Optimize technical debt paydown portfolio

What it's for

Turns technical debt from a qualitative backlog label into a transparent capital-allocation case with avoided cost, NPV, break-even period, and cost of another period's deferral.

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.

{
  "capacity_budget": 5,
  "capacity_opportunity_cost_per_unit": 5,
  "cash_budget": 100,
  "debt_items": [
    {
      "capacity_cost": 2,
      "cash_cost": 50,
      "depends_on": [],
      "drag_growth_rate_per_period": 0,
      "failure_loss": 0,
      "failure_probability_per_period": 0,
      "id": "platform-foundation",
      "recurring_drag_cost": 0,
      "remediation_effectiveness": 1,
      "risk_reduction": 1
    },
    {
      "capacity_cost": 3,
      "cash_cost": 50,
      "depends_on": [
        "platform-foundation"
      ],
      "drag_growth_rate_per_period": 0.1,
      "failure_loss": 200,
      "failure_probability_per_period": 0.05,
      "id": "release-bottleneck",
      "recurring_drag_cost": 50,
      "remediation_effectiveness": 0.8,
      "risk_reduction": 0.8
    }
  ],
  "discount_rate_per_period": 0.02,
  "horizon_periods": 8
}

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.

{
  "decision": "technical_debt_paydown_portfolio_ready",
  "economics": {
    "baseline_discounted_debt_cost": 603.5468,
    "capacity_opportunity_cost_per_unit": 5,
    "discount_rate_per_period": 0.02,
    "horizon_periods": 8,
    "residual_discounted_debt_cost": 120.7094
  },
  "interpretation": "Savings are scenario economics, not measured causal effects; recurring drag, failure exposure, remediation effectiveness, and opportunity cost must be calibrated locally.",
  "method": "discounted_debt_interest_dependency_portfolio_v1",
  "portfolio": {
    "capacity_budget": 5,
    "capacity_used": 5,
    "cash_budget": 100,
    "cash_used": 100,
    "discounted_avoided_cost": 482.8375,
    "net_present_value": 357.8375,
    "one_period_deferral_cost": 45.549,
    "selected_items": 2
  },
  "sample": {
    "candidate_items": 2,
    "dependency_edges": 1
  },
  "search": {
    "evaluated_portfolios": 4,
    "global_optimum_guaranteed": true,
    "strategy": "exact_dependency_safe_enumeration"
  },
  "selected": [
    {
      "break_even_period": null,
      "capacity_cost": 2,
      "cash_cost": 50,
      "discounted_avoided_cost": 0,
      "id": "platform-foundation",
      "net_present_value": -60,
      "one_period_deferral_cost": -1.1765,
      "selected_as_dependency": true
    },
    {
      "break_even_period": 2,
      "capacity_cost": 3,

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 Project each debt item's recurring drag and expected failure loss over the governed horizon, apply remediation effectiveness and risk reduction, and discount avoided costs to present value.
2. 2 Subtract cash cost and the organization-owned opportunity cost of engineering capacity to obtain item economics, break-even timing, and one-period deferral cost.
3. 3 Expand every candidate to its full dependency closure and reject portfolios that violate capacity, cash, item-count, dependency, or mutual-exclusion constraints.
4. 4 Enumerate the global feasible optimum for at most 18 items; above that boundary, use a disclosed dependency-closure greedy heuristic and never label it globally optimal.

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 a dependency and exclusionsafe technicaldebt" }
  → finds "optimize_technical_debt_paydown_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_technical_debt_paydown_portfolio", "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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