convex-suggest

convex-suggest is a skill for Claude Code, Codex from openclaw/clawhub. It costs 65 tokens per session (562 once invoked), scanned A, original, MIT.

A passive guide that recognizes when Convex code duplicates a pattern already provided by a Convex component. Convex components are reusable packages for jobs such as scheduled tasks, file storage, search, rate limits, and workflows.

In plain words
What is it for?
Use it when code involves patterns such as cron jobs, counters, uploads, full-text search, presence, rate limiting, long workflows, or collaborative editing.
Why use it?
It can point out a suitable component after the current task, helping avoid maintaining custom code for a problem Convex already has a package for. It does not interrupt the task or install anything without permission.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

Good fit Use it when code involves patterns such as cron jobs, counters, uploads, full-text search, presence, rate limiting, long workflows, or collaborative editing.

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Install with agentmods
npx agentmods add skills/openclaw/clawhub/convex-suggest
About the project

ClawHub is a public registry where OpenClaw users publish, version, search, and install text-based agent skills and OpenClaw packages. It provides web browsing, a CLI-oriented API, moderation, vector search, and artifact hosting for code plugins, bundle plugins, and experimental whole-agent packages. The catalogue skills and agents are entries that can be discovered or used through this registry.

openclaw/clawhub · 9,402 stars · on GitHub · clawhub.ai

Install

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.

Any agent
npx skills add openclaw/clawhub --skill convex-suggest
Clone the repo
git clone --depth 1 https://github.com/openclaw/clawhub

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for convex-suggest

README.md
[![agentmods](https://agentmods.dev/badge/skills/openclaw/clawhub/convex-suggest.svg)](https://agentmods.dev/skills/openclaw/clawhub/convex-suggest)
Your own site
<a href="https://agentmods.dev/skills/openclaw/clawhub/convex-suggest"><img src="https://agentmods.dev/badge/skills/openclaw/clawhub/convex-suggest.svg" alt="Measured on agentmods" height="20"></a>
Per session 65 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 562 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 3
    Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.
    Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00065 $0.00562
Opus 5 $0.00032 $0.00281
Sonnet 5 $0.00013 $0.00112
Haiku 4.5 $0.00006 $0.00056

Measured 9d ago against content hash 432e34a1af0d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

convex-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 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.agents/skills/convex-suggest/SKILL.md · 28 lines

How it starts

The opening of the file, as written. The whole thing — 28 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.

Workflow

  1. Observe the codeSnippets and userAsk passively — never block the current task to suggest.
  2. 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.
  3. 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.
  4. If the user says yes: run /add <component> or follow the installHint from the detector.
  5. 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 + 1 in 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.).

Read the full file on GitHub · 28 lines

Changes

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.

  1. 9d ago First seen · 28 lines · 65 tokens per session scan A 432e34a1af0d

Subscribe to this mod's changes

convex-suggest is a skill published in the GitHub repository openclaw/clawhub (9,402 stars, last pushed today), licensed MIT. It adds 65 tokens to every session and 562 once invoked, about $0.0003 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.