convex-performance-audit 是做什麼的？
審查 Convex 效能，包括讀取、訂閱、寫入競爭及函數限制。適用於功能緩慢、洞察發現、OCC 衝突或讀取放大等情況。
審查 Convex 效能，包括讀取、訂閱、寫入競爭及函數限制。適用於功能緩慢、洞察發現、OCC 衝突或讀取放大等情況。
此技能用於對 Convex 後端應用進行全面的效能審查。它可以診斷常見問題，如讀取放大（read amplification）、訂閱過度觸發、寫入競爭（write contention）以及 OCC 衝突（Optimistic Concurrency Control）。支援 Convex 平台，使用者可透過分析 Insights 數據來找出效能瓶頸。技能會根據 SKILL.md 文件中的指南，產生結構化的審查報告，協助開發者最佳化查詢、索引和函數的呼叫限制。
npx skills add https://github.com/get-convex/agent-skills/tree/main/skills/convex-performance-auditInstalled? Explore more 寫作與編輯 skills: steipete/notion, langchain-ai/langchain, bytedance/podcast-generation · View all 6 →
審查 Convex 效能，包括讀取、訂閱、寫入競爭及函數限制。適用於功能緩慢、洞察發現、OCC 衝突或讀取放大等情況。
This repo contains 5 individual skills — each has its own dedicated page.
Routes general Convex requests to the right project skill. Use when the user asks which Convex skill to use or gives an underspecified Convex app task.
Builds reusable Convex components with isolated tables and app-facing APIs. Use for new components, reusable backend modules, integrations, or component boundary work.
Plans Convex schema and data migrations with widen-migrate-narrow and @convex-dev/migrations. Use for breaking schema changes, backfills, table reshaping, or zero-downtime rollouts.
Creates or adds Convex to an app. Use for new Convex projects, npm create convex@latest, frontend setup, env vars, or the first npx convex dev run.
Sets up Convex auth, identity mapping, and access control. Use for login, auth providers, users tables, protected functions, or roles in a Convex app.
Notion CLI/API for pages, Markdown content, data sources, files, comments, search, Workers, and raw API calls.
The agent engineering platform.
Use this skill when the user requests to generate, create, or produce podcasts from text content. Converts written content into a two-host conversational podcast audio format with natural dialogue.
Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.
Extracts and analyzes competitors' ads from ad libraries (Facebook, LinkedIn, etc.) to understand what messaging, problems, and creative approaches are working. Helps inspire and improve your own ad campaigns.
Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors