Audit executive technology reporting integrity
Audit a frozen executive technology pack for complete metric/risk scope, point-in-time source and definition lineage, numerical reconciliation, supported narrative direction, independent review and evidence coverage.
What it's for
Makes a Board Quarterly or investor technology pack inspectable: every headline metric and risk narrative traces to the frozen source, effective definition and independently reviewed arithmetic.
What you give it
Every tool is a pure function: it computes from what you pass it and reads nothing on its own. Inputs split into evidence you supply from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.
| Field | Type | Role | Required |
|---|---|---|---|
| as_of_ms | number ≥ 0 | Your calibration | Yes |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| metric_definitions | array of objects (8 fields) | Evidence | Yes |
| metric_observations | array of objects (13 fields) | Evidence | Yes |
| minimum_verified_evidence_fraction | number ≥ 0, ≤ 1 | Your calibration | Optional |
| narrative_claims | array of objects (8 fields) | Evidence | Yes |
| numeric_tolerance | number ≥ 0 | Your calibration | Optional |
| reporting_cycles | array of objects (11 fields) | Evidence | Yes |
| risk_disclosures | array of objects (11 fields) | Evidence | Yes |
Each metric_observations
record
| Field | Type | Required |
|---|---|---|
| cycle_id | string (non-empty) | Yes |
| definition_hash | string (non-empty) | Yes |
| evidence_verified | boolean | Yes |
| id | string (non-empty) | Yes |
| metric_id | string (non-empty) | Yes |
| observed_at_ms | number (≥ 0) | Yes |
| period_end_ms | number (≥ 0) | Yes |
| period_start_ms | number (≥ 0) | Yes |
| recomputed_comparison_value | number | Yes |
| recomputed_current_value | number | Yes |
| reported_change | number | Yes |
| reported_current_value | number | Yes |
| source_snapshot_hash | string (non-empty) | Yes |
{
"as_of_ms": 12000,
"metric_definitions": [
{
"definition_hash": "sha256:delivery-v2",
"direction": "higher_better",
"effective_from_ms": 0,
"effective_until_ms": null,
"evidence_verified": true,
"id": "delivery",
"practical_change_threshold": 1,
"target_value": null
}
],
"metric_observations": [
{
"cycle_id": "board-q3",
"definition_hash": "sha256:delivery-v2",
"evidence_verified": true,
"id": "delivery-q3",
"metric_id": "delivery",
"observed_at_ms": 9500,
"period_end_ms": 9000,
"period_start_ms": 7000,
"recomputed_comparison_value": 10,
"recomputed_current_value": 12,
"reported_change": 2,
"reported_current_value": 12,
"source_snapshot_hash": "sha256:board-q3"
}
],
"narrative_claims": [
{
"assertion": "improved",
"cycle_id": "board-q3",
"evidence_verified": true,
"id": "delivery-claim",
"independently_reviewed": true,
"management_assumption": false,
"subject_id": "delivery",
"subject_type": "metric"
},
{
"assertion": "risk_reduced", Truncated for display — the full payload is 88 lines.
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.
{
"assumptions": [
"The board or executive owner declares the complete metric and risk scope before the cutoff; metric definitions, source snapshots, claims and review evidence are immutable and point-in-time.",
"Narrative consistency is a mechanical reconciliation to governed direction, target and materiality rules; it does not infer causality, intent or strategic merit."
],
"cycle_diagnostics": [
{
"cycle_id": "board-q3",
"failed_gates": [],
"integrity_pass": true,
"reporting_cutoff_at_ms": 10000,
"required_risks": 1,
"scoped_metrics": 1
}
],
"decision": "executive_technology_pack_ready_for_governed_review",
"failed_gates": [],
"limitations": [
"Passing establishes reporting-pack evidence integrity, not the truth of an omitted universe, audit assurance, securities-law compliance, fiduciary adequacy or authority to publish.",
"The function must not infer deception, employee performance, legal fault or investor suitability from a failed reconciliation."
],
"method": "point_in_time_executive_reporting_reconciliation_v1",
"reporting_integrity_ready": true,
"summary": {
"cycles_passing": 1,
"minimum_verified_evidence_fraction": 0.95,
"reporting_cycles": 1,
"required_risks": 1,
"scoped_metrics": 1,
"verified_evidence_fraction": 1
},
"truncated_detail_rows": 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 Freeze each reporting cutoff, source snapshot, complete metric/risk scope, effective metric definitions and narrative ledger before inspecting results.
- 2 Recompute every reported level and change, reconcile definition and snapshot hashes, reject future evidence, and mechanically compare each narrative to governed direction, target and materiality rules.
- 3 Require every scoped metric and risk, owner, independent review and evidence flag; return explicit repair gates rather than inferring intent or silently accepting omissions.
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.
Assumptions & guardrails
- Metric definitions, weights, aggregate grain, sampling, missingness, dependence, and comparison windows correspond to the management claim being audited.
- The board or executive owner declared the complete scope before cutoff and retained immutable metric definitions, source snapshots, observations, risk exposures, claims and review provenance.
- Association, instability, or measurement quality is not a causal effect and must not be converted directly into an individual employment decision.
- Passing establishes pack-level evidence integrity—not audit assurance, securities-law compliance, fiduciary adequacy, causal truth, deception, employee performance or authority to publish.
Minimum evidence
- reporting_cycles: required and organization-defined
- metric_definitions: required and organization-defined
- metric_observations: required and organization-defined
- risk_disclosures: required and organization-defined
- narrative_claims: required and organization-defined
- as_of_ms: required and organization-defined
How to validate it
Validate on future periods or held-out aggregate units, compare with a simple baseline, and require stability across plausible metric definitions and decision thresholds.
Calibrating it to your org
Same for everyone
The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.
Specific to you
- point-in-time cycle-metric and cycle-risk projection joined to effective metric definitions, canonical source snapshots, independently recomputed values, risk-register exposures and one claim per scoped subject without future evidence or omitted rows
- reporting audience and cutoff, complete metric/risk perimeter, definition version and direction, target and practical-change threshold, source snapshot, risk materiality, claim vocabulary, independent review, evidence threshold and publication authority
Calibration workflow
- 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
- 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 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
- 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 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
- 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 20 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 a frozen executive technology pack" }
→ finds "audit_executive_technology_reporting_integrity"
gitrevio_capability_describe
{ "capability_id": "audit_executive_technology_reporting_integrity" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "audit_executive_technology_reporting_integrity", "arguments": { ... } }
→ returns the result shown above Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.