A human example
A product description uses vague claims and never says what the app does.
What the caller supplies
The caller supplies the draft and separate rules for clarity, specificity and actionability.
What happens next
Jev returns advisory checks. The writing agent revises the draft and a reader reviews the result.
Illustrative example, not a recorded result.
Potential value: medium
Repeated editorial checks may be useful when a team has explicit rules, including the Unslop rules used in this project.
Evidence confidence: low
Official guidance proposes semantic linting. We have no blinded study of these writing checks.
The rating describes support for this claim. It is separate from Jev's returned probability. How we assign ratings.
Evidence, including disagreement
- Define ambiguity before evaluating a workflow. reference. Specify what uncertain and mixed cases should do.
- TypeSafe examples for decisions over supplied text. reference. Official examples show how to frame the question; each adaptation needs testing.
The next test
40 original paragraphs with blind human labels per rule, including intentional quotations and technical prose.
Compare against
- Deterministic style checks
- Direct agent review
Measure
- Agreement per rule
- Unnecessary revisions
- Blind preference after editing
- Time
Decision after the test
Keep only rules with useful agreement and low false-positive rates. Do not combine them into an unexplained writing score.
The report will retain inputs, question versions, every attempt and failure examples. We will update the confidence rating after reviewing the result.
Use a related Engine recipe
Recipes are implementation starting points. Their presence does not mean the protocol above has passed.