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dabke reports schedule quality through instruction-level feedback. Feedback is designed for API clients, MCP tools, and LLM consumption. It answers whether scheduling instructions were followed without exposing solver implementation details such as internal constraint buckets.

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Compilation feedback

ModelBuilder.compile() returns feedback alongside the solver request: 
const { request, canSolve, feedback } = builder.compile();

if (!canSolve) {
  console.error(
    feedback.details
      .flatMap((detail) => detail.checks)
      .filter((check) => check.outcome === "error"),
  );
}
The canSolve flag is false when structural errors make solving impossible, for example coverage requirements that reference roles no team member has.

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Solve feedback

schedule(...).solve(...) returns the same feedback shape after analyzing the solver result: 
const result = await schedule({
  // ...
}).solve(client, { dateRange: { start: "2026-02-02", end: "2026-02-08" } });

console.log(result.feedback.status);
console.log(result.feedback.details);

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Feedback structure

A FeedbackReport contains:
  • status: "pass", "has_failures", or "has_errors"
  • summary: check counts by pass, fail, and error
  • details: grouped feedback for one instruction or report-level concern
  • checks: factual checks inside each detail
for (const detail of result.feedback.details) {
  for (const check of detail.checks) {
    console.log(`${detail.title}: ${check.outcome}: ${check.message}`);
  }
}

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Outcomes

  • pass: the instruction was followed
  • fail: a soft instruction was evaluated and not fully followed
  • error: compilation or solving found a blocking issue
When an instruction fully passes, dabke emits a concise check for the whole instruction: 
{
  "id": "instr:1",
  "title": "1x staff during office_hours",
  "checks": [{ "outcome": "pass", "message": "Passed." }]
}
When an instruction has failures, dabke groups facts by outcome and merges adjacent dates and time ranges where possible: 
{
  "id": "instr:2",
  "title": "Time off for alice on 2024-02-01, 2024-02-02",
  "checks": [
    {
      "outcome": "fail",
      "message": "Failed on 2024-02-01.",
      "data": {
        "dateRanges": [{ "start": "2024-02-01", "end": "2024-02-01" }],
        "memberIds": ["alice"],
        "overflow": { "min": 1, "max": 1 }
      }
    }
  ]
}

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Custom rule feedback

Custom rule descriptors should prefer solver artifacts with context for solve-time checks and compile findings for phase errors. Prefer factual messages that describe what happened. Do not include remediation instructions unless your API surface explicitly needs them.