Your agents are a generation behind, and they will never tell you

A TypeSafe model called Jev illustrates a broader challenge for AI-assisted software development: agents trained before recent product releases can confidently recommend outdated approaches without signaling uncertainty. In response, the author describes a “post-cutoff” add-on for the ox CLI that distributes a small, curated set of dated team briefs across repositories and agent tools, preserving judgments about technologies the team has adopted or rejected rather than repeatedly relying on fresh searches. The system emphasizes strict limits, human promotion, token-efficient indexing, and entries that explain reusable problem patterns instead of merely endorsing vendors. Jev serves as an example: it provides fast, inexpensive typed outputs and prevents values outside a specified option set, but independent benchmarks indicate weaker accuracy and unreliable confidence calibration for some tasks, making it best suited to carefully benchmarked routing or gating roles rather than general replacement of larger models. The add-on also addresses multi-repository distribution, diagnostics for missing skills, and security risks in public skill registries through curated first-party content, lockfiles, file hashes, and human approval for executable capabilities. Each entry has an expiration date and is retained in a documented archive when retired, reflecting the view that rapidly aging knowledge must be actively removed once models absorb it or circumstances change.

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