Estimate engineering extreme value risk

What it's for

Ordinary statistics describe the middle. This fits the tail directly, so you can talk about the rare incident or the extreme delay with something better than intuition.

Quantifies the once-per-100 or once-per-1,000 observation event instead of extrapolating severe engineering risk from an average or p90.

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.

{
  "bootstrap_draws": 200,
  "return_period_observations": [
    100,
    1000
  ],
  "seed": 12,
  "series": [
    {
      "id": "incident-duration",
      "values": [
        1.0005003753127737,
        1.0015033834597085,
        1.002509414234171,
        1.0035184828481862,
        1.004530604621176,
        1.0055457949809359,
        1.0065640694646234,
        1.0075854437197567,
        1.0086099335052239,
        1.0096375546923044,
        1.0106683232657028,
        1.011702255324591,
        1.0127393670836666,
        1.0137796748742194,
        1.0148231951452122,
        1.0158699444643726,
        1.0169199395192985,
        1.0179731971185753,
        1.019029734192905,
        1.0200895677962514,
        1.0211527151069941,
        1.0222191934290992,
        1.0232890201933018,
        1.0243622129583028,
        1.0254387894119787,
        1.0265187673726073,
        1.0276021647901046,
        1.0286889997472795,
        1.0297792904610998,
        1.0308730552839767,
        1.03197031270506,
        1.0330710813515527,
        1.0341753799900382,

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": [
    "Observations are stationary enough within the epoch and sufficiently weakly dependent for peaks-over-threshold estimation and row bootstrap.",
    "The selected high threshold is prespecified and leaves enough exceedances; shape stability across nearby thresholds is a diagnostic, not proof of asymptotic convergence.",
    "Method-of-moments generalized-Pareto estimates are compact screening evidence; sparse catastrophic tails require specialist EVT review and exposure-aware declustering.",
    "Return periods count observations, not calendar time, unless the observation arrival process is separately stable and documented."
  ],
  "decision": "heavy_tail_extreme_risk_present",
  "method": "peaks_over_threshold_generalized_pareto_mom_bootstrap_v1",
  "portfolio": {
    "heavy_tail_series": 1,
    "maximum_shape": 0.313011,
    "series_analyzed": 1
  },
  "sample": {
    "bootstrap_draws": 200,
    "confidence_level": 0.95,
    "details_returned": 1,
    "details_truncated": false,
    "total_values": 500
  },
  "series": [
    {
      "classification": "heavy_or_unbounded_tail",
      "exceedances": 50,
      "return_levels": [
        {
          "estimate": 9.8807,
          "interval": [
            6.9748,
            13.307
          ],
          "return_period_observations": 100
        },
        {
          "estimate": 23.719,
          "interval": [
            11.7734,
            33.5141
          ],
          "return_period_observations": 1000
        }
      ],
      "scale": 1.995234,

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 Estimate rare incident, delay, loss, or pipeline-duration return levels with peaks-over-threshold generalized-Pareto fitting, bootstrap uncertainty, and threshold-stability diagnostics.
2. 2 Evaluate the method-specific diagnostics and gates returned by the function, then abstain unless the declared decision clears them under locally governed thresholds.

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": "estimate rare incident delay loss or" }
  → finds "estimate_engineering_extreme_value_risk"

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

gitrevio_capability_run
  { "capability_id": "estimate_engineering_extreme_value_risk", "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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