Convex Deploy Guard

OrganizationPopular

openclaw

convex-deploy-guard

Classify + announce the target Convex deployment before any deployment-affecting command; fresh explicit consent for prod actions; session read-only mode.

Overview

Publisher	openclaw
Repository	`clawhub`
Skill name	`convex-deploy-guard`
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 Deploy Guard 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-deploy-guard
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-deploy-guard .claude/skills/convex-deploy-guard

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

Use it in TypingMind

Enable Convex Deploy Guard 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 Deploy Guard 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 Deploy Guard 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.

Deployment target guard

Deployments are not interchangeable, and most incidents start with a command aimed at the wrong one. Every Convex project has several (personal dev, preview, prod — often across multiple projects on one machine). This guard is the standing discipline: identify, announce, then act — and treat prod as consent-gated, per action, per session.

Workflow

IDENTIFY before you act: read CONVEX_DEPLOYMENT in .env.local, convex.json, and whether CONVEX_DEPLOY_KEY is set; or call the official Convex MCP status tool. Classify the target: local-anonymous | dev | preview | prod. If two sources disagree, resolve before proceeding.
ANNOUNCE in one line before any deployment-affecting command: target: dev (joyful-capybara-123, personal dev). Never run the command in the same breath as discovering the target — announce first.
PROD needs a FRESH explicit yes: before npx convex deploy (when it resolves to prod), npx convex run --prod, env set on prod, snapshot import/export on prod, or starting the MCP with prod access — state exactly what will change on which deployment and get an explicit yes in THIS session. A yes given earlier, or for a different target, does not carry.
MCP safety defaults: start the official MCP scoped non-prod (--deployment dev). The two prod flags are DIFFERENT risk levels — keep them split: a read-only prod audit (advisor/insights reading data/logs/insights) passes ONLY --cautiously-allow-production-pii (read tools); --dangerously-enable-production-deployments (which enables MUTATING prod tools) stays OFF unless the user explicitly asked to CHANGE prod this session. Never pair them by default — 'look at prod' must not silently grant 'mutate prod'.
READ-ONLY session mode: when the user says 'read-only' / 'don't change anything', honor it absolutely for the rest of the session — no deploy, no env set/remove, no mutations via run, no imports; start the MCP with --disable-tools run,envSet,envRemove.
Wrong-deployment diagnosis: when a deploy 'didn't change anything', do NOT re-deploy harder. Re-run step 1 — the deploy almost certainly landed on a different deployment than the one being observed.
Ambiguity = stop: if you cannot determine which deployment a command will hit, find out (status tool; compare npx convex env list fingerprints) — never guess.

Rules

Classify and announce the target BEFORE every deployment-affecting command — identification and action are two separate steps.
Prod consent is per-action, per-target, per-session: state what changes where, get a fresh explicit yes.
Keep the two prod MCP flags split by risk: --cautiously-allow-production-pii (read-only) for an audit; --dangerously-enable-production-deployments (mutating) only when the user explicitly asks to change prod. Both are user-spoken-only; default every MCP start to a non-prod deployment selector.
Read-only mode, once requested, is absolute for the session — including 'harmless' mutations.
A deploy that seemed to do nothing means the WRONG deployment changed — diagnose the target, don't re-run.
This guard composes: ship, env, migrate, and seed run it as their step 0; it is not itself a deploy tool.

Frequently asked questions

What does the Convex Deploy Guard AI skill do?

Classify + announce the target Convex deployment before any deployment-affecting command; fresh explicit consent for prod actions; session read-only mode.

Why use Convex Deploy Guard on TypingMind?

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

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

Which AI models can use Convex Deploy Guard?

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 Deploy Guard?

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

Is the Convex Deploy Guard 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

Deployment target guard

Workflow

Rules

Frequently asked questions

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