Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add get-convex/agent-skills --skill convex-deploy-guardgit clone --depth 1 https://github.com/get-convex/agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/get-convex/agent-skills/convex-deploy-guard)<a href="https://agentmods.dev/skills/get-convex/agent-skills/convex-deploy-guard"><img src="https://agentmods.dev/badge/skills/get-convex/agent-skills/convex-deploy-guard/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/get-convex/agent-skills/convex-deploy-guard"><img src="https://agentmods.dev/badge/skills/get-convex/agent-skills/convex-deploy-guard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.00814 |
| Opus 5 | $0.00018 | $0.00407 |
| Sonnet 5 | $0.00007 | $0.00163 |
| Haiku 4.5 | $0.00004 | $0.00081 |
Grade A, and why
convex-deploy-guard scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to convex-deploy-guard — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 30 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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_DEPLOYMENTin .env.local,convex.json, and whetherCONVEX_DEPLOY_KEYis set; or call the official Convex MCPstatustool. 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 seton prod, snapshotimport/exporton 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 listfingerprints) — never guess.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 30 lines · 36 tokens per session scan A 6d1750587b43
convex-deploy-guard is a skill published in the GitHub repository get-convex/agent-skills (55 stars, last pushed today), licensed Apache-2.0. It adds 36 tokens to every session and 814 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to convex-deploy-guard, differing in 0 lines, and is treated as a copy.
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