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Tier-1 strategy-consultant analysis tailored for retail / hospitality problems — same-store-sales, foot traffic, basket size, mix, store ops. Same five frameworks as the generic master, with retail-aware MECE defaults, industry vocabulary, and common root-cause patterns. Use for retail-specific problems.

Use Arize Phoenix for DSPy tracing and evaluation. Use when you want to set up Phoenix, arize-phoenix, openinference, DSPyInstrumentor, open-source trace viewer, localhost:6006, or LLM evals. Also used for phoenix setup, arize phoenix, pip install arize-phoenix, phoenix local UI, phoenix evaluations, DSPy trace viewer, open-source LLM observability, phoenix vs langtrace, openinference-instrumentat

Apply the 5 Whys technique to drill from a surface problem to a root cause. Outputs five sequentially numbered "Why?" lines with brief answers, ending in a root-cause statement. Use after an issue tree to drill the critical branch deeper, or any time the user wants to test whether the apparent cause is the actual cause.

Describe your AI problem and get routed to the right skill with a ready-to-use prompt. Use when you are not sure which ai- skill to use, want help picking the right approach, or just want to describe what you need in plain language. Also use this when someone says I want to build an AI that..., how do I make my AI..., or describes any AI/LLM task without naming a specific skill, I need AI but do n

Fix broken AI features. Use when your AI is throwing errors, producing wrong outputs, crashing, returning garbage, not responding, or behaving unexpectedly. Also use when you get Could not parse LLM output errors, DSPy program crashes, LLM timeout or rate limit errors, API key not working with DSPy, JSON parse error from LLM, model returns empty response, AI works sometimes but fails other times,

Make your AI follow rules and policies. Use when your AI breaks format rules, violates content policies, ignores business constraints, outputs invalid JSON, exceeds length limits, includes forbidden content, or does not comply with your specifications. Also use when LLM JSON output is unreliable, you get inconsistent formatting with random spaces and line breaks, or there is extraneous text and co

Tier-1 strategy-consultant analysis tailored for manufacturing / industrial-ops problems — OEE, defect rate, yield, supply chain. Same five frameworks with manufacturing-aware MECE defaults, vocabulary, and root-cause priors.

Decompose a core problem into root causes with an ASCII tree. Outputs a fenced code block using box-drawing characters. Use for root-cause analysis, drilling from a high-level problem down to testable sub-problems, or step 2 of a strategic analysis.

Tier-1 strategy-consultant analysis tailored for public-sector / civic-ops problems — backlogs, processing cycle time, citizen satisfaction, procurement. Same five frameworks with government-aware MECE defaults, vocabulary, and root-cause priors.

REMOVED IN DSPy 3.x -- use dspy.Refine or dspy.BestOfN instead (see /dspy-refine, /dspy-best-of-n). Legacy documentation for dspy.Assert and dspy.Suggest kept for existing codebases only. For new code, use dspy.Refine (iterative improvement with feedback) or dspy.BestOfN (sampling, pick best). Also used for dspy.Assert, dspy.Suggest, runtime validation for LLM output, retry on bad output, backtrac

Capacite a IA a diagnosticar como um especialista com 3 métodos inovadores e de aplicação geral, complementados por visualizações impressionantes.

Use before claiming ANY task is complete. Activates at the end of every skill execution, every deliverable generation, and every user request fulfillment. Blocks completion claims that lack enumerated evidence.