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Convex Explain App

OrganizationPopular
openclaw
convex-explain-app

Explain an existing Convex app — data model + relationships, public vs internal functions, auth/ownership model, components, a request→data flow — read from the schema and function surface. Read-only.

Overview

Publisheropenclaw
Repositoryclawhub
Skill nameconvex-explain-app
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 Explain App 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-explain-app
  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-explain-app .claude/skills/convex-explain-app
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Convex Explain App 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 Explain App 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 Explain App 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.

Explain this Convex app

Before you can safely change an app you have to know what it is — and reading 15 function files top-to-bottom is slow and error-prone. This capability produces the map fast and accurately by reading the two sources that can't lie: the schema (the data model) and the function surface (functionSpec / the exported queries/mutations/actions). It is deliberately DESCRIPTIVE — it explains what IS, hands judgment to the audit capabilities and changes to the fixers. It is also the natural first step of an optimize or self-heal session, and the reusable 're-explain the current architecture' that 'change what you built' depends on.

Workflow

  1. DETECT the app: the convex/ directory, schema.ts, and whether a deployment exists (if one does, functionSpec/tables via the official MCP give the authoritative live surface; if not, read the source directly). deploy-guard classifies any deployment read as read-only.
  2. DATA MODEL: from schema.ts, list every table with its fields and, crucially, its RELATIONSHIPS — which v.id("other") fields point where, and which indexes exist (indexes reveal the intended access paths). Draw the foreign-key graph in words: 'tasks belong to projects (projectId) and users (ownerId); messages belong to conversations'.
  3. FUNCTION SURFACE: enumerate every exported function, split PUBLIC (query/mutation/action — the attack/API surface) from INTERNAL (internalQuery/... — not client-reachable), and for each give a one-line 'what it does + what it touches'. The public/internal split is the single most important thing a newcomer needs and the thing source-skimming most often gets wrong.
  4. AUTH / OWNERSHIP MODEL: state how identity is established (auth.config.ts provider? a users table keyed by tokenIdentifier?) and how ownership is enforced (is there a requireOwner-style check? which field is the owner?). Say plainly if there is NO auth foundation — that is load-bearing context for anyone about to change the app. (Describe the model; do not audit it for holes — that's convex-authz.)
  5. COMPONENTS + EXTERNAL EDGES: list the @convex-dev/* components installed (convex.config.ts) and what they provide, the HTTP routes (http.ts) and crons, and any external calls in actions (which APIs, which env vars).
  6. FLOW: trace 1-2 representative end-to-end paths ('client calls createTask → validates → inserts into tasks scoped to the caller → listMyTasks reads it back by the by_owner index') so the reader sees the moving parts connected, not just catalogued.
  7. PRESENT as a scannable map (data model → public/internal functions → auth model → components/edges → a flow or two), accurate to the source. End by pointing at the next verbs: convex-reviewer/convex-authz to audit it, launch-readiness to score it, design/convex-expert to extend it. Never invent behavior the source doesn't show; if something is ambiguous, say so rather than guessing.

Rules

  • Read the schema + function surface (functionSpec/source) as the source of truth — never describe behavior the code doesn't show; flag ambiguity instead of guessing.
  • Lead with the two things a newcomer most needs and skimming most often gets wrong: the data-model relationship graph and the public-vs-internal function split.
  • State the auth/ownership model plainly, including 'there is no auth foundation' when that's the case — but DESCRIBE it; auditing it for holes is convex-authz's job.
  • Descriptive, not evaluative: explain-app maps what IS and hands judgment to the audit capabilities and changes to the fixers.
  • Read-only: any deployment introspection is read-only (deploy-guard); the app is not modified.
  • End by pointing at the right next verb (audit → reviewer/authz, score → launch-readiness, extend → design/expert).

Frequently asked questions

What does the Convex Explain App AI skill do?

Explain an existing Convex app — data model + relationships, public vs internal functions, auth/ownership model, components, a request→data flow — read from the schema and function surface. Read-only.

Why use Convex Explain App on TypingMind?

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

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

Which AI models can use Convex Explain App?

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 Explain App?

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

Is the Convex Explain App 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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