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Convex Quickstart

OrganizationPopular
openclaw
convex-quickstart

Get a barebones Convex + web template running from a one-sentence idea.

Overview

Publisheropenclaw
Repositoryclawhub
Skill nameconvex-quickstart
Stars
9.5K
Forks
1.5K
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Convex Quickstart 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-quickstart
  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-quickstart .claude/skills/convex-quickstart
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Convex Quickstart 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 Quickstart 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 Quickstart 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.

Quickstart: a barebones Convex template, running

Stand up a barebones Next.js + Convex template from the idea, locally, with an anonymous dev deployment. Minimal by design: local dev servers, no publish step, no pre-baked auth.

Workflow

  1. Run recipe quickstart-recipe@^2 with {idea, template} (the pack fetches + caches it; pinned offline fallback). It creates the project, installs deps, starts the backend (anonymous) and the web dev server.
  2. When it prints the dev URL, open it for the user.
  3. Present a short plan and CONFIRM before building features beyond the template.

Rules

  • Never re-run the recipe if it already reported success.
  • Delegate any code under convex/ to the convex-expert capability.
  • Don't add Postgres/Redis/Express — use Convex primitives.
  • Don't add hosting/publish or pre-baked auth here — keep the template minimal unless the user asks for more.
  • DEGRADATION RULE — if the scaffold cannot run (non-interactive session, no network, a sandboxed temp dir, or the user just wants code, not an app): skip the recipe and write a standard Convex project directly. ALL backend code goes under convex/ (schema.ts, functions) — NEVER at the project root; Convex functions only run from the convex/ directory. Write ZERO scaffold/documentation files (no START_HERE.md, ARCHITECTURE.md, MANIFEST.txt, README walls) unless explicitly asked. "Build me a backend" means code, not ceremony.
  • Data access + imports — before writing any convex/*.ts: never an unbounded .collect() on a table that can grow — use .withIndex(...) and .paginate(...)/.take(n). Use an index, not .filter(), for anything that would be a SQL WHERE. .withIndex(...) callbacks only have eq/gt/gte/lt/lte — there is no .range(...) method. Imports: query/mutation/action/internalQuery/internalMutation/internalAction come from ./_generated/server; api/internal come from ./_generated/api; NEVER import from convex/server in application code. v.literal("exact value") for fixed string/enum members, not a bare string. "use node" only at the top of action-only modules — never in a file that also exports a query or mutation. Never import a Node builtin (crypto/fs/path/http/child_process/os, with or without the node: prefix) into a file lacking "use node" — including http.ts route handlers; use Web Crypto (crypto.subtle) instead of importing crypto where possible.
  • Reserved names — never export const <jsReservedWord> = ... (e.g. delete, new, class, function, return) as a query/mutation/action export name; esbuild fails to parse it. Never a table or index name starting with _ (e.g. _migrations: defineTable(...)) — _ is reserved and errors at push as TableNameReserved/IndexNameReserved.
  • HTTP routes — httpRouter has no Express-style :param segments (path: "/users/:id" only matches that literal string and is dead code); use pathPrefix and parse the trailing segment yourself. Every http.route({...}) handler: must be wrapped in httpAction(...) from ./_generated/server — a bare async (ctx, request) => {...} type-checks but isn't a valid HTTP action.
  • ctx.runQuery/ctx.runMutation/ctx.runAction need a codegen'd function reference (api.foo.bar/internal.foo.bar), never a raw imported module member (import * as queries from "./queries"; ctx.runQuery(queries.getX, ...) compiles but fails at runtime).
  • 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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Convex Quickstart AI skill do?

Get a barebones Convex + web template running from a one-sentence idea.

Why use Convex Quickstart on TypingMind?

Because you install it once and use it with any model. Convex Quickstart 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 Quickstart in TypingMind?

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

Which AI models can use Convex Quickstart?

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 Quickstart?

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

Is the Convex Quickstart 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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