waynesutton/convex-agents
Building AI agents with the Convex Agent component including thread management, tool integration, streaming responses, RAG patterns, and workflow orchestration
Building AI agents with the Convex Agent component including thread management, tool integration, streaming responses, RAG patterns, and workflow orchestration
npx skills add https://github.com/waynesutton/convexskills/tree/main/skills/convex-agentsBuilding AI agents with the Convex Agent component including thread management, tool integration, streaming responses, RAG patterns, and workflow orchestration
This repo contains 13 individual skills — each has its own dedicated page.
Prevent feature creep when building software, apps, and AI-powered products. Use this skill when planning features, reviewing scope, building MVPs, managing backlogs, or when a user says "just one more feature." Helps developers and AI agents stay focused, ship faster, and avoid bloated products.
Umbrella skill for all Convex development patterns. Routes to specific skills like convex-functions, convex-realtime, convex-agents, etc.
Guidelines for building production-ready Convex apps covering function organization, query patterns, validation, TypeScript usage, error handling, and the Zen of Convex design philosophy
How to create, structure, and publish self-contained Convex components with proper isolation, exports, and dependency management
Scheduled function patterns for background tasks including interval scheduling, cron expressions, job monitoring, retry strategies, and best practices for long-running tasks
Complete file handling including upload flows, serving files via URL, storing generated files from actions, deletion, and accessing file metadata from system tables
Writing queries, mutations, actions, and HTTP actions with proper argument validation, error handling, internal functions, and runtime considerations
External API integration and webhook handling including HTTP endpoint routing, request/response handling, authentication, CORS configuration, and webhook signature validation
Schema migration strategies for evolving applications including adding new fields, backfilling data, removing deprecated fields, index migrations, and zero-downtime migration patterns
Patterns for building reactive apps including subscription management, optimistic updates, cache behavior, and paginated queries with cursor-based loading
Defining and validating database schemas with proper typing, index configuration, optional fields, unions, and migration strategies for schema changes
Deep security review patterns for authorization logic, data access boundaries, action isolation, rate limiting, and protecting sensitive operations
Quick security audit checklist covering authentication, function exposure, argument validation, row-level access control, and environment variable handling
面向建筑行业知识工作从业者与 AI 研发团队的 Skills、Workflow 与多 Agent 工具包 / Building-industry AI agent skills for BIM, IFC, RAG, GraphRAG, project evidence work, code review, and runtime governance.
Set up a new Obsidian knowledge base with the LLM Wiki pattern. Use when the user wants to create a second brain, initialize a vault, set up a personal knowledge base, or says "onboard". Guides through an interactive wizard to configure vault name, location, domain, agent support, and tooling.
Records decisions and documentation. Use when making architectural decisions, changing public APIs, shipping features, or when you need to record context that future engineers and agents will need to understand the codebase.
Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors). Triggers on: create S3 vector bucket, vector index, store embeddings, semantic search, RAG vector storage, similarity search, vector database, migrate from other vector databases. Do NOT use for: querying tabular data (use querying-data-lake), S3 object storage, or hundreds/thousands of sustained QPS (use OpenSearch).
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep watching open PRs so fresh review feedback is surfaced promptly. Use when the user asks Codex to monitor a PR, watch CI, handle review comments, or keep an eye on failures and feedback on an open PR.
Local skill manager for AI coding agents. One SKILL.md, projected across Claude Code, Cursor, Codex, Gemini. Lockfile-pinned, project-scoped, MIT.