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Convex Suggest

ConvexOfficialPublished by Convex

  • Skill
  • Guidelines
  • Software development
  • Apache-2.0
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From the author

Suggest the matching Convex component when the user hand-rolls a pattern it already solves (crons, sharded-counter, rate-limiter, storage, search, presence, workflow, RAG, prosemirror-sync). Passive — suggest after the task, never interrupt. Never install without consent.
— the description by Convex

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Convex-suggest is a passive assistant skill that monitors user code and intent for patterns that mirror existing Convex components. It identifies when a developer is manually implementing features like rate limiting, cron jobs, or search, and suggests the appropriate pre-built component to reduce maintenance and improve performance. The skill operates non-intrusively, waiting until the current task is complete before offering a single, context-aware recommendation.

What it does

  • Monitors code and user requests for patterns solvable by Convex components.
  • Matches custom implementations against a library of known Convex solutions.
  • Provides a single, targeted suggestion after the current task is finished.
  • Explains the reasoning behind a suggestion by citing the user's specific code.
  • Respects user autonomy by requiring explicit consent before any installation.
  • Prevents redundant suggestions by tracking previously installed components.

When to use it

  • When you want to ensure your codebase leverages existing platform capabilities.
  • When you are unsure if a complex feature you are building has a native Convex equivalent.
  • When you want to avoid common pitfalls like OCC conflicts in high-concurrency mutations.
  • When you prefer to use maintained components over custom-rolled infrastructure.

Advantages

  • Reduces technical debt by replacing custom code with battle-tested components.
  • Improves application performance by suggesting optimized alternatives.
  • Maintains developer flow by keeping suggestions passive and non-interruptive.
  • Provides clear, actionable justifications for every recommendation.

Usage examples

“I am writing a mutation to increment a counter every time a user likes a post.”
The agent completes the task and then suggests: 'I noticed you are manually incrementing a counter; consider using @convex-dev/sharded-counter to avoid OCC conflicts at scale.'
“How can I set up a background job to clean up old records every night?”
The agent provides the code for the task and then suggests: 'Since you are looking for scheduled execution, you might prefer using @convex-dev/crons for this task.'

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/convex/convex-suggest/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip convex-suggest.zip -d .agents/skills

Documentation by Convex

<!-- GENERATED from convex-agents content/capabilities/suggest.json — do not edit by hand. -->

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.).

Files

SKILL.md

<!-- GENERATED from convex-agents content/capabilities/suggest.json — do not edit by hand. -->

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.).

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Convex
Publisher
Convex (Official)
Source
convex/agent-skills · skills/convex-suggest · commit f60629d
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Convex or Convex.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download convex-suggest.zip