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ai-engineering

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Diagnose or improve reliability of a structured, multi-request, rate-limited, or cost-sensitive AI workflow. Use for malformed or truncated outputs, retry/rate-limit failures, unreliable fan-out, cache/resume bugs, or missing run telemetry. Do not use for ordinary prompt edits or simple one-shot model calls.

ai-engineering/SKILL.md

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Nov 9, 2026
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# AI Engineering

Use enough structure to make a model workflow explainable and recoverable without
turning every prototype into an operations project. This skill owns request
validation, retry/cache behavior, rate-aware fan-out, and measurement. Pair it
with `live-ai-pipelines` only when a run needs live progress or durable resume.

For provider-specific API behavior, read [provider operation notes](references/provider-operation-notes.md) when the adapter is OpenAI, Anthropic, or OpenRouter, then verify volatile details in current provider docs. For image/video generation or FAL model fan-outs, read [image, video, and FAL operation notes](references/image-video-fal.md).

## Useful defaults

- Improve the established workflow before introducing another path. Do not turn implementation choices or temporary exceptions into user requirements.
- Do not publish a malformed, partial, or schema-invalid result as complete.
- For a machine-consumed structured artifact, use the selected model provider's documented native structured-output API with an explicit schema. Do **not** treat a free-form completion prompted with “return JSON” plus local `JSON.parse` as structured output.
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