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 agentmods add commands/get-convex/convex-agent-plugins/sentinelgit clone --depth 1 https://github.com/get-convex/convex-agent-pluginsWrote 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/commands/get-convex/convex-agent-plugins/sentinel)<a href="https://agentmods.dev/commands/get-convex/convex-agent-plugins/sentinel"><img src="https://agentmods.dev/badge/commands/get-convex/convex-agent-plugins/sentinel.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00013 | $0.00304 |
| Opus 5 | $0.00006 | $0.00152 |
| Sonnet 5 | $0.00003 | $0.00061 |
| Haiku 4.5 | $0.00001 | $0.00030 |
Grade A, and why
sentinel 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 4d 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
Capture production errors in your own deployment
Install @convex-dev/sentinel to capture production errors (server function failures, client JS/React crashes, OCC and scale signals) into a table in the user's OWN deployment, redacted at write time, then react to new ones. Data never leaves the user's deployment.
Steps
- Install the component:
app.use(sentinel)inconvex/convex.config.ts. - Wire the client SDK: a React error boundary plus
window.onerror/unhandledrejectionand breadcrumbs. - Redaction runs at write time and is on by default (default-deny on secret key names and value patterns).
- Read recent errors with the Convex CLI (
convex data,run-once-query); react to new ones via the monitor'sprod_errorevent. - Optionally enable the self-healing cron:
triageclassifies each error and, for recurring non-transient ones, hands it to ai-runner to open a fix PR.
Rules
- Redaction is mandatory and on by default — never store raw secrets; the agent's reads reach the model provider.
- Data stays in the user's deployment; never send it to a third party.
- Sample and cap to control volume and cost.
- Capturing PROD errors needs a deployed cloud app; install works anonymously.
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.
- 4d ago First seen · 21 lines · 13 tokens per session scan A a430c446f76b
sentinel is a command published in the GitHub repository get-convex/convex-agent-plugins (112 stars, last pushed 6d ago), licensed MIT. It adds 13 tokens to every session and 304 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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