Convex Optimize

OrganizationPopular

openclaw

convex-optimize

Audit and optimize an existing Convex app: security, scale, upgrades, observability.

Overview

Publisher	openclaw
Repository	`clawhub`
Skill name	`convex-optimize`
Stars	9.5K
Forks	1.5K
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/openclaw/clawhub/tree/main/.agents/skills/convex-optimize
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.

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-optimize .claude/skills/convex-optimize

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Convex Optimize 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 Optimize 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 Optimize 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.

Audit and optimize an existing Convex app

The remediation WORKFLOW for an existing app: open with a scored assessment, then act on it — upgrade stale components and set up observability — plan-then-confirm-then-apply. The assessment itself is delegated to launch-readiness (the findings-bus scorer); optimize's distinct value is the actions it takes on the result.

Workflow

Detect the app: a convex/ directory, the schema, and whether it's an anonymous or cloud deployment.
ASSESS via launch-readiness — one scored, deduped report across authz/reviewer/advisor/insights with an ordered fix plan. Do not re-run those passes by hand; optimize consumes launch-readiness's report rather than re-implementing the audit.
UPGRADE: run check-updates against the pinned @convex-dev/* components and fold stale-component (staleness-class) findings into the same plan.
OBSERVABILITY: if the readiness report flagged an observability gap (no prod error capture), offer to install sentinel.
Present the combined prioritized plan — the launch-readiness score + the fix plan + upgrades + observability, security/data-loss first — and apply only on explicit confirmation, dispatching each fix to its fixCapability.
After applying, re-run the launch-readiness assessment and show the score delta.

Rules

Read-only first. Present a plan and CONFIRM before changing any file.
Delegate the audit to launch-readiness (the findings-bus scorer); don't re-implement reviewer/advisor/insights inline — optimize's job is acting on the report (upgrades + observability), not re-scoring.
Prioritize security and data-loss risks above style, following launch-readiness's ordering.
Never auto-land changes on someone's existing prod app; re-assess after applying and show the score moved.

Frequently asked questions

What does the Convex Optimize AI skill do?

Audit and optimize an existing Convex app: security, scale, upgrades, observability.

Why use Convex Optimize on TypingMind?

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

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

Which AI models can use Convex Optimize?

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 Optimize?

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

Is the Convex Optimize 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Audit and optimize an existing Convex app

Workflow

Rules

Frequently asked questions

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