Overview & Result
The author built a portable Agent Skill plus an optional MCP server exposing jev_evaluate. Coding agents can use Jev for bounded routing, ranking and code-review judgments while retaining their main LLM for generation and deterministic code for exact checks. The public repository contains both the Skill and MCP implementation.
How Jev fits in the loop
- A coding agent frames a narrow decision as Choice, Score or Noul instead of asking for free-form generation.
- The Skill can call the TypeSafe API directly or use the optional jev_evaluate MCP tool.
- The agent applies its own deterministic confidence and risk thresholds to the result.
How to reproduce
- Read the Agent Skill in skills/jev-agent-toolkit/SKILL.md.
- For the optional tool bridge, follow mcp/jev/README.md and configure a TypeSafe API key.
- Try a bounded code-review or routing question and inspect the returned typed probabilities.
Why this build matters
A concrete Skill and MCP integration showing where a small judgment model fits inside a coding-agent workflow, with an explicit statement that overall gains remain unmeasured.
Limitations
- The author explicitly reports no benchmark showing that full coding-agent runs are better, cheaper or faster overall.
- Parsing, compilation, tests, thresholds and other exact operations remain deterministic code paths.