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Found 486 Skills
Feishu Calendar (calendar): Provides comprehensive management capabilities for calendars and schedules (meetings). Core scenarios include: viewing/searching schedules, creating/updating schedules, managing attendees, checking free/busy status, and recommending available time slots. For high-frequency operations, prioritize using Shortcuts: +agenda (quick overview of today's/upcoming schedules), +create (create a schedule and invite attendees as needed), +freebusy (check the free/busy status of the user's primary calendar and RSVP status), +suggestion (provide multiple time recommendation solutions for appointment schedule requests with undetermined times).
Audit and optimize Convex application performance, covering hot path reads, write contention, subscription cost, and function limits. Use when a Convex feature is slow, reads too much data, writes too often, has OCC conflicts, or needs performance investigation.
Use this skill when encountering errors, bugs, performance issues, or unexpected behavior in an InsForge project — from frontend SDK errors to backend infrastructure problems. Trigger on: SDK returning error objects, HTTP 4xx/5xx responses, edge function failures or timeouts, slow database queries, authentication/authorization failures, realtime channel issues, backend performance degradation (high CPU/memory/slow responses), edge function deploy failures, or frontend Vercel deploy failures. This skill guides diagnostic command execution to locate problems; it does not provide fix suggestions.
Write or rewrite technical text with the rules of ASD-STE100 Simplified Technical English so it is clear, unambiguous, and free of AI slop. Use for documentation, READMEs, runbooks, procedures, error messages, release notes, incident reports, and API guides. Also use when the user says "STE", "Simplified Technical English", "ASD-STE100", "de-slop", "make this readable", "write for non-native readers", or asks for docs that translate well. Enforces the standard's 53 rules: 20/25-word sentence limits, one word one meaning, simple tenses, active voice, condition before command.
Optimize web performance for faster loading and better user experience. Use when asked to "speed up my site", "optimize performance", "reduce load time", "fix slow loading", "improve page speed", or "performance audit".
Remove AI-generated code slop from the current branch. Use after writing code to clean up unnecessary comments, defensive checks, and inconsistent style.
Optimizes a React Native app by profiling first to find real bottlenecks, then sweeping for mechanical issues. Entry-point for all performance work. Use when the app feels slow, user asks to optimize, fix re-renders, reduce jank, or improve startup. Delegates to argent-react-native-profiler for measurement.
Profile a React Native Hermes app to measure re-render and CPU performance using argent profiler tools. Use when optimizing for performance, measuring before/after a fix, spotting slow components, diagnosing re-renders, checking CPU hotspots, or producing a ranked issue report.
Use for anything related to EAS Observe — adding `expo-observe` to an Expo project (AppMetricsRoot/ObserveRoot HOC, markInteractive, the useObserve hook, and the Expo Router / React Navigation integrations for per-route metrics), querying via the EAS CLI (`eas observe:metrics-summary`, `observe:metrics`, `observe:routes`, `observe:events`, `observe:versions`), or interpreting the resulting metrics (cold/warm launch, TTR, TTI, navigation cold/warm TTR, update download, and the TTI frameRate params for triaging slow startups).
Inspect and profile React Native component trees from agent-device. Use when debugging React Native props, state, hooks, render causes, slow components, excessive re-renders, or questions like why a component re-rendered.
Query resource usage metrics for Railway services. Use when user asks about resource usage, CPU, memory, network, disk, or service performance like "how much memory is my service using" or "is my service slow".
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.