hk-ipo/.codex/skills/audit/SKILL.md

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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:

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.