Optimize knowledge resilience portfolio

What it's for

Lets a CTO move from 'we have a bus-factor problem' to a financially constrained, evidence-backed cross-training and backup-ownership plan that prices common shocks and scarce mentor time.

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.

{
  "budget": 30,
  "intervention_studies": [
    {
      "comparable_outcome": true,
      "control_failures": 40,
      "control_trials": 100,
      "evidence_verified": true,
      "id": "study-a",
      "option_type": "cross_training",
      "propensity_known": false,
      "prospectively_registered": true,
      "randomized": true,
      "transport_weight": 1,
      "treated_failures": 10,
      "treated_trials": 100
    }
  ],
  "knowledge_units": [
    {
      "baseline_failure_probabilities": [
        0.4,
        0.7
      ],
      "baseline_owner_refs": [
        "owner-a"
      ],
      "common_risk_group_id": "platform",
      "direct_loss_values": [
        100,
        180
      ],
      "evidence_verified": true,
      "id": "payments",
      "mandatory_resilience_required": true
    }
  ],
  "learner_capacity_hours": {
    "learner-a": 10
  },
  "maximum_cvar_loss": 500,
  "maximum_expected_failure_count": 1,
  "mentor_capacity_hours": {
    "mentor-a": 10

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": {
    "common_risk_loss_counted_once_per_scenario_draw": true,
    "individual_employment_decision_permitted": false,
    "one_option_per_knowledge_unit": true,
    "simulation_draws": 200,
    "transport_weighted_beta_binomial_relative_failure_risk": true
  },
  "constraints": {
    "all_unit_evidence_verified": true,
    "budget": 30,
    "maximum_cvar_loss": 500,
    "maximum_expected_failure_count": 1,
    "tail_probability": 0.1
  },
  "decision": "selected",
  "effect_posteriors": {
    "cross_training": {
      "p05_relative_failure_risk": 0.1454,
      "p95_relative_failure_risk": 0.3939,
      "posterior_mean_relative_failure_risk": 0.2587,
      "probability_reduces_failure": 1
    }
  },
  "limitations": [
    "Intervention effects transport only to the declared comparable outcome and context; posterior intervals do not prove universal effectiveness.",
    "The optimizer proposes governed knowledge-resilience work and never executes staffing, access, review, or production changes.",
    "Opaque mentor and learner references are operational assignments, not performance rankings or employment recommendations."
  ],
  "method": "transport_weighted_bayesian_knowledge_resilience_stochastic_portfolio_v1",
  "pareto_frontier": [
    {
      "conditional_value_at_risk": 173.5,
      "expected_failure_count": 0.135,
      "expected_loss_including_cost": 35.05,
      "option_ids": [
        "cross-train"
      ],
      "total_cost": 15
    }
  ],
  "search": {
    "candidate_combination_count": 2,
    "evaluated_portfolio_count": 1,

How it works

Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

1. 1 Admit only prospectively registered randomized or propensity-known studies with comparable mature continuity outcomes and transport weights; estimate intervention-type posterior relative failure risk without treating observational activity as causal benefit.
2. 2 Enumerate or deterministically beam-search one controlled option per knowledge unit, simulate posterior effect and coherent contributor-availability scenarios, count direct unit loss once and shared common-risk loss once per group and draw, and enforce mandatory resilience, budget, mentor and learner capacity.
3. 3 Return the minimum expected loss-plus-cost feasible portfolio, posterior effect intervals, expected failures, VaR/CVaR and a cost/risk Pareto frontier; hold when no governed option set satisfies the declared 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": "choose one baseline crosstraining pairedreview rotation" }
  → finds "optimize_knowledge_resilience_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_knowledge_resilience_portfolio", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

Related tools

See every tool in People, retention & knowledge →