Zero-modification Human-in-the-Loop adapter for OpenOPC's agentic DAG runtime.
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Updated
Aug 20, 2026 - Python
Zero-modification Human-in-the-Loop adapter for OpenOPC's agentic DAG runtime.
Runs ~20 options strategies against live market data in shadow mode, records every hypothetical fill under worst/base/optimistic assumptions, and grades each with anytime-valid e-processes. Places no orders.
Three Agent Skills: replayable audit logs, read-only instruments, shadow-mode validation — observation precedes intervention. The installable how-counterpart to AAP ADR-0006/0005.
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jevify: find the AI calls in your app that are really decisions, then migrate them to JEV in shadow mode. Skills for Lovable and Claude Code, backed by the jevify MCP.
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Run a candidate detector observe-only inside a live chain, isolated from the decision and the budget
Champion/challenger pricing framework — shadow mode, SHA-256 routing, SQLite quote log, bootstrap LR test, ENBP audit (146 tests)
A diagnostic framework defining irreversible growth as the contraction of accessible future states under entropy constraints.
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Document extraction with an LLM, where the verification layer is the product. Shadow-mode registry checks that cannot alter a decision, plus a harness that measures how much a model disagrees with itself.
Marketplace refund-policy toolkit with evidence matrices, guardrails, simulator flows, and a Node shadow-mode evaluator.
claude code plugin that measures jev before trusting it: background verdicts, joined with the classifier's real denials, and a report
Shadow-mode Jev authn/authz event auditing and alert telemetry for OAuth2 and IAM
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