Infer stability selected temporal metric graph

What it's for

Maps which delivery, reliability, review, demand, and capacity signals add stable predictive information about each other without selling lagged association as causal proof.

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,
  "false_discovery_rate": 0.2,
  "maximum_lag": 2,
  "metric_names": [
    "review_load",
    "cycle_time",
    "negative_control"
  ],
  "minimum_edge_stability": 0.6,
  "observations": [
    {
      "id": "temporal-example-0",
      "metrics": {
        "cycle_time": -0.24,
        "negative_control": -0.8,
        "review_load": -0.5
      },
      "period": 0
    },
    {
      "id": "temporal-example-1",
      "metrics": {
        "cycle_time": -0.46199999999999997,
        "negative_control": 0.6000000000000001,
        "review_load": -0.19999999999999998
      },
      "period": 1
    },
    {
      "id": "temporal-example-2",
      "metrics": {
        "cycle_time": -0.3186,
        "negative_control": 0.30000000000000004,
        "review_load": -0.52
      },
      "period": 2
    },
    {
      "id": "temporal-example-3",
      "metrics": {
        "cycle_time": -0.74758,
        "negative_control": 0,
        "review_load": -0.11199999999999999

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": {
    "block_length": 6,
    "bootstrap_draws": 200,
    "false_discovery_rate": 0.2,
    "maximum_lag": 2,
    "minimum_edge_stability": 0.6,
    "minimum_relative_rmse_improvement": 0.03,
    "minimum_standardized_effect": 0.1,
    "validation_fraction": 0.25
  },
  "decision": "stable_temporal_dependencies_supported",
  "edge_count": 2,
  "edges": [
    {
      "lag": 1,
      "p_value": 0.01,
      "practical_stability": 1,
      "q_value": 0.0597,
      "sign_stability": 1,
      "source": "review_load",
      "standardized_effect": 0.838,
      "target": "cycle_time",
      "target_relative_rmse_improvement": 0.5568
    },
    {
      "lag": 2,
      "p_value": 0.01,
      "practical_stability": 1,
      "q_value": 0.0597,
      "sign_stability": 1,
      "source": "cycle_time",
      "standardized_effect": -0.457,
      "target": "review_load",
      "target_relative_rmse_improvement": 0.1236
    }
  ],
  "graph": {
    "most_conditionally_predicted_targets": [
      {
        "incoming_edges": 1,
        "metric": "cycle_time"
      },
      {

How it works

Network & dependency analysis — Trace how load, failure and knowledge propagate through a graph of teams, services or components.

1. 1 Infer a compact aggregate temporal dependency graph with a chronologically held-out ridge VAR, moving-block coefficient bootstrap, practical-effect stability selection, and false-discovery control.
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": "infer a compact aggregate temporal dependency" }
  → finds "infer_stability_selected_temporal_metric_graph"

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

gitrevio_capability_run
  { "capability_id": "infer_stability_selected_temporal_metric_graph", "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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