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--- name: qq-email-ground-truth description: Build, rebuild, audit, and compare QQ mailbox invoice ground truth datasets for this repository using the existing truth-building scripts and evidence artifacts. Use when the task is to generate a QQ mailbox ground truth set, validate QQ batch output against truth, review QQ mailbox invoice evidence, or investigate QQ-specific invoice extraction pitfalls in this project. --- # QQ Email Ground Truth Use this project-level skill for QQ mailbox ground truth work in this repository. Read `references/workflow.md` and `references/pitfalls.md` first. Read `references/case-study-qq-20260201-20260311.md` and `references/project-artifacts.md` when you need a validated example, current artifact paths, or prior evidence. ## Hard Boundaries - Treat this as a project-local skill, not a global mailbox skill. - Treat the current case-study counts, type distribution, and mail IDs as one validated example only. Do not reuse them as fixed thresholds for future QQ runs. - Treat the currently known reimbursable types as a reference range, not a closed list. If a new type, provider, attachment pattern, or procurement scenario appears, send it to review instead of auto-excluding it. - Reuse `build_truth_dataset.py` and `audit_email_truth.py`. Do not create a parallel truth-building flow unless the user explicitly asks for one. ## Required Workflow 1. Confirm the mailbox account, mailbox folder, and time window. 2. Follow `references/workflow.md` for the build and validation sequence. 3. Use `references/pitfalls.md` as the default debug checklist when counts or fields look wrong. 4. Use `references/case-study-qq-20260201-20260311.md` only as a worked example and evidence sample. 5. Use `references/project-artifacts.md` to find the current canonical manifests, reports, and diagnostics. ## Output Expectations - Prefer `truth_manifest.json` for machine comparison. - Prefer `ground_truth_report.md` for human review. - Require `pending_review_count = 0` before calling a dataset final. - Keep excluded-email and excluded-document audit trails instead of silently dropping evidence.