Portfolio Env Analytics

Portfolio Environment Analytics

Turn a scoped analytics prompt over the shared portfolio environment into a single JSON object that exactly matches the task's answer_template.json. Three task families share one procedure: portfolio mix, SLA aging/breach, and release readiness.

When to use

• The prompt references <TASK_ENV_BASE_URL> or environment_access.md, and
• asks you to "return a single JSON object matching input/payloads/answer_template.json" (no prose outside the JSON), and
• the work is a portfolio-mix readout, an SLA / reliability / security aging or breach audit, or a release-readiness assessment.

Entry procedure (run in order)

1. Read the contract. Read train_tasks/<task>/input/prompt.txt for scope and deliverables, then train_tasks/<task>/input/payloads/answer_template.json. The template is the contract: its required, additionalProperties:false, const, enum, minItems/maxItems, and description fields encode the exact shape, ordering, and rounding. Never add a forbidden field; never omit a required one; pull every controlled vocabulary (action/rationale codes, ship_decision, category enums) from the template's enums — never invent values.

2. Resolve environment access from environment_access.md — network access comes ONLY from this file. Re-read it each run; do not hardcode the base URL or token. The base URL line (e.g. GDPEVO_ENV_BASE_URL=…) is <TASK_ENV_BASE_URL>; the X-Env-Token value is the auth header. Call only the allowed endpoints listed there. See references/env_access.md for endpoint shapes and the restricted SQL query endpoint.

3. Gather data, filtering by scope. Scope fields come from the prompt: teams, quarter, product_area, scope_id, as-of date, recent-closed window, release_id. Prefer the restricted POST /api/query (SELECT-only SQL over the 7 tables) to filter/aggregate server-side; fall back to the GET list endpoints + client filter. Pull every table the deliverable needs (work_items, mix_targets, sla_policy, releases, milestones, blockers, dependencies).

4. Apply data-quality rules before any counting. See references/data_quality.md. In short: use authoritative status (never mirror_status); ignore legacy_category; treat Duplicate/duplicate_of and Cancelled as non-primary; classify each item into exactly one portfolio category {NewFeature, TechDebt, Reliability, Security} via the conventions in the reference, never via legacy_category. Surface every excluded record in the template's exclusion/cluster fields — never drop a record silently.

5. Compute derived metrics with the template's precision. Percentages → 1 decimal place (percentage points; mix_targets fractions are 0–1, multiply by 100). Rates (breach / sla_breach / readiness) → exactly 3 decimals. Counts are item counts, not story points. Formulas and per-family field maps live in references/metrics_ordering.md and references/task_families.md.

6. Order everything stably. ID lists ascending/lexicographically (or closed_at asc then id asc where the template says so); teams alphabetical; categories in fixed order NewFeature, TechDebt, Reliability, Security; milestones by milestone_id asc; duplicate clusters by primary_id; dependency chains lexicographically by full path. Use each id once.

7. Validate and emit. Run python3 skill/validate_answer.py <template.json> <answer.json> (or any JSON-Schema validator). Fix every violation. Then emit exactly one JSON object — no prose, no markdown fences.

Cross-cutting rules (always apply)

• The template is truth. Match its shape exactly; pull controlled vocabularies from its enums.
• Authoritative fields only. mirror_status and legacy_category are distractors; release truth lives in the release/milestone/blocker/dependency records.
• Primary work is counted; duplicates/cancellations/distractors are reported, not counted.
• One JSON object, matching the template, nothing else.

Supporting files (in this package)

• references/env_access.md — endpoints, query SQL semantics, work_items field list.
• references/data_quality.md — authoritative vs mirror/legacy, primary vs duplicate/cancelled/distractor, category classification convention.
• references/metrics_ordering.md — formulas, rounding, ordering rules.
• references/task_families.md — per-family field checklists (mix / SLA / release).
• validate_answer.py — generic answer-vs-template JSON-Schema validator.