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    🔁 Automation Acceptance Replay Harness

    by JustHandled Labs

    1

    Replay automation traces against explicit rules for required steps, order, retries, approvals, idempotency, external side effects, duration, and terminal success.

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    You say

    Contract: required ingest,classify,approve,send,record; email_sent requires idempotency and prior human_review; max retries 2. Trace: email_sent succeeded twice with side_effect_key email:lead-99; first send had no idempotency key; no approved review event; classification failed; terminal completed.

    Your agent does

    Recommendation: REJECT Critical: duplicate successful email side effect. Critical: missing idempotency key and human approval. Critical: terminal completed after required classification failed. High: required approval and record steps missing; retry limit exceeded.

    What you get

    Replay production-like executions before approving an automation releaseCatch duplicate emails, writes, charges, or other side effects across retriesVerify configured human approval occurs before a gated actionCreate a portable client acceptance record after workflow changes

    About this skill

    Automation Acceptance Replay Harness tests exported workflow or agent execution traces against a declared operational acceptance contract. Define required and ordered steps, allowed external side effects, idempotency requirements, actions that need prior human approval, retry limits, duration limits, and accepted terminal states. The included deterministic local helper then finds missing or out-of-order steps, duplicate successful effects, absent idempotency keys, unapproved actions, missing approval, retry overflow, duration breaches, and false-success executions. It returns findings, per-execution dispositions, a JSON recommendation, and a client-ready review memo without executing the workflow or accessing connectors.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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    Creator

    JustHandled Labs builds focused agent skills for the work nobody wants to do by hand. Each one is a single repeatable job done well: catching the security and data mistakes that quietly ship, keeping docs and tests honest, gating the commands an agent is about to run, sharpening writing, and handling the founder chores around launches, outreach, and brand setup. Not generic AI productivity. Specific workflows that are easy to run, review, and repeat. Maintained by H.J. Westerfield, with a background in communications, editing, project coordination, customer support, and practical AI systems. Tools for people who want useful automation without theatrical complexity.

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