Qq Email Ground Truth

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.