ai-auditing-code
Audit Your DSPy Code
When NOT to use this skill:
- Code is crashing or throwing errors -> use
/ai-fixing-errorsfirst - Need to measure or improve accuracy -> use
/ai-improving-accuracy - Have not built anything yet -> use
/ai-kickoffor/ai-choosing-architecture - Want to learn a specific DSPy API -> use the matching
/dspy-*skill
Step 1: Ask 2 questions
Before reading any code, ask:
- Point me to the code — which files contain your DSPy code?
- What are your concerns? — general review, a specific worry, or a pre-launch check?
Do not proceed until you have answers to both.
More from lebsral/dspy-programming-not-prompting-lms-skills
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50ai-do
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Pull structured data from messy text using AI. Use when parsing invoices, extracting fields from emails, scraping entities from articles, converting unstructured text to JSON, extracting contact info, parsing resumes, reading forms, pulling data from transcripts (VTT, LiveKit, Recall), extracting fields from Langfuse traces, or any task where messy text goes in and clean structured data comes out. Also use when emails are messy and lack structure, or structured data extraction from unstructured content is unreliable., extract entities from text, parse PDF with AI, structured extraction from unstructured text, OCR plus AI extraction, convert email to structured data, pull fields from documents automatically, AI data entry automation, invoice parsing, resume parsing with AI, medical record extraction.
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Make AI solve hard problems that need planning and multi-step thinking. Use when your AI fails on complex questions, needs to break down problems, requires multi-step logic, needs to plan before acting, gives wrong answers on math or analysis tasks, or when a simple prompt is not enough for the reasoning required. Covers ChainOfThought, ProgramOfThought, MultiChainComparison, and Self-Discovery reasoning patterns in DSPy., AI gives shallow answers, LLM does not think before answering, chain of thought prompting, make AI show its work, AI fails at math, complex analysis with LLM, multi-step problem solving, AI reasoning errors, LLM logic mistakes, think step by step DSPy, AI cannot do basic arithmetic, deep reasoning with language models, self-consistency for better answers, tree of thought.
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