
Convex Component Suggester
Suggests a matching Convex component when you hand-roll a pattern it already solves
- Repository
- openclaw/clawhub
- Stars
- 9,546
- Licence
- MIT
- Files
- 1 file
What it does
This skill makes an AI agent watch for code or requests that rebuild something a Convex component already provides, such as cron jobs, rate limiting, file storage, or presence indicators. After finishing the task you asked for, the agent offers one targeted suggestion that names the component, quotes the code that triggered it, and explains why it fits. It never installs anything unless you say yes.
Great for
- Developers building apps on the Convex backend platform who want to avoid reinventing common features
- Teams reviewing Convex code for patterns that scale poorly, such as concurrent counter updates
Skip it if
- Projects that do not use Convex, since the suggestions only target Convex components and the agent may waste time on irrelevant matches
- Users who want an agent to change dependencies automatically, because the skill is designed to ask for consent before any install
Try asking
- “Finish adding a like button to my Convex mutation and suggest a better approach if the counter will be hit by many users”
- “Write a scheduled cleanup job for my Convex app and tell me if there is a Convex component for it”
- “Add an upload feature to my Convex project and recommend the right storage option”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 1 original files, the MIT licence and an attribution note.
convex-suggest.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip convex-suggest.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip convex-suggest.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Let the agent finish your current task first; suggestions are designed to come after, not during, the work
- Say no to a suggestion once and the agent should drop it rather than repeat it
- Ask the agent directly for a specific component if you already know what you want
What's inside
- SKILL.md
Redistributed unmodified from openclaw/clawhub under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.