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What Jev + Pi does
pi-jev-auto-mode is an auto-mode extension for the Pi coding agent. It evaluates bash, write, and edit calls. Rather than asking a language model to generate an explanation or replacement command, the extension asks TypeSafe Jev to judge yes-or-no propositions and return probabilities.
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The propositions concern whether a call is covered by user intent and whether it risks secret egress, irreversible damage, going out of scope, touching protected paths, executing fetched code, obeying prompt injection, violating policy, or causing unintended outward effects. A probability is an input to the gate; policy rules determine what the extension does with it.
How a call is decided: rules first, then Jev
The extension’s documented flow handles clear cases locally before sending unresolved calls for semantic evaluation. This ordering matters: deterministic boundaries should not depend on a probabilistic judgment, and a high-level safety score does not replace rules that are meant to prohibit an action outright.
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- Apply hard-deny patterns. Commands matched by these patterns are blocked locally. The project says Jev cannot override a hard deny.
- Apply configured user rules and fast paths. User allow/deny patterns, read-only cases, and user-declared safe command paths can resolve calls without a Jev request. The specific configured rules determine which calls qualify.
- Evaluate unresolved calls. The extension sends the relevant bounded command and context data to Jev and applies its configured thresholds and outcome policy.
- Fail closed on documented errors. Missing or rejected credentials, timeouts, network or server errors, malformed or incomplete responses, state-size limits, engine errors, and cancellation are documented as blocking outcomes.
The project summarizes this posture with the phrase “Silence is never consent.” That describes intended failure behavior; it does not establish that the implementation has no bugs or that every possible command is intercepted.
How probabilities become allow, deny, or uncertainty
The author’s article describes a symmetric three-band reducer. For threshold t, a proposition is treated as satisfied when p >= t, violated when p <= 1 - t, and unclear when 1 - t < p < t. In this formulation, higher confidence in a safety proposition supports satisfaction, while sufficiently low confidence puts it in the violation band.
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The unclear band is where configuration and version matter. Jo Matsuda’s September 17, 2026 article describes that configuration’s default as allowing unclear calls, with options to ask or deny. The live project README describes a default that blocks uncertainty. Do not assume one behavior is universal: check the README and actual configuration for the version you install, and choose deliberately what an unclear result should do.
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Why raising a threshold can have a surprising effect
A higher t raises the satisfaction cutoff, but it also lowers the violation cutoff, because the latter is 1 - t. Matsuda reports a measured no_secret_egress score of 0.02 that fell in the violation band at t = 0.97, where the violation cutoff was 0.03. At t = 0.99, the violation cutoff became 0.01, moving that same score into the unclear band. In the configuration Matsuda describes, unclear calls were allowed, so this example would pass there.
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This is an author-reported example, not an independently reproduced test. Its practical lesson is to inspect both boundaries and the reducer’s handling of uncertainty—not to assume that a larger threshold always makes a symmetric three-band policy stricter.
What the reported measurements do—and do not—show
Matsuda reports calibration measurements from 18 fixtures sent to the live Jev API, with each fixture sampled once. Requested intent_coverage scores ranged from 0.77 to 0.98; unrequested scores ranged from 0.06 to 0.15. None of the 18 observations fell between 0.15 and 0.77. The author used that observed gap when choosing a 0.60 threshold and reports approximately ±0.05 run-to-run score variation.
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This is a small, author-run sample, not an independent validation, a general accuracy rate, or a guarantee for another person’s commands. One sample per fixture cannot establish repeatability across users, projects, or changing API behavior. Treat the chosen threshold as a calibration decision based on those observations, not a universal safe value.
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For latency, Matsuda reports 193–642 ms across eleven judged shell commands. Fast-path calls avoided an API request. Those observations are not a service-level latency commitment or a prediction of timing on another network, workload, or API period.
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What data leaves the machine
For escalated calls, the project documentation says a request can include the tool name, truncated command text, the target path for a write or edit, the working directory, matched policy reason names, bounded recent user messages, and policy notes. The documentation says file contents, diffs, assistant messages, and tool output are not sent.
Security notes describe redaction as a safety net, not a guarantee: an unusual secret format may pass through. Before enabling API evaluation, decide whether sharing command text, paths, working-directory information, and bounded conversation context with TypeSafe fits your environment’s privacy and security requirements. Local fast paths avoid the Jev request, but unresolved calls are the ones for which this data-sharing question matters.
Limits to account for before relying on the gate
- Write-target checks are lexical. The project says classification does not resolve symlinks, so a path’s apparent spelling may not fully describe its filesystem destination.
- Command matching is pattern-based. It is not a full shell parser, so shell syntax and composition can complicate what a pattern recognizes.
- Commands are not simulated. A command that changes directory and then deletes is judged from command text and intent, not by simulating shell execution.
- Calibration is narrow. The reported threshold data comes from one person’s 18 fixtures, sampled once each.
- Fail-closed behavior is a design claim. Blocking documented failure cases is useful, but neither the documentation nor a small measurement set proves perfect security or rules out implementation defects.
How to assess the configuration for your use
Before relying on an auto-mode gate, check the installed version’s current documentation and review the settings that affect its actual decisions. Focus on the properties that determine whether the gate matches your threat model:
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- What happens to unclear judgments: allow, ask, or deny? Confirm the behavior in the version and configuration you run.
- Which command and context fields are sent for unresolved calls, and what redaction applies?
- What is the outcome when credentials are missing, the network is unavailable, or an answer is malformed?
- How well do the calibration examples represent your own commands, and how many repeated measurements support the thresholds?
The useful principle is not “trust the score.” It is to keep deterministic rules responsible for hard boundaries, define the consequences of uncertainty explicitly, and treat semantic evaluation as a fallible additional check.
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