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-agentgit 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-agent)<a href="https://agentmods.dev/skills/get-convex/agent-skills/convex-agent"><img src="https://agentmods.dev/badge/skills/get-convex/agent-skills/convex-agent/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-agent"><img src="https://agentmods.dev/badge/skills/get-convex/agent-skills/convex-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector 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 | $0.00025 | $0.00415 |
| Opus 5 | $0.00013 | $0.00208 |
| Sonnet 5 | $0.00005 | $0.00083 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
convex-agent 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 9d 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.
What it actually says
Add an AI agent / RAG backend
Install @convex-dev/agent for durable threads, message history, tool-calls, and vector search/RAG — the backend for an in-app AI agent. Call models through the Convex AI Gateway by default: Convex holds the provider credentials, so there is no LLM key to obtain, store, or rotate.
Workflow
- Install @convex-dev/agent + @convex-dev/ai-sdk-provider; add the agent component to convex.config.ts.
- Define the agent (tools, instructions) with
languageModel: convexGateway("provider/model")— no API key needed (needs convex 1.45+ on a Convex Cloud deployment, paid plan). - Create threads + stream messages; persist history in Convex.
- For RAG: embed docs into a vector index and retrieve in the tool. The gateway does not serve embeddings yet, so store the embedding provider's key via the
envmicro power. - Only if the gateway is unavailable (free plan, self-hosted, local backend): call the provider SDK with a key stored via the
envmicro power.
Rules
- Default to the Convex AI Gateway (
convexGatewayfrom @convex-dev/ai-sdk-provider) for model calls; fall back to a provider key in Convex env only where the gateway is unavailable (free plan, self-hosted, local backend). - Never expose a provider API key client-side; when one is needed (embeddings, gateway fallback), keep it in Convex env via the
envmicro power. - Run model calls in actions ('use node' if the SDK needs it).
- Persist threads/messages in Convex for durability + reactivity.
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.
- 9d ago First seen · 26 lines · 25 tokens per session scan A d4ab9270edba
convex-agent is a skill published in the GitHub repository get-convex/agent-skills (53 stars, last pushed 5d ago), licensed Apache-2.0. It adds 25 tokens to every session and 415 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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