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transcription/.github/skills/evidence-provenance-auditor/skill.md
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Jim Lancaster 30fcef3892
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gpt-5.3-codex review Phase 3
2026-08-19 20:50:21 -05:00

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name, description
name description
evidence-provenance-auditor Deterministic reviewer for transcription evidence/provenance guarantees. Use when changes touch execution attempts, source storage, retries, transport evidence, artifact provenance, or evidence exports.

Evidence & Provenance Auditor

Perform focused, deterministic audits of evidence integrity and provenance behavior.

When to Use

  • Reviewing changes in:
    • src/transcription/services/sources.py
    • src/transcription/services/store.py
    • src/transcription/services/workflows.py
    • src/transcription/services/evidence.py
    • src/transcription/db/models.py
  • Auditing evidence exports/imports or evidence-display behavior.
  • Verifying no drift from canonical provenance invariants.

Normative References (must be used)

  1. docs/invariant/ai_evidence_and_provenance.md
  2. docs/ver4/schema_v4.md
  3. docs/ver4/requirements_v4.md
  4. docs/ver4/error_handling_v4.md

Deterministic Pass/Fail Checks

A. Append-only history

  • Every provider call results in a new ExecutionAttempt.
  • Runtime paths do not mutate historical attempts to represent new outcomes.
  • Retry behavior appends attempts rather than rewriting prior rows.

B. Projection vs authority separation

  • Source.raw_transcription and preferred pointers are mutable projection surfaces.
  • Attempt rows remain authoritative historical evidence.
  • Candidate promotion updates projection pointers without rewriting history.

C. Transport evidence semantics

  • Transport evidence is correctly labeled as application-boundary capture.
  • SDK snapshots/normalized metadata are not mislabeled as native upstream payload.
  • No-response timeout/network states are explicit.

D. Canonical source identity

  • Canonical stored bytes/hash/size are internally consistent.
  • If ingest normalization is applied, code/docs consistently represent resulting canonical identity.
  • Post-ingest derivatives do not overwrite canonical source bytes.

E. Secret safety

  • No credentials/auth headers/cookies/unrestricted headers persisted.
  • Header persistence uses explicit allowlist semantics.

F. Route/path safety

  • Print/export source access is record-validated.
  • UI/media path construction does not expose local filesystem paths.

G. Schema/docs alignment

  • Evidence-related model fields and semantics align with canonical docs.
  • Evidence model changes require same-change doc updates.

H. Canonical authority boundaries

  • Active guidance resolves against docs/ver4/* and current instruction files.

Review Workflow

  1. Read normative references first.
  2. Inspect model + service + workflow write paths.
  3. Inspect evidence read/display/export paths.
  4. Report high-confidence findings with concrete path/line evidence.
  5. Classify each finding by invariant family (A-H).

Output Format

Use this structure:

  • Verdict by invariant family (A-H)
  • Findings with Location, Observed Behavior, Risk, Recommended Fix
  • Drift table (Doc claim vs Code reality vs Action)
  • Regression guards needed