agentleFS
convexconvex/agent-skills/skills/convex-suggest/SKILL.md

convex-suggest

Skill68 starsChanged 15 days agoApache-2.0View on GitHub

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.

6 other projects use a version of this.

Adopted by

Steady: 0 new projects took it in the last 4 weeks, none the 4 weeks before.thin data

Week of Sep 13: 0 new projectsWeek of Sep 20: 0 new projectsWeek of Sep 27: 0 new projectsWeek of Oct 4: 0 new projectsWeek of Oct 11: 0 new projectsWeek of Oct 18: 0 new projectsWeek of Oct 25: 0 new projectsWeek of Nov 1: 0 new projects
Sep 2026Oct 2026Nov 2026
New projects taking a version of this file each week, over the 8 weeks our latest run read: only 0 in all, so this is a hint, not a trend.

Most recently taken up by

Used with

Projects with it also use convex-acquire-domain, convex-check-updates and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • FabienGreardSkill

    convex-acquire-domain

    Find and buy a domain for the current Convex app through Convex, then bind it (labs; spend action).

    Also in 4 projects that use this

  • FabienGreardSkill

    convex-check-updates

    Check the current app's pinned Convex components against recommended versions and upgrade them behind a build gate.

    Also in 4 projects that use this

  • openclawSkill

    clawhub-ban-appeals

    Review ClawHub account-ban appeals, distinguish bans from moderation holds or blocked releases, and prepare an evidence-backed moderation decision. Route validated scanner inaccuracies to clawhub-clawscan.

    Also in 3 projects that use this

  • openclawSkill

    clawhub-clawscan

    Investigate ClawScan health alerts, diagnose scanner or ClawHub integration failures, prepare operational repairs, and route validated verdict inaccuracies upstream. Account ban appeals use clawhub-ban-appeals.

    Also in 3 projects that use this

  • +8 more from openclaw

The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 5 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    After finishing the current task, offer ONE suggestion: name the component, quote the specific code or phrase that triggered it, explain …

    line 16
  2. Very specific:

    If the user says yes: run /add <component> or follow the installHint from the detector.

    line 17
  3. Distinctive:

    If the user says no or ignores it: drop it.

    line 18
  4. Distinctive:

    Never install without explicit consent — suggest, explain, wait for a yes.

    line 25
  5. Distinctive:

    Do not suggest a component the user has already installed.

    line 26
  6. Distinctive:

    Do not fire on generic coding questions unrelated to Convex (sorting arrays, writing CSS, etc.).

    line 27

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
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.).

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.