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/polarcoding85/convex-agent-skillz/convex-components-skillnpx skills add PolarCoding85/convex-agent-skillz --skill convex-components-skillgit clone --depth 1 https://github.com/PolarCoding85/convex-agent-skillzWrote 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/polarcoding85/convex-agent-skillz/convex-components-skill)<a href="https://agentmods.dev/skills/polarcoding85/convex-agent-skillz/convex-components-skill"><img src="https://agentmods.dev/badge/skills/polarcoding85/convex-agent-skillz/convex-components-skill.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 5.1 | $0.00042 | $0.01294 |
| Opus 5 | $0.00021 | $0.00647 |
| Sonnet 5 | $0.00008 | $0.00259 |
| Haiku 4.5 | $0.00004 | $0.00129 |
Grade A, and why
convex-components 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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Convex Components
Components are sandboxed packages with their own database tables, functions, and isolated execution.
Universal Installation Pattern
All components follow the same installation pattern:
npm install @convex-dev/<component-name>
// convex/convex.config.ts
import { defineApp } from 'convex/server';
import componentName from '@convex-dev/<component-name>/convex.config';
const app = defineApp();
app.use(componentName);
// Multiple instances with different names
app.use(componentName, { name: 'instance2' });
export default app;
Run npx convex dev to generate code.
Accessing Components
import { components } from './_generated/api';
// Default instance
const instance = new ComponentClass(components.componentName, {
/* config */
});
// Named instance
const instance2 = new ComponentClass(components.instance2, {
/* config */
});
Transaction Semantics
Component mutations participate in the parent transaction:
export const doWork = mutation({
handler: async (ctx) => {
await ctx.db.insert('myTable', { data: 'value' });
await component.doSomething(ctx); // Same transaction
// If mutation throws, BOTH writes roll back
}
});
Component exceptions can be caught:
try {
await rateLimiter.limit(ctx, 'myLimit', { throws: true });
} catch (e) {
// Only component's writes roll back
// Parent mutation can continue
}
Available Components
Durable Functions
- Workflow - Long-running, durable code flows with retries
- Workpool - Queue actions with parallelism limits
- Action Retrier - Retry failed actions with backoff
Backend Utilities
- Rate Limiter - Application-layer rate limiting
- Aggregate - Efficient COUNT, SUM, MAX operations
- Sharded Counter - High-throughput counting
- Presence - Real-time user presence tracking
- Action Cache - Cache expensive action results
- Migrations - Stateful online data migrations
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/ACTION-CACHE.md 6.4 KB
- references/ACTION-RETRIER.md 6.9 KB
- references/AGGREGATE.md 5.6 KB
- references/MIGRATIONS.md 7.7 KB
- references/PRESENCE.md 6.5 KB
- references/PROSEMIRROR-SYNC.md 8.4 KB
- references/RATE-LIMITER.md 4.5 KB
- references/RESEND.md 8.8 KB
- references/SHARDED-COUNTER.md 0 B
- references/STRIPE.md 14 KB
- references/WORKFLOW.md 6.8 KB
- references/WORKPOOL.md 6.0 KB
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.
- 6d ago First seen · 177 lines · 42 tokens per session scan A 8c164169c6cc
convex-components is a skill published in the GitHub repository PolarCoding85/convex-agent-skillz (17 stars, last pushed 6mo ago), licensed MIT. It adds 42 tokens to every session and 1,294 once invoked, about $0.0002 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.
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