmic-audit playbook ## 1. Recognise the task family Every task in this family gives you three inputs (names may vary slightly): - **`prompt.txt`** — business framing (who is deciding what). It is context only; the *authoritative* spec is the JSON payloads. - **`payloads/analysis_request.json`** — the registered protocol. It declares the geography scope, analysis years / reference year, outcome / exposure / adjustment variables, evidence & cohort definitions, an ordered set of **audit modules** (each with its exact method name, cohort, feature order, seeds, grids, checkpoints), the **robustness gates**, and the **decision rule**. - **`payloads/answer_template.json`** — the output contract: required top-level keys, per-field types, array lengths, ordering rules, identifier rules, precision, and cardinality constraints. Your deliverable is **exactly one JSON object** conforming to it, with **no narrative** outside the JSON. Access to the evidence portal is described in the task's own access file (an `environment_access.md` or equivalent that lists the base URL and the allowed read-only routes). **Read that file to learn how to reach the portal in the current environment** — do not assume routes; the portal is read-only and exposes a catalog, geography references, per-domain data tables, a revisions table, a methodology library, and a bulk CSV/table export. Pull the full tables once and compute locally. **Produce the final answer JSON directly.** There is no validation or feedback service to call at solve time; correctness comes from following the protocol exactly. ## 2. The fixed workflow 1. **Parse both payloads completely.** List every required output key and its constraints from `answer_template.json`; list every module, order, seed, grid, and threshold from `analysis_request.json`. These two lists are your checklist. 2. **Extract the portal data.** Load every relevant table in full (geography references, the domain health/socioeconomic/indicator tables, and the revisions table). Keep FIPS / ISO3 identifiers as **text** (leading zeros are meaningful). 3. **Resolve final releases** independently per cell — see `references/resolution_and_cohorts.md`. This is the foundation; get it exactly right before any modelling. 4. **Build the declared cohorts** (complete-case, primary/reference-year, balanced panel, ML/augmented, strict dual-source, broad reference). Report their counts and sorted code lists. 5. **Run each audit module** in the declared order using the method named — see `references/module_playbook.md`. Reproduce seeds, PRNG streams, grids, feature orders, and checkpoints **exactly**. 6. **Evaluate the gates and decision rule** from your computed statistics and emit the classification enum by the declared precedence. 7. **Serialise to the template**: exact keys, orders, lengths, precision, identifier casing, literal required values. Validate structure before returning. ## 3. What to read next (bundled references) - **`references/portal_data_model.md`** — the datasets, their columns, the categorical vocabularies (value types, source types, release/revision/quality flags, reason codes), and the geography facts (regions, the 9 census divisions, RUCC bands). - **`references/resolution_and_cohorts.md`** — the release/revision resolution algorithm, suppression & scale-break (anomaly) handling, country-label reconciliation, and every cohort pattern. - **`references/module_playbook.md`** — one section per recurring statistical module with the estimator, the reproducibility knobs, and the fields it must emit. - **`references/reproducibility_and_output.md`** — ordering, reference categories, transforms, precision/rounding, null policy, decision-rule mechanics, output discipline, and a pre-submit self-check. - **`scripts/audit_toolkit.py`** — dependency-light, endpoint-free helper functions (final-revision resolution, cohort intersection, XORSHIFT32 / PCG32 generators, deterministic farthest-first k-means, adjusted Rand index, jackknife SE, WLS + HC3, nearest-rank conformal quantile, plus-one bootstrap p-value, exact Shapley). Adapt them to the exact method names in the request. ## 4. The five habits that decide correctness 1. **Resolution before modelling.** Almost every downstream number depends on picking the right FINAL record (highest applied revision), honouring suppression, and never zero-filling. A single mis-resolved cell shifts a whole cohort. 2. **Order is data.** The request fixes feature/coefficient/division/state/checkpoint/ source-group orders and the template forbids re-sorting aligned arrays. Carry these orders literally; align cross-module arrays (e.g. a shared `state_order`). 3. **Reproduce, don't approximate, the stochastic pieces.** Bootstrap checkpoints, GMM steps, k-means initialisation, and Shapley enumeration follow the *named* algorithm with the *given* seed/stream. Match the algorithm exactly or the reproducibility fields will not match. 4. **Compute unrounded; round only on output.** Round only reported non-integers to the declared decimals; keep integers and booleans as natural JSON types; use `null` only when a statistic is mathematically undefined (never `NaN`/`Infinity`/zero). 5. **Sanity-check counts.** Region membership is fixed (Northeast 9, Midwest 12, South 17, West 13 jurisdictions incl. DC = 51). Cohort state counts should be consistent with the region scope; DC is included in "50 states + DC".