ml-failure-audit
General workflow for auditing ML CI failures, experiment regressions, training run failures, golden metric failures, and telemetry-backed ML work-product claims from local repositories, logs, metrics, configs, and artifacts. Use when Codex needs to decide whether an ML failure is a model/convergence issue, correctness bug, data/config issue, infrastructure/runtime issue, evaluation/gating policy issue, or unsupported claim, and produce structured evidence-backed outputs.
Installation method (CLI)
Install this skill via the agent-skills CLI. See the repo README for full instructions.
Use npx @eigent-ai/agent-skills list to see all available skills.
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