Audit attention fragmentation evidence integrity

What it's for

Makes Gitrevio's meeting-load, focus-time and context-switching claims defensible by proving the consent, identity, schedule and source evidence before showing an aggregate result.

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.

{
  "as_of_ms": 172800000,
  "availability_windows": [
    {
      "contributor_id": "opaque-0",
      "ends_at_ms": 147600000,
      "evidence_verified": true,
      "id": "window-0",
      "starts_at_ms": 118800000
    },
    {
      "contributor_id": "opaque-1",
      "ends_at_ms": 147600000,
      "evidence_verified": true,
      "id": "window-1",
      "starts_at_ms": 118800000
    },
    {
      "contributor_id": "opaque-2",
      "ends_at_ms": 147600000,
      "evidence_verified": true,
      "id": "window-2",
      "starts_at_ms": 118800000
    },
    {
      "contributor_id": "opaque-3",
      "ends_at_ms": 147600000,
      "evidence_verified": true,
      "id": "window-3",
      "starts_at_ms": 118800000
    },
    {
      "contributor_id": "opaque-4",
      "ends_at_ms": 147600000,
      "evidence_verified": true,
      "id": "window-4",
      "starts_at_ms": 118800000
    }
  ],
  "calendar_events": [
    {
      "ends_at_ms": 133200000,
      "evidence_verified": true,
      "id": "meeting-1",

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.

{
  "coverage": {
    "availability_schedule_coverage": 1,
    "calendar_source_coverage": 1,
    "identity_coverage": 1,
    "work_source_coverage": 1
  },
  "decision": "accepted",
  "finding": "evidence_ready",
  "gates": {
    "integrity_passed": true,
    "maximum_context_switches_per_40h": 20,
    "maximum_meeting_share": 0.35,
    "minimum_calendar_source_coverage": 0.9,
    "minimum_cohort_size": 5,
    "minimum_focus_share": 0.3,
    "minimum_identity_coverage": 0.95,
    "minimum_work_source_coverage": 0.8
  },
  "integrity_diagnostics": {
    "duplicate_event_participants": 0,
    "duplicate_provider_ids": 0,
    "future_availability": 0,
    "future_calendar_information": 0,
    "future_work_information": 0,
    "invalid_availability": 0,
    "invalid_calendar_intervals": 0,
    "invalid_work_intervals": 0,
    "orphan_availability": 0,
    "orphan_event_participants": 0,
    "orphan_work_sessions": 0,
    "overlapping_availability": 0,
    "unconsented_contributors": 0,
    "unverified_availability": 0,
    "unverified_calendar_events": 0,
    "unverified_contributors": 0,
    "unverified_work_sessions": 0
  },
  "interpretation": "Meeting share, protected focus, and switching are aggregate operating-system signals. Calendar occupancy is not productivity, and this result must not rank people or infer individual effort.",
  "method": "point_in_time_consent_calendar_availability_work_session_lineage_audit",
  "repair_queue": [],
  "summary": {
    "available_hours": 40,
    "cohort_size": 5,

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 tenant, consent, identity, provider-sync, availability and work-session cutoff; reject duplicate provider events, future records, impossible intervals, orphan participants or sessions, overlapping schedules and unverified evidence.
2. 2 Within each governed availability window, union overlapping confirmed non-all-day meetings, subtract them once, count only free segments above the locally governed focus threshold, and derive project switches only from verified ordered work sessions.
3. 3 Publish cohort-level coverage and attention-pressure findings only after minimum cohort, identity, calendar, work and schedule gates pass; keep evidence defects separate from high meeting load, weak focus or frequent switching.

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 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 consented pointintime contributor identity availability" }
  → finds "audit_attention_fragmentation_evidence_integrity"

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

gitrevio_capability_run
  { "capability_id": "audit_attention_fragmentation_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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