taste-lint
CLI that uses Jev probabilities on semantic taste checks to catch AI slop in UI, copy, and agent instructions before ship; measurable rules stay local and active findings can fail a run.
何を判断するか
CHOICE
Picks one option from a fixed set
SCORE
Rates against ordered levels
NOUL
Answers a yes / no question
Inferred from this project's own one-line summary in the community list — it reads as a NOUL decision. Read the source to confirm.
NOUL 呼び出しの形
この質問型の一般的な骨格であって、このプロジェクトの実際のコードではない。
from typesafe import TypeSafe
ts = TypeSafe()
result = ts.evaluate(
state=tool_call,
questions={
"is_risky": {
"type": "noul",
"instructions": "Could this call delete or overwrite user data?",
}
},
)
if result["is_risky"] and result["is_risky_confidence"] > 0.6:
escalate_to_human(tool_call)検証とガードレールの他のプロジェクト
jev-reviewstaged code-review workflow and local dashboard where Jev gates each review stage before a change advances.is-maliciousasks Jev `Noul` checks about source and build files, escalates suspicious chunks for a second pass, and returns implicated files and lines before execution.pi-jevadds a measured tool-call gate to the Pi coding agent so risky calls are checked by Jev before execution.OpenWorkwires Jev into its eval testkit as a verification judge so agent-produced work is gated by typed verdicts rather than a text model.