Audit incident learning evidence integrity

What it's for

Turns postmortems from documents into auditable management evidence: leaders can see whether serious incidents actually produced reviewed, traceable and verified learning actions before trusting a reliability narrative.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "as_of_ms": 8640000000,
  "corrective_actions": [
    {
      "completed_at_ms": null,
      "due_at_ms": 9504000000,
      "evidence_verified": true,
      "id": "action-a",
      "mechanism_id": "dependency-timeout",
      "opened_at_ms": 8035200000,
      "postmortem_id": "postmortem-a",
      "state": "open",
      "verification_id": null
    }
  ],
  "incidents": [
    {
      "evidence_verified": true,
      "id": "incident-a",
      "resolved_at_ms": 7862400000,
      "service_id": "checkout",
      "severity": "sev1",
      "source_complete": true,
      "started_at_ms": 7776000000
    }
  ],
  "postmortems": [
    {
      "action_disposition": "actions_recorded",
      "data_cutoff_ms": 7862400000,
      "evidence_verified": true,
      "id": "postmortem-a",
      "incident_id": "incident-a",
      "published_at_ms": 7948800000,
      "reviewed": true,
      "root_cause_id": "dependency-timeout"
    }
  ]
}

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "decision": "accepted",
  "detail_truncated": false,
  "finding": "evidence_ready",
  "gates": {
    "integrity_passed": true,
    "maximum_overdue_action_rate": 0.2,
    "minimum_required_postmortem_coverage": 0.95,
    "minimum_verified_action_rate": 0.95,
    "overdue_action_rate_passed": true,
    "postmortem_coverage_passed": true,
    "verified_action_rate_passed": true
  },
  "integrity_diagnostics": {
    "closed_without_verification": 0,
    "delayed_postmortems": 0,
    "future_action_information": 0,
    "future_incidents": 0,
    "future_postmortem_information": 0,
    "impossible_action_times": 0,
    "impossible_incident_times": 0,
    "impossible_postmortem_times": 0,
    "incomplete_incident_sources": 0,
    "inconsistent_action_states": 0,
    "invalid_dispositions": 0,
    "missing_declared_actions": 0,
    "missing_root_cause": 0,
    "multiple_postmortems": 0,
    "orphan_actions": 0,
    "orphan_postmortems": 0,
    "overdue_actions": 0,
    "prewindow_incidents": 0,
    "unreviewed_postmortems": 0,
    "unverified_actions": 0,
    "unverified_incidents": 0,
    "unverified_postmortems": 0
  },
  "interpretation": "Overdue corrective-action debt is an aggregate operating finding, not an evidence defect by itself. The audit does not attribute individual fault or authorize remediation.",
  "method": "point_in_time_incident_postmortem_corrective_action_lineage_audit",
  "repair_queue": [],
  "summary": {
    "corrective_action_count": 1,
    "open_action_count": 1,
    "overdue_action_rate": 0,

How it works

Statistical audit & measurement — Check whether a number is fit to decide on: coverage, timing, reconciliation, and the gaps a dashboard hides.

1. 1 Freeze the incident window and severity policy, then reconcile every eligible resolved incident to exactly one reviewed, version-consistent postmortem and its declared action disposition.
2. 2 Validate incident, publication, cutoff, action due/completion and verification time order; expose future leakage, orphans, duplicates, incomplete sources, missing causes, unreviewed records, declared-but-absent actions and unverifiable closures.
3. 3 Gate analytical use on postmortem coverage and verified-action evidence while reporting overdue open actions as a separate aggregate operational finding, not an evidence defect or personal score.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "audit the complete pointintime incidenttopostmortemtocorrectiveaction lineage" }
  → finds "audit_incident_learning_evidence_integrity"

gitrevio_capability_describe
  { "capability_id": "audit_incident_learning_evidence_integrity" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "audit_incident_learning_evidence_integrity", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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