Calculate execution value leakage

What it's for

Makes strategy-execution drift legible in money: leaders and investors can see how much approved value is disappearing through unimplemented scope, late delivery, rework and exceptions—and whether the execution reserve is adequate.

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.

{
  "current_execution_leakage_reserve": 50,
  "decisions": [
    {
      "delay_periods_scenarios": [
        1,
        2
      ],
      "exception_cost_scenarios": [
        1,
        4
      ],
      "full_value_scenarios": [
        100,
        80
      ],
      "id": "product-launch",
      "implementation_fraction_scenarios": [
        0.9,
        0.7
      ],
      "rework_cost_scenarios": [
        3,
        8
      ],
      "value_decay_per_period_scenarios": [
        0.05,
        0.1
      ]
    }
  ],
  "scenarios": [
    {
      "id": "base",
      "probability": 0.7
    },
    {
      "id": "stress",
      "probability": 0.3
    }
  ],
  "tail_probability": 0.3
}

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.

{
  "configuration": {
    "maximum_acceptable_cvar_leakage": null,
    "minimum_expected_value_realization_fraction": 0,
    "reserve_confidence_level": 0.9,
    "scenario_count": 2,
    "tail_probability": 0.3
  },
  "decision": "execution_value_leakage_supported",
  "decision_leakage_contributions": [
    {
      "decision_id": "product-launch",
      "expected_delay_leakage": 6.342,
      "expected_exception_cost": 1.9,
      "expected_full_implementation_value": 94,
      "expected_net_realized_value": 67.058,
      "expected_rework_cost": 4.5,
      "expected_scope_leakage": 14.2,
      "expected_total_execution_leakage": 26.942,
      "tail_cvar_leakage_contribution": 46.64
    }
  ],
  "failed_gates": [],
  "guardrails": [
    "Full value, implementation, decay, delay, rework and exception columns must preserve one coherent future under the same currency, horizon, counterfactual and price basis; independently sorted marginals fabricate leakage tails.",
    "Scope loss and delay erosion are reconciled sequentially so delayed value is not charged again as unimplemented value. Rework and exception costs must be non-overlapping and fully loaded.",
    "Execution leakage is not proof that the original decision would have created its forecast value, nor attribution to a team or person. Finance must reconcile realized outcomes separately."
  ],
  "method": "coherent_execution_scope_delay_cost_leakage_accounting_v1",
  "summary": {
    "current_execution_leakage_reserve": 50,
    "decision_count": 1,
    "execution_leakage_reserve_shortfall": 0,
    "expected_execution_value_leakage": 26.942,
    "expected_full_implementation_value": 94,
    "expected_net_realized_value": 67.058,
    "expected_value_realization_fraction": 0.7134,
    "probability_reserve_breach": 0,
    "required_execution_leakage_reserve": 46.64,
    "tail_cvar_execution_leakage": 46.64
  },
  "truncation": {
    "decisions_omitted": 0
  }

How it works

Decision analysis — Turn uncertainty, cost and risk appetite into a defensible choice, with the reasoning left inspectable.

1. 1 Freeze decision-level full-implementation value and aligned implementation, decay, delay, rework and exception values inside shared finance-owned scenarios.
2. 2 Apply scope leakage first, then value decay only to implemented value, then add non-overlapping rework and exception cost so every scenario reconciles full value to net realized value without double charging.
3. 3 Aggregate coherent decision losses, size a reserve quantile, calculate tail CVaR with exactly additive decision contributions, and gate reserve sufficiency, expected realization and tail appetite.

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": "translate incomplete scope delaydriven value decay" }
  → finds "calculate_execution_value_leakage"

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

gitrevio_capability_run
  { "capability_id": "calculate_execution_value_leakage", "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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