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Found 1,247 Skills
Master the AI tools that handle administrative work and boost personal productivity. From meeting notes to email management, get more done with less effort. Use when "meeting notes, email management, calendar optimization, productivity, time management, productivity, meetings, email, calendar, personal" mentioned.
Marketing: growth, content, brand, social, SEO, email, PR, events, video, design. Triggers: marketing campaign, social media, content strategy, SEO, email marketing, press release, brand guidelines, landing page, video content, graphic design, community management, growth marketing, paid ads, conversion optimization.
Expert full-cycle enterprise sales strategist for B2B SaaS. Use when planning sales strategy, pipeline management, deal progression, account planning, competitive displacement, or territory optimization. Covers multi-threading, executive engagement, champion development, buying committee navigation, and complex deal orchestration. Use for enterprise selling, account expansion, land-and-expand, and quota attainment.
Analyze routes and recommend whether to use Server Actions or API routes based on use case patterns including authentication, revalidation, external API calls, and client requirements. Use this skill when deciding between Server Actions and API routes, optimizing Next.js data fetching, refactoring routes, analyzing route architecture, or choosing the right data mutation pattern. Trigger terms include Server Actions, API routes, route handler, data mutation, revalidation, authentication flow, external API, client-side fetch, route optimization, Next.js patterns.
Expert prompt optimization for LLMs and AI systems. Use PROACTIVELY when building AI features, improving agent performance, or crafting system prompts. Masters prompt patterns and techniques.
Sets up comprehensive GitHub Actions CI/CD workflows for modern web applications. This skill should be used when configuring automated lint, test, build, and deploy pipelines, adding preview URL comments on pull requests, or optimizing workflow caching. Use when setting up continuous integration, deployment automation, GitHub Actions, CI/CD pipeline, preview deployments, or workflow optimization.
Measure and improve how well your AI works. Use when AI gives wrong answers, accuracy is bad, responses are unreliable, you need to test AI quality, evaluate your AI, write metrics, benchmark performance, optimize prompts, improve results, or systematically make your AI better. Covers DSPy evaluation, metrics, and optimization.
Generate synthetic training data when you don't have enough real examples. Use when you're starting from scratch with no data, need a proof of concept fast, have too few examples for optimization, can't use real customer data for privacy or compliance, need to fill gaps in edge cases, have unbalanced categories, added new categories, or changed your schema. Covers DSPy synthetic data generation, quality filtering, and bootstrapping from zero.
Fine-tune models on your data to maximize quality and cut costs. Use when prompt optimization hit a ceiling, you need domain specialization, you want cheaper models to match expensive ones, you heard "fine-tuning will make us AI-native", you have 500+ training examples, or you need to train on proprietary data. Covers DSPy BootstrapFinetune, BetterTogether, model distillation, and when to fine-tune vs optimize prompts.
Track which optimization experiment was best. Use when you've run multiple optimization passes, need to compare experiments, want to reproduce past results, need to pick the best prompt configuration, track experiment costs, manage optimization artifacts, decide which optimized program to deploy, or justify your choice to stakeholders. Covers experiment logging, comparison, and promotion to production.
Python performance optimization patterns using profiling, algorithmic improvements, and acceleration techniques. Use when optimizing slow Python code, reducing memory usage, or improving application throughput and latency.
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.