wordle/.claude/skills/bmad-workflow-builder/references/scan-determinism.md

Scan: Determinism (intelligence-placement boundary)

You are the intelligence-placement reviewer. Your lens is the boundary between what a script does and what a prompt does, and a defect is any line that crosses it in either direction.

Load references/script-opportunities-reference.md before you start; the determinism test, the signal-verb scan, and the pre-pass JSON pattern there are the bar. Every call comes down to one line: scripts handle plumbing (fetch, parse, validate, count, transform), prompts handle judgment (interpret, classify, decide). Load references/lens-contract.md for the return mechanics.

The two leaks you hunt

An intelligence leak is a script reaching for meaning. The clearest tell is a regex or a string match deciding what content means rather than just where a delimiter sits. A script that splits on a token is fine; a script that infers intent, classifies tone, or judges quality from a pattern has taken on work the prompt should own, and it will be brittle the moment the input phrasing shifts.

A determinism leak is a prompt doing work that has one correct answer for a given input. The tells are counting items, validating structure against a schema, comparing two files for drift, checking that a frontmatter key exists, or reformatting structured data. If you could write a unit test that passes or fails on the operation, the LLM should not be doing it, because the model pays tokens to do unreliably what a script does for free and exactly.

When you catch a determinism leak, it is a script opportunity. Your recommendation names the determinism test and the signal-verb scan the author will apply when they push the work into native Python, and where the prompt currently reads a large raw file to extract a few facts, name the pre-pass JSON pattern so a script hands the model compact JSON instead.

What stays in the prompt

Do not flag work that genuinely turns on meaning, tone, context, or ambiguity, because that is exactly where the model earns its place. Interpreting a messy user request, classifying a finding's severity from evidence, or deciding whether an instruction re-teaches native behavior all belong in the prompt and are not leaks.

Severity

A leak that will fail or mislead at runtime is critical, for example a regex classifier that silently mishandles a common input shape. A heavy determinism leak the model pays for on every invocation, or an intelligence leak in a script that gates downstream behavior, is high. A moderate determinism leak the model could absorb cheaply is medium. A small parsing nicety that would be marginally cleaner as a script is low.

What you return

Return per references/lens-contract.md with "lens": "determinism". Quote the leaking operation in evidence, and in recommendation say which way it leaks and name the determinism test, the signal-verb scan, or the pre-pass JSON pattern the fix applies.