Update RLXP study reports from structured experiment evidence.
npx skills add https://github.com/junhyekh/rlxp --skill rlxp-update-report
Please help me install this Agent Skill. Skill: rlxp-update-report Source: https://github.com/junhyekh/rlxp/tree/main/plugins/rl-experiment-assistant/skills/rlxp-update-report Command: npx skills add https://github.com/junhyekh/rlxp --skill rlxp-update-report
This Skill turns structured experiment evidence into a clear, auditable RLXP study report so teams can track progress, decisions, risks, and stop conditions without inventing results.
Use the rlxp-update-report skill to refresh the study report from the latest contract, ledger, metrics, and audit evidence.
High-intent search queries and answers about installing and using this skill.
You can generate an auditable reinforcement learning study report by consolidating structured experiment evidence such as contracts, ledgers, and metric tables into a unified document tracking incumbent status, guardrails, and budget usage.
Evidence-based RLXP experiment reporting consolidates approved run summaries, candidate evaluations, and audits into a consistent report, ensuring stakeholders can review experiment states without inventing metrics or results.
You track stop decisions and guardrails by refreshing the study report with current budget usage, monitor status, and audit evidence, ensuring the experiment state and next-action recommendations are accurately reflected.
Yes, you can update baseline reviews and candidate evaluations using only approved experiment and metric tables, ensuring the report refreshes from structured evidence without fabricating data or inventing metrics.
Refreshing an RLXP study report requires incumbent tracking, experiment and metric tables, guardrails, budget usage, open risks, queue state, and monitor status to generate accurate next-action recommendations.
A key limitation is that automated reporting cannot invent metrics; it strictly requires structured evidence like ledgers and analyses to function, meaning missing input data limits the report's completeness.
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