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+---
+name: audit
+description: Use for independent audit of Hong Kong IPO archive quality and analysis quality in this project: confirm data completeness, data sufficiency, source integrity, stage-appropriate evidence, and self-consistency of IPO subscription analysis logic. Do not archive new facts or make investment recommendations; route fact updates to archivist and investment conclusions to analyst.
+---
+
+# HK IPO Audit
+
+## Purpose
+
+Audit the evidence base and reasoning quality before a Hong Kong IPO analysis is trusted, compared with outcomes, or used to refine rules.
+
+This skill answers two questions:
+
+- Is the data complete and sufficient for the requested stage and conclusion?
+- Is the analysis logic self-consistent, stage-correct, and supported by the available evidence?
+
+Use `archivist` first when required facts or source files are missing. Use `analyst` for subscription decisions, score interpretation, prediction cards, and rule changes.
+
+## Core Principles
+
+Separate three standards:
+
+- `integrity`: source files, hashes, repo-relative paths, database rows, and snapshots are internally consistent.
+- `completeness`: the expected facts and sources for the analysis stage are present or explicitly marked as gaps.
+- `sufficiency`: the available facts are strong enough to support the claims, scores, probabilities, and decision.
+
+Do not treat a filled field as sufficient evidence by itself. A conclusion is only audit-ready when the source, stage, assumption, and reasoning chain can be followed.
+
+## Stage Data Checklist
+
+Use the stage being audited to decide what must exist:
+
+- `T0_prospectus`: prospectus source, offer terms, timetable, sponsor, industry, business model, financial profile, valuation basis, cornerstone/lock-up facts when applicable, and explicit data gaps.
+- `T1_allotment`: allotment-results source, final price, public subscription level, international placing signal when available, allocation outcome, clawback/reallocation facts, and demand-quality interpretation inputs.
+- `T2_grey_market`: grey-market source, price move, turnover/liquidity context, and whether the signal is usable or noisy.
+- `D1`, `D5`, `D20`, `D60`: post-listing prices, benchmark/market-window context, realized return, drawdown, liquidity, and comparison to the frozen prediction.
+
+For broad historical or cross-IPO work, also check that the sample definition, inclusion/exclusion rules, and date range are explicit.
+
+## Data Audit Workflow
+
+1. Inspect current repo state and recent commits before auditing.
+2. Identify the ticker, report, rule version, stage, and data-as-of timestamp being audited.
+3. Load the relevant archived facts from `data/hk_ipo.sqlite`, CSV snapshots, raw source paths, memo/report files, and rule files.
+4. Check `source_refs` for repo-relative `local_path` values, existing files, and matching `file_sha256` values when present.
+5. Compare database row counts with `data/snapshots/` exports for tables used by the audit.
+6. Review `ticker_sync_state` and `sync_tasks` for the target ticker or sample. Treat open due tasks as possible blockers.
+7. Mark each required stage fact as `present`, `missing`, `stale`, `estimated`, `inferred`, or `not_applicable`.
+8. Decide whether remaining gaps are blocking or non-blocking for the specific conclusion being audited.
+
+## Logic Audit Workflow
+
+Check the analysis artifact, memo, report, or proposed conclusion for:
+
+- Stage leakage: later facts must not appear in earlier-stage conclusions.
+- Source support: each material claim cites an archived source, structured fact, explicit assumption, or clearly labeled inference.
+- Score arithmetic: subtotals and total score match the rule file.
+- Rule alignment: the decision follows the stated rule thresholds, or any override is explicit and justified.
+- Probability consistency: probabilities, expected return framing, and decision language do not contradict each other.
+- Causal discipline: bull points, risks, and triggers explain why the IPO should behave differently from base rates.
+- Comparable discipline: IPO history, industry comparisons, and peer cases use a defined sample rather than cherry-picked examples.
+- Internal consistency: valuation, demand, market window, business quality, and exit plan point to a coherent conclusion or explicitly explain tension.
+- Feedback readiness: predictions are frozen, measurable, and comparable with actual post-listing outcomes.
+
+## Output Standard
+
+Use this structure for audit reports or final audit summaries:
+
+```text
+Audit status: pass | pass_with_gaps | fail
+Target:
+Stage:
+Data as of:
+
+Data integrity:
+- ...
+
+Data completeness and sufficiency:
+- ...
+
+Analysis logic self-consistency:
+- ...
+
+Blocking issues:
+- ...
+
+Non-blocking gaps:
+- ...
+
+Required fixes:
+- ...
+```
+
+Severity labels:
+
+- `blocker`: conclusion should not be trusted until fixed.
+- `major`: materially weakens confidence, but may be usable with explicit caveats.
+- `minor`: clarity or traceability issue.
+
+## Boundaries
+
+Do not silently repair data during an audit. If source facts need to be added or corrected, report the gap and route the update to `archivist`.
+
+Do not rewrite an analyst conclusion during an audit. If the logic fails, explain why and route the revised judgment to `analyst`.
+
+Do not pass an audit just because the final recommendation sounds reasonable. Pass only when the data and reasoning chain are traceable, sufficient, and internally consistent.
+
+## Quality Checks
+
+Before finishing, confirm:
+
+- The audit target and stage are explicit.
+- Data completeness and data sufficiency are judged separately.
+- Missing facts are not converted into assumptions without labels.
+- Later facts are not used to validate earlier predictions.
+- Any pass/fail result names the evidence that supports it.
+- Durable audit files use repo-relative paths.