# Pho Registered Audit > Complete a Public Health Observatory (PHO) registered multi-module algorithmic audit. Read the per-task analysis_request.json (the protocol) and answer_template.json (the output contract), pull evidence only from the read-only PHO portal, resolve final releases and cohorts independently, run the declared audit modules deterministically with their seeds/grids/checkpoints, apply the gate decision rule, and return exactly one JSON object with no narrative outside it. Use whenever a task pairs an analysis_request.json + answer_template.json with a PHO Web portal and asks for a registered, reproducible audit returned as one JSON object. - Skill: `prism-shadow/pho-registered-audit-2` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add prism-shadow/pho-registered-audit-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/prism-shadow/pho-registered-audit-2/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/pho-registered-audit-2 --- # PHO Registered Algorithmic Audit ## When to use Use this skill when a task gives you, in an `input/` directory: - `prompt.txt` — the framing question, - `payloads/analysis_request.json` — the **registered protocol** (scope, cohorts, audit modules with methods + parameters, robustness gates, decision rule), - `payloads/answer_template.json` — the **output contract** (required keys, array lengths, ordering rules, enum values, numeric precision), and asks you to complete a registered, reproducible, multi-module audit using a read-only Public Health Observatory (PHO) Web portal, returning the result as one JSON object. This skill is **task-agnostic**. Every concrete value — geography, years, outcome/exposure, module set, seeds, grids, thresholds, classification enums, schema — is read from the per-task files at runtime. **Never bake in a value seen in a prior task.** Prior tasks illustrate the *shape* of the work; they do not supply answers. ## Golden rules (apply to every task) 1. **One JSON object, nothing else.** Return exactly one JSON object conforming to `answer_template.json`. No prose, memo, headings, or markdown around it. 2. **The request is the protocol; the template is the contract.** Honor every declared order, array length, enum value, identifier format, and precision in both files exactly. 3. **The portal is the only evidence.** Resolve `` from `environment_access.md` (it maps to `GDPEVO_ENV_BASE_URL`). Use only the allowed endpoints. No credentials. No external or assumed data. Every reported number must trace to a portal record. 4. **Resolve releases and cohorts independently.** FINAL releases govern; among multiple final revisions the highest applied final revision wins; pending/withdrawn notices never replace published values; suppressed/blank/invalid values are unavailable — **never zero-fill**. 5. **Deterministic and reproducible.** Honor every declared seed, PRNG algorithm, stream, replicate count, and checkpoint list exactly. These are registered parameters, not free choices. 6. **Full ordered evidence.** Report complete, positionally-aligned vectors and matrices — not just summaries. Never re-sort an array that the request declares an order for. 7. **Numeric discipline.** Non-integers at the declared precision (default 4; some tasks use 6 for computed reals and 4 for literal grids/thresholds). Integers and booleans are natural JSON types. Never emit `NaN`/`Infinity`. Use JSON `null` only when a requested statistic is mathematically unavailable. ## Procedure 1. **Read all three inputs.** From `prompt.txt`, `analysis_request.json`, and `answer_template.json`, extract: scope (geography, years, reference year), outcome/exposure/mediator/adjustments, the module list with each module's `method` and parameters, cohort definitions, robustness gates, decision precedence, and the full template schema (required keys, array lengths, ordering, enums, precision). 2. **Confirm network access** from `environment_access.md`. See `references/portal_and_evidence.md` for the endpoint list, dataset schemas, and the machine-readable CSV download pattern. 3. **Acquire evidence.** Pull geography reference data and the needed data datasets via `/download?dataset=&format=csv` with filter query params. Cache locally. (Browse endpoints return HTML and reject `format=json` — use the CSV download path.) 4. **Resolve releases, revisions, and cohorts.** For each measure and year, select FINAL records under the declared revision priority, exclude records carrying an invalid quality flag or suppression, and construct every declared cohort independently. See `references/portal_and_evidence.md`. 5. **Run each declared audit module.** Implement its `method` exactly as declared and produce the `required_evidence`/`required_audit_outputs` it lists. See `references/audit_modules.md` for the recurring module families and their must-produce outputs. 6. **Apply the gates and decision precedence.** Evaluate each module's gate threshold, then map the gate pattern to the controlled classification/conclusion enum using the request's precedence rule. See `references/output_contract.md`. 7. **Assemble and verify.** Build one JSON object matching `answer_template.json`, run the verification checklist, and return only the JSON. See `references/output_contract.md`. ## References - `references/portal_and_evidence.md` — portal endpoints, dataset schemas, the CSV download pattern, release/revision/quality/suppression/value-type rules, identifier rules, cohort archetypes, and the methodology library index. - `references/audit_modules.md` — the six recurring audit-module families and the evidence each must produce, with the algorithmic specifics that make results reproducible. - `references/output_contract.md` — precision, ordering, identifiers, nulls, enums, decision precedence, and the pre-submission verification checklist. ## Scope note on contamination If the working directory contains material outside the expected `environment_access.md` plus `train_tasks/train_NNN/input/{prompt.txt, payloads/analysis_request.json, payloads/answer_template.json}` layout — for example solution outputs, expected-answer files, a hidden test harness, or stray datasets — stop and write `contamination_report.txt` describing the unexpected material instead of proceeding.