Estimate budget contingency reserve

What it's for

Gives CTOs, CFOs, product owners, and investors a defensible answer to how much delivery contingency is supported and which aggregate cost exposures create or offset the joint tail.

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.

{
  "confidence_level": 0.8,
  "cost_items": [
    {
      "budgeted_cost": 100,
      "cost_scenarios": [
        100,
        120,
        150
      ],
      "id": "platform"
    },
    {
      "budgeted_cost": 50,
      "cost_scenarios": [
        50,
        40,
        70
      ],
      "id": "migration"
    }
  ],
  "current_contingency_reserve": 20,
  "probability_ambiguity_radius": 0.1,
  "scenarios": [
    {
      "id": "base",
      "probability": 0.5
    },
    {
      "id": "pressure",
      "probability": 0.3
    },
    {
      "id": "stress",
      "probability": 0.2
    }
  ],
  "tail_probability": 0.2
}

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.

{
  "calibration": {
    "confidence_level": 0.8,
    "probability_ambiguity_radius": 0.1,
    "robust_quantile_uses_only_represented_scenarios": true,
    "tail_probability": 0.2
  },
  "cost_item_tail_contributions": [
    {
      "budgeted_cost": 100,
      "cost_item_id": "platform",
      "expected_cost": 116,
      "expected_cost_variance_to_budget": 16,
      "robust_tail_overrun_contribution": 50
    },
    {
      "budgeted_cost": 50,
      "cost_item_id": "migration",
      "expected_cost": 51,
      "expected_cost_variance_to_budget": 1,
      "robust_tail_overrun_contribution": 20
    }
  ],
  "decision": "contingency_reserve_shortfall",
  "guardrails": [
    "Cost scenarios must be one coherent joint distribution; independently sorted item marginals destroy natural offsets and tail dependence.",
    "The total-variation radius is calibrated from later resolved probability error and moves mass only among submitted futures; it cannot protect against an omitted regime.",
    "Tail contribution is finance reconciliation, not blame, causal attribution, or a person, vendor, project-owner, or geography score.",
    "Contingency reserve excludes management reserve for unknown unknowns unless those regimes are explicitly represented and governed."
  ],
  "method": "coherent_total_variation_budget_contingency_reserve_v1",
  "scenario_diagnostics": [
    {
      "nominal_probability": 0.2,
      "portfolio_overrun": 70,
      "robust_tail_weight": 1,
      "scenario_id": "stress",
      "total_cost": 220,
      "worst_case_probability": 0.3
    },
    {
      "nominal_probability": 0.3,
      "portfolio_overrun": 10,
      "robust_tail_weight": 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 finance-owned item budgets and one aligned joint scenario matrix of fully loaded costs; preserve shared shocks and natural offsets instead of sorting item marginals independently.
2. 2 Calculate portfolio overrun, nominal quantile and CVaR, adversarially move the governed total-variation probability mass toward costly represented futures, then recompute the robust reserve, CVaR, and reserve-exhaustion probability.
3. 3 Reconcile robust tail overrun exactly to signed item contributions, compare the required reserve with the current reserve and finance limit, and expose omitted-scenario and management-reserve boundaries.

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": "size an engineering or investment contingency" }
  → finds "estimate_budget_contingency_reserve"

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

gitrevio_capability_run
  { "capability_id": "estimate_budget_contingency_reserve", "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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