Convex backend development guidelines. Use when writing Convex functions, schemas, queries, mutations, actions, or any backend code in a Convex project. Triggers on tasks involving Convex database operations, real-time subscriptions, file storage, or serverless functions.
npx skills add https://github.com/CloudAI-X/claude-workflow-v2 --skill convex-backend
Comprehensive guide for building Convex backends with TypeScript. Covers function syntax, validators, schemas, queries, mutations, actions, scheduling, and file storage.
Reference these guidelines when:
| Category | Impact | Description |
| ----------------- | -------- | --------------------------------------------- |
| Function Syntax | CRITICAL | New function syntax with args/returns/handler |
| Validators | CRITICAL | Type-safe argument and return validation |
| Schema Design | HIGH | Table definitions, indexes, system fields |
| Query Patterns | HIGH | Efficient data fetching with indexes |
| Mutation Patterns | MEDIUM | Database writes, patch vs replace |
| Action Patterns | MEDIUM | External API calls, Node.js runtime |
| Scheduling | MEDIUM | Crons and delayed function execution |
| File Storage | LOW | Blob storage and metadata |
// Public functions (exposed to clients)
import { query, mutation, action } from "./_generated/server";
// Internal functions (only callable from other Convex functions)
import {
internalQuery,
internalMutation,
internalAction,
} from "./_generated/server";
export const myFunction = query({
args: { name: v.string() },
returns: v.string(),
handler: async (ctx, args) => {
return "Hello " + args.name;
},
});
| Type | Validator | Example |
| -------- | --------------------------------- | ------------- |
| String | v.string() | "hello" |
| Number | v.number() | 3.14 |
| Boolean | v.boolean() | true |
| ID | v.id("tableName") | doc._id |
| Array | v.array(v.string()) | ["a", "b"] |
| Object | v.object({...}) | {name: "x"} |
| Optional | v.optional(v.string()) | undefined |
| Union | v.union(v.string(), v.number()) | "x" or 1 |
| Literal | v.literal("status") | "status" |
| Null | v.null() | null |
// Public functions
import { api } from "./_generated/api";
api.example.myQuery; // convex/example.ts → myQuery
// Internal functions
import { internal } from "./_generated/api";
internal.example.myInternalMutation;
// Schema
messages: defineTable({...}).index("by_channel", ["channelId"])
// Query
await ctx.db
.query("messages")
.withIndex("by_channel", (q) => q.eq("channelId", channelId))
.order("desc")
.take(10);
args and returns validators on all functionsv.null() for void returns - never omit return validatorwithIndex() not filter() - define indexes in schemainternalQuery/Mutation/Action for private functionsctx.db - use runQuery/runMutation insteadFor the complete guide with all rules and detailed code examples, see AGENTS.md.
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
Fast CLI/Python queries to 20+ bioinformatics databases. Use for quick lookups: gene info, BLAST searches, AlphaFold structures, enrichment analysis. Best for interactive exploration, simple queries. For batch processing or advanced BLAST use biopython; for multi-database Python workflows use bioservices.
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications. Use when: bullmq, bull queue, redis queue, background job, job queue.
Create custom external web service APIs for Moodle LMS. Use when implementing web services for course management, user tracking, quiz operations, or custom plugin functionality. Covers parameter validation, database operations, error handling, service registration, and Moodle coding standards.
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
Take cloudai-x/convex-backend from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.