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Convex Verify

OrganizationPopular
openclaw
convex-verify

Prove a Convex feature works — seed, drive as multiple mocked users via convex-test, assert behavior including the negative authz cases (wrong user refused, data-scope enforced).

Overview

Publisheropenclaw
Repositoryclawhub
Skill nameconvex-verify
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 Verify 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-verify
  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-verify .claude/skills/convex-verify
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Convex Verify 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 Verify 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 Verify 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.

Prove a feature works — seed, drive, assert

A green typecheck proves the code parses; it does not prove a non-owner is actually denied, that a query returns the right rows, or that a mutation has the effect it claims. This capability closes that gap with the loop the whole field is missing: seed → drive → assert, run in-process with convex-test so it needs no deployment. Its highest-value assertions are the NEGATIVE ones — the caller who should be refused — because those are exactly the authz defects the 30-app corpus shows are the #1 real bug and the ones a happy-path demo never catches.

Workflow

  1. IDENTIFY the feature to prove: the specific exported query/mutation/action (or a small set) the user just built/changed, and its intended behavior — who should be allowed, what data should come back, what a mutation should change. If the intent is unstated, ask one focused question rather than guessing the contract.
  2. SET UP convex-test: ensure convex-test + vitest are dev deps AND a vitest.config.ts sets test.environment: "edge-runtime" with server.deps.inline: ["convex-test"] — WITHOUT that config, convexTest(schema) fails at runtime with import.meta.glob is not a function (verified). Also install @edge-runtime/vm. Then convexTest(schema) gives a t handle. Reuse the project's existing test setup if present (compose with the test capability, don't fork it).
  3. SEED realistic data through the app's OWN functions where possible (so the seed exercises the same validators/mutations a real user would), falling back to t.run(async (ctx) => ctx.db.insert(...)) for fixtures the public API can't create. Seed at least: the caller's own rows AND a second user's rows, so cross-user access is testable.
  4. DRIVE the feature as DIFFERENT identities with t.withIdentity({ subject, tokenIdentifier, ... }): call the function as (a) the legitimate owner, (b) a different authenticated user, and (c) unauthenticated (t with no identity). Use the real identity shape the app's auth uses (subject/tokenIdentifier), matching how ownership is resolved.
  5. ASSERT behavior — POSITIVE and NEGATIVE:
    • positive: the owner gets the expected rows / the mutation made the expected change (expect(await t.withIdentity(owner).query(api.x.y, args)).toEqual(...)).
    • NEGATIVE (the load-bearing half): a different user calling the same function is REFUSED — await expect(t.withIdentity(other).mutation(api.x.cancel, {id})).rejects.toThrow(/forbidden|not authorized|403/) — and an unauthenticated caller is refused where auth is required. A feature is not proven until the wrong caller is shown to be blocked.
    • data-scope: a list/query returns ONLY the caller's rows, never the second user's (assert the second user's row is absent).
  6. RUN the tests (npx vitest run) and report: what was proven (each positive + negative assertion that passed), and — critically — any assertion that FAILED, because a failed negative assertion is a real authz hole found before ship. Emit findings on the bus (specs/finding.schema.json, class authz/correctness, evidence kind probe-result with the exact failing call) for anything that didn't behave.
  7. Do NOT weaken a test to make it pass: if the owner-only query returns another user's row, the FIX is in the function (hand to convex-authz), not in the assertion. A test changed until it's green proves nothing.

Rules

  • Prove behavior, not compilation: every verification includes at least one NEGATIVE assertion (a caller who should be refused is refused) — the happy path alone is not proof.
  • Drive the feature as multiple identities with t.withIdentity (owner, other user, unauthenticated) using the app's real subject/tokenIdentifier shape.
  • Seed both the caller's rows AND a second user's rows so cross-user access and data-scope are actually testable.
  • A vitest.config.ts with environment 'edge-runtime' + convex-test inlined is REQUIRED for convex-test to run (import.meta.glob needs it); author it, don't just author the test file.
  • Run in-process with convex-test — no deployment needed; compose with the test capability's setup rather than forking it.
  • Never weaken an assertion to make it pass: a failing negative test is a real defect → hand the fix to convex-authz/convex-expert, don't edit the test until it's green.
  • Emit a bus finding for any assertion that failed (authz/correctness, evidence: the failing probe call) so a composite pass or self-heal can pick it up.
  • This drives a SPECIFIC built feature; a request to set up a test framework generally is the test capability.

Frequently asked questions

What does the Convex Verify AI skill do?

Prove a Convex feature works — seed, drive as multiple mocked users via convex-test, assert behavior including the negative authz cases (wrong user refused, data-scope enforced).

Why use Convex Verify on TypingMind?

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

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

Which AI models can use Convex Verify?

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 Verify?

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

Is the Convex Verify 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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