Prompts Library
Curated prompts that have proven useful in production work. Each entry includes the prompt itself, a short description, recommended models, and a tag list. Submit a prompt if you've built one worth sharing.
By category
All prompts
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Structured Data Extraction to JSON
Extracts specific fields from unstructured text into a strict JSON schema. Reliable across providers when JSON mode is on.
extraction ▲ 52
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Code Review — Specific, Severity-Graded
Reviews code with concrete fixes per finding, grouped by severity. Avoids generic "consider adding tests" suggestions.
coding ▲ 47
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Debug a Production Error
Walks through a stack trace systematically: what failed, why, and the smallest change to fix it without introducing risk.
coding ▲ 44
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Rewrite for Clarity, Keep Meaning
Rewrites a paragraph to be clearer and tighter without changing the underlying claims. Preserves voice.
writing ▲ 41
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RAG Answer — Citations Required
Answers a question from provided context with inline citations. Refuses when the context doesn't support an answer.
rag ▲ 38
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Write Unit Tests — Cover Edge Cases
Generates unit tests in {{framework}} that cover the happy path, edge cases, and error conditions. Tests are runnable, not pseudocode.
coding ▲ 35
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Intent Classification with Confidence
Routes a user message to one of N categories with a confidence score. Returns "unclear" rather than guessing when uncertain.
classification ▲ 31
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Meeting Notes — Decisions and Action Items
Summarizes meeting transcript into decisions, action items (with owners), and unresolved questions. Skips small talk.
writing ▲ 29
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Explain a SQL Query
Translates a complex SQL query into plain English step by step. Notes performance characteristics and missing indexes.
coding ▲ 26
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Technical Translation — Preserve Code and Terms
Translates technical content while preserving code blocks, API names, version numbers, and untranslatable terms.
writing ▲ 18
What makes a prompt worth saving
The prompts here aren't generic "you are a helpful assistant" starters. Each one targets a specific production use case — code review, structured extraction, RAG with grounding, classification — and includes the constraints that make it actually work at scale: explicit output schema, refusal clauses, severity grouping, citation requirements.
Submit a prompt if it meets the same bar. Include what model you used it with, what the input format looks like, and what failure modes it solves.