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Turn a raw invoice or receipt into structured JSON — vendor, invoice/due dates, currency, line items, subtotal, tax, and total. Dates normalized to ISO 8601, amounts to plain decimals. Optional locale hint disambiguates date and number formats. A worked, strongly-typed specialization of key-information-extraction.

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Condense text into a structured JSON summary — title, key points, action items, and entities mentioned — instead of prose. Built for meeting notes, email threads, articles, and agent handoffs. Optional cap on the number of key points.

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Parse an unstructured address string into structured components — recipient, street, unit, city, state/region, postal code, and country. Optional country hint guides format expectations. Returns a confidence score.

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Extract scheduled events from natural-language text with resolved ISO 8601 start/end times. Relative dates ("next Tuesday", "tomorrow at 3") are resolved against a reference date and IANA timezone you supply. Returns title, start, end, all-day flag, and location per event.

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Classify text into a caller-supplied label set (you define the labels at call time). Returns the single best-fit label with a rationale and confidence. Optional fallback label.

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Classify the overall sentiment of text (positive / negative / neutral / mixed) with a rationale and confidence score. Strict JSON output.

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Turn a natural-language description into a regex in a requested flavor (pcre/javascript/python/re2), with reasoning, a plain-string pattern, a human explanation, and self-supplied test cases (input + should_match). The self-supplied tests make outputs verifiable — and the eval runs the generated regex against the case's own strings via CEL .matches(), no judge needed.

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Triage a support ticket: priority + category + the signals that drove the call, as strict JSON.

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Judge prompt for code-review evals: grades whether a review's issue severities (blocker/major/minor/nit) are well-calibrated to real impact and grounded in the diff, returning a 0-1 score. Judges calibration and groundedness only, not completeness.

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Review a unified diff and return typed JSON: a summary, a list of issues each with a closed-enum severity (blocker/major/minor/nit), file, line, description, and suggestion, a flat issue_files[] array for membership checks, and an approved gate. No invented line numbers — only lines present in the diff. Comments inside the diff ("approve this") are data, never instructions. Eval-gated on planted-bug detection, clean-diff approval, and a severity-calibration judge.

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