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Minimal DBL stress-case runner for deterministic event streams

Project description

dbl-stress-template

CI PyPI Python

dbl-stress-template is a small diagnostic template for stress-testing real systems against DBL invariants. It helps you locate where normativity enters, what is treated as authority, and whether replay is possible. It is not a framework and not a product. It is a repeatable analysis scaffold. Use it to surface implicit decisions, observation leaks, and non-replayable state. The template is designed to map directly onto DBL artifacts: L, G, V, DECISION, PROOF, EXECUTION.

The 5-field stress frame

  1. Trigger
  2. Normative Question
  3. Authority
  4. Irreversibility
  5. Replay Requirement

Compact examples

Access decision (ALLOW or DENY)

  • Trigger: request to access a protected resource
  • Normative Question: is access permitted
  • Authority: policy version and admitted inputs (L)
  • Irreversibility: access once granted can leak data
  • Replay Requirement: decision must be reproducible from V

Irreversible deletion

  • Trigger: delete request
  • Normative Question: is deletion permitted
  • Authority: policy version and admitted inputs (L)
  • Irreversibility: deletion cannot be undone
  • Replay Requirement: decision and justification must be reconstructible

What this is not

  • Not a policy engine
  • Not a governance framework
  • Not a runtime or integration layer
  • Not a compliance product

How to use this with DBL

  • Trigger maps to INTENT creation and L admission.
  • Normative Question must be resolved only via DECISION events.
  • Authority must be admitted by L and consumed by G, never from observations.
  • Irreversibility increases the need for explicit DECISION and stable policy versions.
  • Replay Requirement must be satisfied by V alone, using DECISION and PROOF separation.

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