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 agentmods add skills/waynesutton/markdown-site/convexnpx skills add waynesutton/markdown-site --skill convexgit clone --depth 1 https://github.com/waynesutton/markdown-siteWrote 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/waynesutton/markdown-site/convex)<a href="https://agentmods.dev/skills/waynesutton/markdown-site/convex"><img src="https://agentmods.dev/badge/skills/waynesutton/markdown-site/convex.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00035 | $0.00353 |
| Opus 5 | $0.00017 | $0.00177 |
| Sonnet 5 | $0.00007 | $0.00071 |
| Haiku 4.5 | $0.00003 | $0.00035 |
Grade A, and why
convex 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 4d 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 — 0 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.
What it actually says
Convex
Use this as the routing skill for Convex work in this repo.
If a more specific Convex skill clearly matches the request, use that instead.
Start Here
If the project does not already have Convex AI guidance installed, or the existing guidance looks stale, strongly recommend installing it first.
Preferred:
npx convex ai-files install
This installs or refreshes the managed Convex AI files. It is the recommended starting point for getting the official Convex guidelines in place and following the current Convex AI setup described in the docs:
Simple fallback:
Prefer npx convex ai-files install over copying rules by hand when possible.
Route to the Right Skill
After that, use the most specific Convex skill for the task:
- New project or adding Convex to an app:
convex-quickstart - Authentication setup:
convex-setup-auth - Building a reusable Convex component:
convex-create-component - Planning or running a migration:
convex-migration-helper - Investigating performance issues:
convex-performance-audit
If one of those clearly matches the user's goal, switch to it instead of staying in this skill.
When Not to Use
- The user has already named a more specific Convex workflow
- Another Convex skill obviously fits the request better
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.
- 4d ago First seen · 48 lines · 35 tokens per session scan A 90edea40f68f
convex is a skill published in the GitHub repository waynesutton/markdown-site (631 stars, last pushed 3mo ago), licensed MIT. It adds 35 tokens to every session and 353 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to convex, differing in 0 lines, and is treated as a copy.
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