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.
git 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/suggest)<a href="https://agentmods.dev/commands/get-convex/convex-agent-plugins/suggest"><img src="https://agentmods.dev/badge/commands/get-convex/convex-agent-plugins/suggest/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/commands/get-convex/convex-agent-plugins/suggest"><img src="https://agentmods.dev/badge/commands/get-convex/convex-agent-plugins/suggest.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00060 | $0.00535 |
| Opus 5.5 | $0.00024 | $0.00214 |
| Sonnet 5.5 | $0.00012 | $0.00107 |
| Haiku 4.5 | $0.00006 | $0.00053 |
Grade A, and why
suggest 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 23 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Proactively suggest the right Convex component
When you see code or intent that duplicates what a Convex component already does, surface a targeted suggestion: ONE component, WHY (anchored in the user's own code or ask), and a concrete install hint. Never install without explicit consent. Never suggest more than one component at a time unless the user asks.
Steps
- Observe the codeSnippets and userAsk passively — never block the current task to suggest.
- Match against the detector rules (see generators/suggest-detector.mjs): email/SMTP → resend; push notifications → expo-push; setInterval/cron → @convex-dev/crons; shared counter increments → @convex-dev/sharded-counter; .collect().length scans → @convex-dev/aggregate; multi-step/long-running actions → @convex-dev/workflow; bounded concurrency → @convex-dev/workpool; rate-limit counters in DB → @convex-dev/rate-limiter; fs.write/S3 uploads → Convex Storage; Elasticsearch/Algolia → built-in full-text search; presence/typing → @convex-dev/presence; Pinecone/external vector DB → @convex-dev/rag; collaborative editing → @convex-dev/prosemirror-sync.
- After finishing the current task, offer ONE suggestion: name the component, quote the specific code or phrase that triggered it, explain why the component fits better.
- If the user says yes: run
/add <component>or follow the installHint from the detector. - If the user says no or ignores it: drop it. Do not repeat the same suggestion.
Rules
- Passive — never interrupt the current task; surface the suggestion AFTER completing what the user asked.
- One at a time — pick the highest-priority match; do not dump a list of five components.
- Cite WHY from the user's own code or ask — 'I noticed you wrote
post.likes + 1in a mutation that many users call concurrently; that causes OCC conflicts at scale.' - Never install without explicit consent — suggest, explain, wait for a yes.
- Do not suggest a component the user has already installed.
- Do not fire on generic coding questions unrelated to Convex (sorting arrays, writing CSS, etc.).
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.
- 1mo ago First seen · 23 lines · 60 tokens per session scan A 2551f00d6646
suggest is a command published in the GitHub repository get-convex/convex-agent-plugins (117 stars, last pushed 7d ago), licensed MIT. It adds 60 tokens to every session and 535 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other commands, from other repositories
querying
Query documents from a search index using type-safe filters with support for pagination, sorting, field selection, scoring, and highlighting. Count matching documents efficiently without returning results.
inference.embed
Embed one or more texts into vectors.
proofrag
Evaluate a RAG/LLM app — generate a golden set, judge it, and produce a scorecard.
vector.batch_upsert
Upsert multiple vectors in one itemwise batch.
inference.rank
Rank passages against a query.
vector.upsert
Insert or replace one vector.