# Asteria Fleet Dq Certification > Use when a task asks you to audit, reconcile, or certify data from the "Asteria Fleet Data Quality Hub" (a read-only HTTP API given via environment_access.md) and return a single strict JSON object that matches a supplied answer_template.json for a population defined in a case_scope.json. Covers the whole Asteria family: partner/contact master certification, field-service contact-readiness, fuel/freight transaction normalization & accrual close, and maintenance-log integrity. Trigger cues: "Asteria Fleet Data Quality Hub", case_scope.json + answer_template.json payloads, a + Bearer token, "reconcile overlapping source records as of the business cutoff", authoritative source snapshot, quarantine, canonical entity, duplicate cluster, normalized totals, focus clusters / focus people / focus assets, opaque control codes (IC-/OR-/FP-/RB-/SB-/LD-/MS-/HR-), certification / reconciliation / close status + next action. - Skill: `prism-shadow/asteria-fleet-dq-certification-2` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add prism-shadow/asteria-fleet-dq-certification-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/prism-shadow/asteria-fleet-dq-certification-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/asteria-fleet-dq-certification-2 --- # Asteria Fleet Data-Quality Hub — reconciliation & certification ## What this family of task is Every task in this family gives you the same three things and asks for one JSON answer: 1. **`prompt.txt`** — the business framing and the narrative rules (which endpoints matter, what counts as an exception, tie-break wording, "do not include quarantined rows in totals", etc.). **Read it as normative** — it frequently states the decisive rule (e.g. "a channel is ready only when consent is granted", "quarantined charges do not enter normalized totals"). 2. **`payloads/case_scope.json`** — the scope: `collection_id`, a business **cutoff / `as_of` / business_period**, the **focus / anchor / watchlist** items you must decide individually, ranking limits & sort/tie-break policy, and any **thresholds / gates** and **status→action map**. 3. **`payloads/answer_template.json`** — the **output contract**. It is either a JSON Schema (draft 2020-12) *or* a custom `field_contract` object. It is the single source of truth for the shape, required keys, `additionalProperties: false`, allowed **enum values** (including every allowed control-code value), array lengths, ordering, uniqueness, and numeric precision. Runtime connection details (base URL, `AUTHORIZATION: Bearer …`, and the exact list of permitted endpoints) come **only** from `environment_access.md`. The job is always: **discover → select authoritative source → page all rows → reconcile duplicates → classify validity / quarantine → normalize → compute metrics, focus decisions, rollups & rankings → assign control codes → decide status & action → emit one strict JSON object with no prose.** The concrete numbers, IDs, and code values differ per task — **never carry a value from one task into another**. Derive everything from the live hub for the scope you were given. ## Operating rules (do these in order) 1. **Read all three inputs before calling anything.** Extract from `case_scope.json`: the collection id, the cutoff/`as_of`/period, every focus / anchor / watchlist / control-case id (with its seed/evidence row ids and the *exact* order they must be reported in), ranking limit + primary sort + tie breaks, thresholds, gate conditions, and the status→action map. Then read `answer_template.json` and list every required key, enum set, array length/`minItems`/`maxItems`, ordering rule, uniqueness rule, and rounding precision. Build your output skeleton from that list. 2. **Connect read-only.** Take the base URL and bearer token from `environment_access.md`; send the `AUTHORIZATION` header on every request. Use **only** the endpoints listed there. This hub is read-only — never attempt a write; `POST /api/query` is a read/query interface, not a mutation. See `references/hub-access.md`. 3. **Discover the model.** `GET /api/catalog/collections` (confirm the scoped collection) and `GET /api/catalog/schema` (learn field names, types, which field is the *business date*, which fields are stable IDs, how snapshots and the query interface are shaped, and whether a code/enum dictionary is exposed). Do not assume field names — read them. 4. **Select the authoritative snapshot.** `GET /api/source-snapshots`. Pick the authoritative snapshot for the collection as of the cutoff/`as_of`: prefer `CERTIFIED` over `PROVISIONAL` over `STALE`, and among those the latest snapshot effective **≤** the cutoff. Report its id (and status where the contract asks). Overlapping logical records are resolved in its favour (details in `references/reconciliation.md`). 5. **Fetch the full population, paginated.** The collection is larger than one page — loop until every page is retrieved (follow the cursor / page param; `POST /api/query` may aggregate or page more efficiently). Filter to the scoped collection and to rows whose **business date ≤ cutoff** (use `as_of` / `business_period` as the contract dictates). Also fetch the reference tables you need: `/api/reference/aliases`, `/api/reference/conversions`, `/api/reference/fx`. Confirm your row counts against a `POST /api/query` count when possible. 6. **Reconcile duplicates → logical entities.** Use the right mode: - *Stable-ID reconciliation* (transactions, maintenance events, charges): the same stable id appearing in multiple snapshots is one logical record; keep the occurrence from the authoritative snapshot. `duplicate_raw_count = raw_rows − logical_records`. - *Entity resolution / clustering* (contacts, people, partners): cluster rows that share strong normalized identifiers (email, phone digits, name+key), pick a survivor/master, and set each canonical field by **field-level source-system precedence**. `canonical_entity_count` **includes** quarantined entities. See `references/reconciliation.md`. 7. **Classify validity / quarantine.** Apply the quarantine conditions for the sub-family (no usable contact channel; unrecognized/ambiguous category; nonpositive/invalid quantity, weight, or distance; missing/invalid timestamp; invalid odometer; negative/extreme labor; …). **A category/class *mismatch* is still valid and stays in normalized totals; only *quarantined* rows are excluded.** Keep these two buckets strictly separate. 8. **Normalize.** Map free-text → canonical category via aliases (exactly one match required; 0 → unrecognized, >1 → ambiguous — both quarantine). Convert units to the canonical unit via conversions and currency to the base currency via fx, using the factor/rate applicable to each record's **business date**. Normalize contact fields (NFKC-trim-lowercase email, digits-only phone, Unicode-preserving name). Carry full precision; round **only** at final aggregation to the contract's decimals. 9. **Compute the reported artefacts** exactly as the contract names them: summary counts, per-focus/anchor decisions (membership, survivor/master, canonical values, source systems), quarantine/mismatch id sets, readiness / channel partitions, region/depot rollups (one row per represented value), and rankings (apply the primary sort, then the listed tie-breaks, then the limit). 10. **Assign control codes.** The opaque codes (IC-/OR-/FP-/RB-/SB-/LD-/MS-/HR-) have their expansions withheld — infer each family's meaning from the data and apply it **consistently** to every case in that family. Only ever emit values from the current template's enum. See the inference procedure in `references/reconciliation.md`. 11. **Decide status & action.** Compute `quarantine_rate = quarantine_count / canonical_entity_count`, rounded to the contract's decimals. Apply `status_thresholds` from the case scope (PASS ≤ pass_max; PASS_WITH_EXCEPTIONS ≤ pass_with_exceptions_max; else HOLD). **Hard gates override thresholds** — if a gate condition holds (e.g. odometer regression present), force the gate status/action. Map status→action via the case scope's map, or the standard PASS→RELEASE / PASS_WITH_EXCEPTIONS→REVIEW_EXCEPTIONS / HOLD→BLOCK_AND_REMEDIATE. See `references/output-contract.md`. 12. **Emit one JSON object, validate, stop.** Output *only* the JSON — no Markdown, no commentary. Run the pre-submit checklist in `references/output-contract.md` (keys, enums, lengths, ordering, uniqueness, rounding, `additionalProperties:false`) before you finish. ## Determinism Everything must be reproducible from the hub state as of the cutoff. When a rule is genuinely ambiguous, resolve it from `prompt.txt` first, then the schema, then the most conservative reading — and apply the same choice everywhere. Do not introduce randomness or wall-clock time into any decision. ## References - `references/hub-access.md` — endpoints, auth, snapshots, pagination, the query interface. - `references/reconciliation.md` — dedup modes, validity/quarantine per sub-family, normalization (aliases/conversions/fx), canonical-field precedence, readiness logic, and the control-code inference procedure. - `references/output-contract.md` — count definitions, ordering/uniqueness, rounding, thresholds/gates → status/action, and the pre-submit checklist.