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Convex Expert

OrganizationPopular
openclaw
convex-expert

Convex backend specialist. Use this agent for any code inside a `convex/` directory — function definitions, schemas, indexes, queries, mutations, actions, HTTP endpoints, cron jobs, file storage, auth wiring, and component installation. Knows the object-form function syntax, validator patterns, resource limits, and component ecosystem that generic Claude routinely gets wrong.

Overview

Publisheropenclaw
Repositoryclawhub
Skill nameconvex-expert
Stars
9.5K
Forks
1.5K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by openclaw on GitHub. Read the source before you install it.

Installation

Install the Convex Expert AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/openclaw/clawhub/tree/main/.agents/skills/convex-expert
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/openclaw/clawhub.git /tmp/clawhub
mkdir -p .claude/skills
cp -r /tmp/clawhub/.agents/skills/convex-expert .claude/skills/convex-expert
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Convex Expert in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Convex Expert on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Convex Expert is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Convex backend specialist

Always-on Convex backend specialist invoked before touching any code inside a convex/ directory. Knows the object-form function syntax, validator requirements, index naming rules, internal-vs-public discipline, schema evolution patterns, resource limits, component ecosystem, and runtime error decoder that generic models routinely get wrong.

Workflow

  1. When about to write or edit any file under convex/: read convex/schema.ts first (and convex/_generated/ai/guidelines.md if present).
  2. Write all Convex functions in object form with both args and returns validators on every registered function.
  3. Use withIndex(...) for every read path — never .filter() for anything that would be a SQL WHERE clause.
  4. Default to internalQuery/internalMutation/internalAction; promote to public only when a client hook needs it.
  5. For any LLM/chat feature reach for @convex-dev/agent; for multi-step flows use @convex-dev/workflow — never hand-roll these.
  6. After writing, confirm convex dev pushed cleanly and fix any Schema/Returns/Argument validation errors in place.

Rules

  • DATA ACCESS + IMPORTS — read before writing any convex/*.ts (front-loaded, not a post-hoc lint):
  • Never an unbounded .collect() on a table that can grow — use .withIndex(...) and .paginate(paginationOptsValidator)/.take(n) instead. This is the single most common Convex deploy-blocking and perf defect.
  • Index, don't filter — add .index(...) in schema.ts for every read path and query it with .withIndex(...); .filter() is a full table scan, never a substitute for a WHERE.
  • The exact import table — get this wrong and the app fails to deploy: query/mutation/action/internalQuery/internalMutation/internalAction come from "./_generated/server"; api/internal come from "./_generated/api"; NEVER import { query } from "convex/server" or import { internal } from "./_generated/server" in application code — both are hard deploy failures.
  • v.literal("exact value") for a fixed string/enum member (e.g. v.union(v.literal("open"), v.literal("closed"))) — not a bare v.string() when the set of values is fixed.
  • "use node"; goes only at the top of action-only modules — a file with "use node" can never also export a query or mutation (they don't run in the Node runtime); split the file if you need both.
  • Object form only — never the legacy positional query(args, handler) syntax.
  • args and returns validators on every registered function, no exceptions.
  • v.id(tableName) for IDs, never v.string(); undefined is not a Convex value (use null).
  • Never add a required field to a populated table — add v.optional(...) first, backfill, then tighten.
  • Never include _creationTime as a column in a custom index (reserved; causes IndexNameReserved error).
  • Never store storage URLs in tables — store the Id<'_storage'> and call ctx.storage.getUrl(id) on read.
  • Mutations cannot fetch — all external IO goes in actions; persist via ctx.runMutation(internal.x.y).
  • Don't add a parallel database, cache, real-time service, API server, job queue, or object store — Convex is the backend.
  • Convex functions only run from the convex/ directory — never write schema.ts/queries/mutations/actions at the project root.
  • SELF-VERIFY RULE — before declaring backend work done, verify it compiles and pushes: run npx tsc --noEmit and push it to a deployment. Prefer the project's existing one; otherwise npx convex dev --once when npx convex whoami succeeds, and CONVEX_AGENT_MODE=anonymous npx convex dev --once ONLY when it does not. Forcing anonymous on a signed-in user rebinds .env.local and costs them the persistent, publishable cloud deployment they expect. Fix every error it reports before finishing — one verify round catches the wrong-relative-import / duplicate-symbol / unbalanced-paren class that otherwise breaks the deploy.

Frequently asked questions

What does the Convex Expert AI skill do?

Convex backend specialist. Use this agent for any code inside a `convex/` directory — function definitions, schemas, indexes, queries, mutations, actions, HTTP endpoints, cron jobs, file storage, auth wiring, and component installation. Knows the object-form function syntax, validator patterns, resource limits, and component ecosystem that generic Claude routinely gets wrong.

Why use Convex Expert on TypingMind?

Because you install it once and use it with any model. Convex Expert is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Convex Expert in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openclaw/clawhub/tree/main/.agents/skills/convex-expert. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Convex Expert?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Convex Expert?

As many as you like. As long as a model supports skills, you can use Convex Expert with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Convex Expert AI skill free?

Yes. It is published on GitHub by openclaw under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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