DAG Quality

Coordinates separate quality evidence lanes between DAG nodes. Use Multidimensional Quality and Evidence Status: schema conformance, claim evidence, provenance, uncertainty, acceptance, and user preference are not interchangeable scores.

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When to Use

✅ Use for:

• Validating node output against declared schema
• Evaluating a defined confidence forecast with an outcome oracle and calibration evidence
• Detecting fabricated content, false citations, unverifiable claims
• Deciding whether to iterate (re-execute a node or loop)
• Generating structured improvement feedback for re-execution
• Monitoring quality trends across iterations

❌ NOT for:

• Executing nodes (use dag-runtime)
• Planning DAG structure (use dag-planner)
• Grading skills themselves (use skill-grader)

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Quality Pipeline

flowchart TD
  O[Node output] --> SV[Structural contract result]
  O --> CE[Claim-evidence result]
  O --> PV[Provenance and authority result]
  O --> UP[User/task preference result]
  SV --> D[Declared acceptance evaluator]
  CE --> D
  PV --> D
  UP --> D
  D --> ACC[Accept, hold, reject, or escalate with reasons]

Schema Validation

Structural check: does the output match the node's declared output contract?

• Required fields present
• Types correct (string, number, array, object)
• Constraints met (min/max length, ranges, enums)
• Nested structures valid

Content Validation

Semantic check: is the content reasonable?

• Non-empty meaningful content (not just filler)
• Length within expected range for the task
• Internal consistency (no contradictions)
• References exist (cited files, URLs, identifiers)

Confidence Scoring

Record confidence only as a forecast of a defined event with a resolution source and calibrated cohort. Do not combine self, peer, downstream, or human observations into a universal score; an aggregate requires a declared decision loss and validation.

Hallucination Detection

Claim-evidence checks:

• Citation verification: Is the source identity, passage, date, and entailment/contradiction relation inspectable?
• Internal consistency: Does the output contradict itself or the input?
• Uncertainty: Missing or inaccessible evidence is insufficient evidence, not falsity.
• Entity verification: Do named entities (people, tools, APIs) actually exist?

Iteration Decision

flowchart TD
    A[Acceptance condition unmet or new evidence] --> B[Identify affected evidence lane and artifact]
    B --> C{Authorized bounded change can test a causal factor?}
    C -->|Yes| D[Propose revision with acceptance/regression checks]
    C -->|No| E[Hold or escalate with unresolved evidence]
    D --> F[Record new result without overwriting prior evidence]

Feedback Synthesis

When iterating, produce structured improvement guidance:

{
  "acceptance_status": "needs_revision",
  "contract": "illustrative-five-recommendation-contract",
  "specific_issues": [
    {"field": "recommendations", "issue": "Only 2 of 5 required recommendations provided", "fix": "Add 3 more recommendations addressing scalability, testing, and deployment"},
    {"field": "citations", "issue": "Source [3] was not retrievable at review time", "fix": "Record insufficient evidence and verify source identity/claim entailment before retaining the claim"}
  ],
  "strengths_to_preserve": ["Clear structure", "Good code examples"],
  "iteration_guidance": "Focus on completeness (missing recommendations) and citation accuracy. Do not rewrite the well-structured sections."
}

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Convergence Monitoring

Track declared acceptance conditions and comparable evidence across revisions:

• Improving: a comparable acceptance measure improves; continue only if a bounded useful revision and remaining authority/resources justify it
• Plateauing: the same acceptance condition is unmet and no changed-factor experiment remains → hold or escalate
• Degrading: a revision fails its declared regression check → retain the prior artifact and investigate
• Oscillating: evaluators disagree → preserve their evidence and route by the declared conflict policy

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Related specialisms

This coordinating skill routes to these narrower skills; their existence is not a claim that a runtime implementation has been replaced: dag-output-validator, dag-confidence-scorer, dag-hallucination-detector, dag-convergence-monitor, dag-iteration-detector, dag-feedback-synthesizer