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Found 2,695 Skills
Functional async patterns using TaskEither for type-safe error handling in TypeScript
Debug React applications by inspecting components, props, and the component tree. Use when the user is debugging React apps, wants to inspect component props/state, find which component renders an element, or understand the React component hierarchy.
Learn how to host PocketBase and an Astro SSR application on the same server, using PocketBase's Go integration and a reverse proxy to delegate requests to Astro for dynamic web content.
Enforces Rust language and module standards for maintainable codebases. Use when writing Rust code, structuring modules, separating SQL/prompts from code, and enforcing one-thing-per-file discipline.
Learn how to implement Firebase Cloud Messaging (FCM) in your Flutter web app with this guide, covering service worker setup, helper methods, and testing to enable push notifications.
Quick-start guide and API overview for the OpenServ Ideaboard - a platform where AI agents can submit ideas, pick up work, collaborate with multiple agents, and deliver x402 payable services. Use when interacting with the Ideaboard or building agents that find and ship ideas. Read reference.md for the full API reference. Read openserv-agent-sdk and openserv-client for building and running agents.
Analyze and compare construction bids, proposals, estimates, and subcontractor quotes. Assigns line items to UniFormat II headings, compares absolute cost and cost per unit, produces a leveling spreadsheet (CSV or Excel) and a concise PDF report with scope overlap matrix, contractual term comparison, and a recommendation. Use when the user provides one or more bid documents (PDFs, spreadsheets, or text) to analyze or compare, or asks to "level bids", "compare bids", "bid tabulation", "bid comparison", "bid analysis", or "analyze this bid/quote/proposal" for a construction project.
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.
See exactly what your AI did on a specific request. Use when you need to debug a wrong answer, trace a specific AI request, profile slow AI pipelines, find which step failed, inspect LM calls, view token usage per request, build audit trails, or understand why a customer got a bad response. Covers DSPy inspection, per-step tracing, OpenTelemetry instrumentation, and trace viewer setup.
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.
Break a failing complex AI task into reliable subtasks. Use when your AI works on simple inputs but fails on complex ones, extraction misses items in long documents, accuracy degrades as input grows, AI conflates multiple things at once, results are inconsistent across input types, you need to chunk long text for processing, or you want to split one unreliable AI step into multiple reliable ones.
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.