ai-features_skill

Overview

This skill helps bootstrapped SaaS founders add practical, revenue-driving AI features without over-engineering. It focuses on integrating LLM APIs, designing prompt-driven product features, managing costs, and shipping maintainable AI that non-technical founders can run. The guidance prioritizes simplicity, measurable user value, and graceful degradation when APIs fail.

How this skill works

The skill prescribes proven feature patterns (smart drafts, summarization, categorization, RAG search, recommendations) and maps each to an API-first implementation plan. It recommends model selection by cost/quality, explicit system prompts as product specs, caching, rate limits, and fallbacks. It also includes cost-estimation formulas, deployment checklists, and prompt-versioning best practices to iterate safely.

When to use it

• When users perform repetitive tasks that can be automated (drafts, tagging, routing)
• When users need interpretable analysis or summaries of collected data
• When AI can save frequent, high-value user time and improve core product outcomes
• When competitors offer AI features and parity or differentiation is needed
• When you can add a lightweight RAG system to let users query their own data

Best practices

• Start with the cheapest model that meets quality requirements and only upgrade per feature
• Design prompts as product specs: role, context, rules, exact output format
• Cache responses and limit output tokens to control cost
• Show loading state, handle errors, and provide a manual fallback mode
• Rate limit per user and per plan tier; monitor spend and set billing alerts
• Allow users to edit/override AI output and track edit rates as a quality signal

Example use cases

• Smart draft generator for emails, reports, or product descriptions with regenerate/edit flow
• Summaries of meeting notes or customer feedback displayed as cards with cached results
• Automatic categorization/tagging of tickets or content using a low-cost model with override option
• RAG-powered Q&A over a customer’s documents: embed, retrieve top chunks, send context to LLM with citations
• AI-driven recommendations suggesting next actions or features based on user behavior and past data