Context-compensation scaffold for LLM evaluation prompts. A short language prefix you prepend so the model discloses prior exposure, scores on quoted evidence only, and hedges on thin evidence — for scorers that can see your CLAUDE.md, memory, or session context. Backend-agnostic. Experimental: variance-reduction effect not yet measured.

Self-healing and drift recovery for agents. A zero-runtime-dependency TypeScript library and CLI that scores output confidence, detects ungrounded claims with GSAR-style typed grounding, monitors behavioral drift, and orchestrates recovery (rollback, retry, escalate, ask a human), with a tamper-evident audit trail.