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Found 45 Skills
Build a conversational AI assistant with memory and state. Use when you need a customer support chatbot, helpdesk bot, onboarding assistant, sales qualification bot, FAQ assistant, or any multi-turn conversational AI. Powered by DSPy for response quality and LangGraph for conversation state management.
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 doesn't comply with your specifications. Covers DSPy Assert/Suggest for hard and soft rules, content policies, format enforcement, retry mechanics, and composing multiple constraints.
Fix broken AI features. Use when your AI is throwing errors, producing wrong outputs, crashing, returning garbage, not responding, or behaving unexpectedly. Covers DSPy debugging, error diagnosis, and troubleshooting.
Verify and validate AI output before it reaches users. Use when you need guardrails, output validation, safety checks, content filtering, fact-checking AI responses, catching hallucinations, preventing bad outputs, quality gates, or ensuring AI responses meet your standards before shipping them. Covers DSPy assertions, verification patterns, and generate-then-filter pipelines.
Chain multiple AI steps into one reliable pipeline. Use when your AI task is too complex for one prompt, you need to break AI logic into stages, combine classification then generation, do multi-step reasoning, build a compound AI system, orchestrate multiple models, or wire AI components together. Powered by DSPy multi-module pipelines.
Scaffold a new AI feature powered by DSPy. Use when adding AI to your app, starting a new AI project, building an AI-powered feature, setting up a DSPy program from scratch, or bootstrapping an LLM-powered backend.
Condense long content into short summaries using AI. Use when summarizing meeting notes, condensing articles, creating executive briefs, extracting action items, generating TL;DRs, creating digests from long threads, summarizing customer conversations, or turning lengthy documents into bullet points. Powered by DSPy summarization.
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 isn't enough for the reasoning required. Covers ChainOfThought, ProgramOfThought, MultiChainComparison, and Self-Discovery reasoning patterns in DSPy.
Know when your AI breaks in production. Use when you need to monitor AI quality, track accuracy over time, detect model degradation, set up alerts for AI failures, log predictions, measure production quality, catch when a model provider changes behavior, build an AI monitoring dashboard, or prove your AI is still working for compliance. Covers DSPy evaluation for ongoing monitoring, prediction logging, drift detection, and alerting.
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, or any task where messy text goes in and clean structured data comes out. Powered by DSPy extraction.
Auto-moderate what users post on your platform. Use when you need content moderation, flag harmful comments, detect spam, filter hate speech, catch NSFW content, block harassment, moderate user-generated content, review community posts, filter marketplace listings, or route bad content to human reviewers. Covers DSPy classification with severity scoring, confidence-based routing, and Assert-based policy enforcement.
Reduce your AI API bill. Use when AI costs are too high, API calls are too expensive, you want to use cheaper models, optimize token usage, reduce LLM spending, route easy questions to cheap models, or make your AI feature more cost-effective. Covers DSPy cost optimization — cheaper models, smart routing, per-module LMs, fine-tuning, caching, and prompt reduction.