Coding-agent memory where every fact is a verbatim quote graded by TypeSafe Jev's calibrated confidence. Local-first, SQLite receipts, zero dependencies.
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Updated
Sep 22, 2026 - Python
Coding-agent memory where every fact is a verbatim quote graded by TypeSafe Jev's calibrated confidence. Local-first, SQLite receipts, zero dependencies.
Agent skills for designing, training, evaluating and improving application-specific decision systems. Primitive/model selection, data assembly, export/reload and bounded hill climbing. TypeSafe Jev is the default hosted exemplar; independent of TypeSafe.
Per-repo memory, outcome telemetry, and a calibrated-confidence gate for Claude Code, with MCP and AGENTS.md projections so other AI coding tools can read its context. Notes survive sessions; success claims need test evidence; your reverts are remembered. Local-only, stdlib runtime.
Eight minimal working examples of TypeSafe's Jev (a System One model) applied to mechanical and electrical engineering: CAD/CAE/CAM routing, FEM result triage, DFM screening, BOM alignment, hallucination-proof extraction. Zero dependencies.
mBFT: Metacognitive Byzantine Fault Tolerance — reference implementation for Consensus-Driven Metacognition in Multi-Agent Systems
Make AI agents check what they actually verified before claiming something is done.
Minimal demo of calibrated LLM-as-a-judge scores from the single-call token-logprob distribution (Wang et al. 2025; G-Eval).
Unofficial agent skill for TypeSafe AI's Jev: typed Choice, Score, and Noul decisions with calibrated confidence, and eleven implementation shapes, each with a code sketch.
To associate your repository with the calibrated-confidence topic, visit your repo's landing page and select "manage topics."