dsh-jev-interceptor

dsh-jev-interceptor ⚡ Millisecond judgement for every tool call and every recalled message — for about two millionths of a dollar each. Your agent's most expensive habits: asking a poetry-writing LLM yes/no questions, and amputating your context by *age*. This plugin wires Jev — the non-generative "System One" model that broke everyone's feed — into the decision points of DeepSeek Harness where an LLM is overkill and rules are blind.

English | 中文 The Web panel (sidebar ⚡ → Jev 决策): live KPIs and net-benefit card, a filterable decision log with full evidence per row, shadow-data threshold advice that tracks whether you adopted it, and one-click connection test. Every screenshot above is the real plugin running real decisions.

What it does, in one breath: before a tool call runs, Jev classifies its risk, irreversibility, task-fit, and injection-suspicion in one ~$0.00002 request — confident high-risk calls get denied, medium ones escalate to a human, and clearly-granted reversible ones stop wasting your clicks on approval dialogs. And when an @session snapshot gets injected, Jev scores every message's value for the citing task so the error traceback survives the byte budget instead of the oldest small talk. Every doubt, every timeout, every missing key degrades to stock dsh behavior. Nothing to configure away, nothing that can widen a permission. Why this exists

dsh ships zero per-call risk classification — the pre-execute waterfall's default is a bare allow. Its only built-in precedent, experimental/auto-review, does the classification with a *generative* LLM: one full model request per tool call, temperature 0, hand-rolled JSON text protocol, self-described as slow, expensive, and experimental. That's a System-2 scribe doing a System-1 reflex's job: Jev's maker TypeSafe reports up to 200× faster / 400× cheaper than LLMs on classification workflows — and this plugin is that number, landed in a real agent harness, with receipts in /jev-stats.

We believe this is the first System-1 decision plugin in the dsh ecosystem. The full map of where decision models fit in dsh — this plugin's two hooks plus eleven more verified hooks (semantic model routing, context-retention scoring, image-offload pre-planning, worker-report verification...) — is in docs/jev-usage-points.md. The FIFO pain point nobody talks about

When you @-mention a past session in dsh, the harness injects a bounded snapshot of it — and when that snapshot exceeds its byte budget, it drops messages oldest-first. Pure FIFO. Zero semantics. The bug report you pasted at the top of the session and the three-line question that started it all? Dropped first. The "thanks!" and "ok, continue"? Kept — they were newer.

dsh's own retention code is honest about it: drop the oldest, then truncate the longest, done. This plugin's jev-session-reference row takes that decision over (by subclassing the upstream resolver, so @-completion, budgets, spill, and cancellation stay inherited): one Jev fan-out scores each droppable message — *noise / background / relevant / critical* — and the drop order becomes least valuable first, with truncation cutting padding before substance. Checkpoints and the newest message stay protected exactly as upstream; the byte budget is honored exactly as upstream; and with sessionReferenceEnabled: false (the default) the row renders byte-for-byte like stock.

In shadow mode you get the receipts before trusting it: every injection logs the counterfactual — which messages FIFO dropped that scoring would have kept — then flip to enforce. The 60-second tour

No YAML needed. Once installed, control everything from the chat box — commands persist to the profile and take effect on the next tool call, no reload:

Use your agent normally. In shadow mode every decision lands in telemetry with its full probability distribution; /jev-stats summarizes: