Value dependency unblocking

What it's for

Turns a blocker from a red icon into the expected financial value, affected downstream work, probability, and tail risk of resolving it.

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.

{
  "blocker_id": "platform",
  "blocker_unblocked_duration_scenarios": [
    1,
    2
  ],
  "scenario_probabilities": [
    0.6,
    0.4
  ],
  "tasks": [
    {
      "completion_value": 0,
      "duration_scenarios": [
        5,
        8
      ],
      "id": "platform",
      "predecessor_ids": [],
      "value_decay_per_period": 0
    },
    {
      "completion_value": 1000,
      "duration_scenarios": [
        2,
        2
      ],
      "id": "product-a",
      "predecessor_ids": [
        "platform"
      ],
      "value_decay_per_period": 0.1
    },
    {
      "completion_value": 2000,
      "duration_scenarios": [
        3,
        3
      ],
      "id": "product-b",
      "predecessor_ids": [
        "platform"
      ],
      "value_decay_per_period": 0.1

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.

{
  "assumptions": [
    "The dependency graph is acyclic, duration scenario columns encode the same joint future, tasks start as soon as all predecessors finish, and no unmodeled shared resource constraint changes the critical path.",
    "Completion value decays exponentially at the governed task-specific rate, is incremental and non-duplicative, and unblock cost and value share currency and horizon.",
    "Unblock success is represented as a governed Bernoulli probability independent of scenario unless encoded otherwise; earlier completion is model-conditional value, not causal proof that a team or person created it."
  ],
  "configuration": {
    "scenario_count": 2,
    "tail_probability": 0.1,
    "task_count": 3,
    "unblock_cost": 100,
    "unblock_success_probability": 0.9
  },
  "decision": "dependency_unblocking_value_positive",
  "method": "scenario_dag_dependency_unblocking_value_v1",
  "summary": {
    "affected_task_count": 3,
    "blocker_id": "platform",
    "cvar_net_unblocking_value_loss": 100,
    "expected_gross_value_gain_if_unblock_succeeds": 751.633,
    "expected_net_unblocking_value": 576.4697,
    "probability_positive_net_unblocking_value": 0.9
  },
  "task_diagnostics": [
    {
      "expected_completion_time_saved": 4.8,
      "expected_gross_value_gain_if_unblock_succeeds": 484.117,
      "task_id": "product-b"
    },
    {
      "expected_completion_time_saved": 4.8,
      "expected_gross_value_gain_if_unblock_succeeds": 267.516,
      "task_id": "product-a"
    },
    {
      "expected_completion_time_saved": 4.8,
      "expected_gross_value_gain_if_unblock_succeeds": 0,
      "task_id": "platform"
    }
  ],
  "truncated_task_count": 0
}

How it works

Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.

1. 1 Freeze an acyclic task graph, aligned baseline and unblocked blocker durations, incremental nonduplicative completion values, decay rates, unblock cost, and success probability.
2. 2 Compute baseline and unblocked earliest completion in every joint scenario, revalue every affected descendant, and combine success-net and failure-net states into expected value, positive-value probability, and CVaR.
3. 3 Support unblocking only when represented net value and probability clear the built-in decision rule, while separately testing shared capacity, causal effectiveness, and omitted downstream effects.

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": "value shortening one blocker by propagating" }
  → finds "value_dependency_unblocking"

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

gitrevio_capability_run
  { "capability_id": "value_dependency_unblocking", "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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