Open-source LLM migration and regression testing for AI agents. Compare models, detect tool-call regressions, and gate model changes in CI.
Agent skill that upgrades LLM code to Claude Opus 5.5, GPT-6, Gemini 3.8 without breaking prod. Fixes breaking changes, then proves it with tests and evals.
Plan, research, and evaluate LLM application migrations between models, providers, and platforms (Claude, GPT, Bedrock) before changing code
Evaluate Claude Sonnet model migrations with paired workload evidence, critical quality gates, normalized token costs, compatibility checks, and reversible rollout plans.
Govern the switch when you change the AI model in production: require approval, activate with read-back confirmation, verify from telemetry, and roll back to a locked target. Leaves one portable migration record. TypeScript, zero runtime dependencies.
A verified production LLM model migration, and how its method evolved into ModelPromote: blind evaluation, human approval, read-back activation, telemetry verification and rollback. Runnable offline on synthetic data.
A beginner-friendly, hands-on learning path for AI backend development, covering the fundamentals of TypeScript and backend architecture, PostgreSQL and database design, Drizzle ORM and migrations, embeddings and vector databases with pgvector, vector similarity search, RAG pipelines, and the core concepts needed to build AI-powered backend systems
Open-source capture SDK for AI agents — records model and tool calls to build golden suites for LLM migration and regression testing.
GitHub Action for LLM migration and regression testing — runs your golden suite on every PR and fails the check on model regressions.
Claude Code skill: swap System 2 LLM pipeline components for System 1 TypeSafe Jev decisions via investigation, live three-arm eval, fallback, and an independent judge
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