Jev decisions

Jev answers typed questions about supplied state. Choose it for a bounded decision with explicit criteria; use the harness's generative model for prose, explanations, and code.

For quick invocation, use the absolute launcher recorded in knowledge/local/command.md when present. It handles pin resolution, local credential injection and logging; do not rediscover authentication. See launcher setup only if unconfigured.

For default use and fresh-subagent access, read harness defaults. Availability does not require a call on every task.

Use the same information-sharing boundary for Jev request content as for the user's use of the OpenAI API. Relevant private or sensitive context needs no additional Jev-specific disclosure approval. Withhold only information that would also be withheld from the OpenAI API; honor explicit user/project restrictions. See task data.

1. Identify the state, decision, and allowed outputs. Keep the state focused on evidence needed for that decision.
2. Choose choice, score, or noul. Use the knowledge index and targeted local search to find question design, API guidance, or interpretation details. Read only the relevant topic. Multiple questions share state and are independent; dependent questions need another call.
3. Use the CLI or TypeScript library directly; do not register or invoke a Jev MCP server. Follow tool use for the pinned CLI or library. Use recipes to triage excerpts before loading more context (evidence_relevance) or check candidate findings against evidence (finding_support). Invoke within the host's authorization and parse correlated results. Check availability before claiming inference has run.
4. Model confidence is not a measured probability that a decision is correct. Consult the local interpretation topic when selecting thresholds.
5. Explain how the answer affected the task. Record used, overridden, or unavailable with the returned callId through jev outcome or the library. These are caller reports, not quality scores. Do not add a call solely to fill a usage quota.

The knowledge index also routes skill selection, harness patterns, and upstream sources. The host retains responsibility for authorization and deterministic constraints; a model decision supplies evidence for the workflow.

Check the memory index for applicable observed lessons. Maintain new expertise and lessons in this component's writable, scoped local directories with dates and evidence. Keep indexes current and avoid loading the whole corpus. Shared caches and unrelated host memory are not write targets.