Helicone is an open-source platform for monitoring and routing requests to large language models. AI engineers use it to inspect agent and application traces, track cost and latency, evaluate quality, manage prompts, and access multiple models through an API gateway. Catalogue add-ons support observability and LLM workflow tasks.
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.
git clone --depth 1 https://github.com/Helicone/heliconeWrote 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/rules/helicone/helicone/jawn)<a href="https://agentmods.dev/rules/helicone/helicone/jawn"><img src="https://agentmods.dev/badge/rules/helicone/helicone/jawn/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/helicone/helicone/jawn"><img src="https://agentmods.dev/badge/rules/helicone/helicone/jawn.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00543 | $0.00543 |
| Opus 5.5 | $0.00217 | $0.00217 |
| Sonnet 5.5 | $0.00109 | $0.00109 |
| Haiku 4.5 | $0.00054 | $0.00054 |
Grade A, and why
jawn 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 1mo 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Helicone Jawn Controller and TanStack Query Integration
Controller Pattern
- Jawn controllers (
valhalla/jawn/src/controllers/) follow a RESTful design with standard HTTP methods - Controllers use decorators like
@Route,@Get,@Post, etc. from TSOA - Controllers accept
JawnAuthenticatedRequestand returnResult<T, string> - Required parameters should come before optional parameters in controller methods
Manager Pattern
- Each controller has a corresponding manager (
valhalla/jawn/src/managers/) - Managers extend
BaseManagerand handle database operations - Managers receive
authParamsfrom the controller request - Use
dbExecutefor database operations and wrap results inResult<T, string>
Frontend Hooks with TanStack Query
-
Use
useJawnClient()to interact with Jawn controllers -
Implement custom hooks with TanStack Query for data fetching and mutations
-
For GET requests:
const query = useQuery({ queryKey: ['resource', id], queryFn: async () => { const response = await jawn.GET('/v1/resource/{id}', { params: { path: { id } } }); if (response.error) throw response.error; return response.data; } }); -
For mutations:
const mutation = useMutation({ mutationFn: async (input) => { const response = await jawn.POST('/v1/resource', { body: input }); if (response.error) throw response.error; return response.data; }, onSuccess: (data) => { // Invalidate queries queryClient.invalidateQueries({ queryKey: ['resource'] }); } });
Type Definitions
- Define request and response interfaces in the controller file
- Use proper typing with TanStack Query
- Handle Jawn response structure properly with error checking
URL State Management
- Use URL parameters for persisting filter state between pages
- Update URL when filter changes with
useSearchParamsfrom Next.js
Database Schema
- Tables follow snake_case naming
- Use proper indexes for performance
- Implement proper RLS policies for security
- Create functions for specialized operations (e.g., incrementing versions)
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.
- 1mo ago First seen · 73 lines · 543 tokens per session scan A 691a78ad487b
jawn is a cursor rule published in the GitHub repository Helicone/helicone (6,226 stars, last pushed 7d ago), licensed Apache-2.0. It adds 543 tokens to every session, about $0.0022 per session on Opus 5.5. 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.
Other cursor rules, from other repositories
api-standards
A set of standards for designing, calling, and handling errors from application programming interfaces, or APIs. The input marks these standards as high priority.
ddd-design-guide
Apply when designing or refactoring domain models, services, stores, or discussing DDD (Domain-Driven Design) architecture. Also apply when working on business logic layers, API modules, or planning domain boundaries and migrations.
convex
Cursor rule "convex" from waynesutton/promptstack, covering convex guidelines, function guidelines, new function syntax, http endpoint syntax and function registration.
cursorrules
This document serves as some special instructions when working with Convex.
backend
Backend Development Practices and API Design Standards.
dev2
description: full-stack AI convex developer globs: alwaysApply: true.