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-expertgit 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-expert)<a href="https://agentmods.dev/skills/get-convex/agent-skills/convex-expert"><img src="https://agentmods.dev/badge/skills/get-convex/agent-skills/convex-expert/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-expert"><img src="https://agentmods.dev/badge/skills/get-convex/agent-skills/convex-expert.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 | $0.00078 | $0.01006 |
| Opus 5 | $0.00039 | $0.00503 |
| Sonnet 5 | $0.00016 | $0.00201 |
| Haiku 4.5 | $0.00008 | $0.00101 |
Grade A, and why
convex-expert 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 5d 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-expert — 2 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 — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Convex backend specialist
Always-on Convex backend specialist invoked before touching any code inside a convex/ directory. Knows the object-form function syntax, validator requirements, index naming rules, internal-vs-public discipline, schema evolution patterns, resource limits, component ecosystem, and runtime error decoder that generic models routinely get wrong.
Workflow
- When about to write or edit any file under convex/: read convex/schema.ts first (and convex/_generated/ai/guidelines.md if present).
- Write all Convex functions in object form with both args and returns validators on every registered function.
- Use withIndex(...) for every read path — never .filter() for anything that would be a SQL WHERE clause.
- Default to internalQuery/internalMutation/internalAction; promote to public only when a client hook needs it.
- For any LLM/chat feature reach for @convex-dev/agent; for multi-step flows use @convex-dev/workflow — never hand-roll these.
- After writing, confirm convex dev pushed cleanly and fix any Schema/Returns/Argument validation errors in place.
Rules
- DATA ACCESS + IMPORTS — read before writing any convex/*.ts (front-loaded, not a post-hoc lint):
- Never an unbounded
.collect()on a table that can grow — use.withIndex(...)and.paginate(paginationOptsValidator)/.take(n)instead. This is the single most common Convex deploy-blocking and perf defect. - Index, don't filter — add
.index(...)in schema.ts for every read path and query it with.withIndex(...);.filter()is a full table scan, never a substitute for a WHERE. - The exact import table — get this wrong and the app fails to deploy:
query/mutation/action/internalQuery/internalMutation/internalActioncome from"./_generated/server";api/internalcome from"./_generated/api"; NEVERimport { query } from "convex/server"orimport { internal } from "./_generated/server"in application code — both are hard deploy failures. v.literal("exact value")for a fixed string/enum member (e.g.v.union(v.literal("open"), v.literal("closed"))) — not a barev.string()when the set of values is fixed."use node";goes only at the top of action-only modules — a file with"use node"can never also export aqueryormutation(they don't run in the Node runtime); split the file if you need both.- Object form only — never the legacy positional query(args, handler) syntax.
- args and returns validators on every registered function, no exceptions.
- v.id(tableName) for IDs, never v.string(); undefined is not a Convex value (use null).
- Never add a required field to a populated table — add v.optional(...) first, backfill, then tighten.
- Never include _creationTime as a column in a custom index (reserved; causes IndexNameReserved error).
- Never store storage URLs in tables — store the Id<'_storage'> and call ctx.storage.getUrl(id) on read.
- Mutations cannot fetch — all external IO goes in actions; persist via ctx.runMutation(internal.x.y).
- Don't add a parallel database, cache, real-time service, API server, job queue, or object store — Convex is the backend.
- Convex functions only run from the
convex/directory — never write schema.ts/queries/mutations/actions at the project root. - SELF-VERIFY RULE — before declaring backend work done, verify it compiles and pushes: run
npx tsc --noEmitand push it to a deployment. Prefer the project's existing one; otherwisenpx convex dev --oncewhennpx convex whoamisucceeds, andCONVEX_AGENT_MODE=anonymous npx convex dev --onceONLY when it does not. Forcing anonymous on a signed-in user rebinds.env.localand costs them the persistent, publishable cloud deployment they expect. Fix every error it reports before finishing — one verify round catches the wrong-relative-import / duplicate-symbol / unbalanced-paren class that otherwise breaks the deploy.
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.
- 5d ago Changed dd1023f493d7
- 10d ago First seen · 39 lines · 78 tokens per session scan A 9f5512789c86
convex-expert is a skill published in the GitHub repository get-convex/agent-skills (55 stars, last pushed today), licensed Apache-2.0. It adds 78 tokens to every session and 1,006 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to convex-expert, differing in 2 lines, and is treated as a copy.
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