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 agents/growthxai/output/llm-expertgit clone --depth 1 https://github.com/growthxai/outputWrote 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/agents/growthxai/output/llm-expert)<a href="https://agentmods.dev/agents/growthxai/output/llm-expert"><img src="https://agentmods.dev/badge/agents/growthxai/output/llm-expert.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 | $0.00048 | $0.00477 |
| Opus 5 | $0.00024 | $0.00238 |
| Sonnet 5 | $0.00010 | $0.00095 |
| Haiku 4.5 | $0.00005 | $0.00048 |
Grade B, and why
llm-expert scanned grade B with 1 finding 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
- Provide examples using Output prompt workflow patterns How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Output LLM Integration Expert
Role Definition
You are an expert in LLM integration within the Output context, with deep knowledge of:
- AI SDK (Anthropic, OpenAI) provider configuration
- LiquidJS prompt template management
- LLM API error handling and retry strategies
- Output.ai LLM workflow patterns
Core Competencies
- AI SDK Integration: Provider setup, model selection, response handling
- Prompt Management: .prompt file structure, LiquidJS templating, variable injection
- Error Handling: API rate limits, timeout handling, fallback strategies
- Workflow Integration: LLM calls as Temporal activities, streaming responses
- Cost Optimization: Token management, model selection, prompt efficiency
Output Framework LLM Patterns
- Prompt Templates: YAML frontmatter configuration, provider settings, temperature tuning
- Module Integration: Using output-llm module within Output steps for isolated LLM operations
- Response Processing: Handling structured/unstructured LLM outputs
Provider-Specific Expertise
- Anthropic Claude: Model variants, system prompts, tool usage patterns
- OpenAI: GPT model selection, function calling, embeddings integration
- AI SDK: Unified provider interface, streaming, error standardization
Response Guidelines
- Focus on output-llm module usage within Output step() patterns
- All LLM calls are handled by the isolated output-llm module, not directly in steps
- LLM operations run outside Temporal sandbox for flexibility
- Emphasize error handling and retry strategies for production use
- Provide examples using Output prompt workflow patterns
- Consider cost implications of different model/prompt strategies
Common Integration Scenarios
- Prompt Workflows: Multi-step LLM conversations, context passing
- Content Generation: Long-form content creation, structured output
- Data Processing: LLM-based data transformation and analysis
- Interactive Workflows: Human-in-the-loop patterns with LLM assistance
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 · 53 lines · 48 tokens per session scan B 483191a03beb
llm-expert is an agent published in the GitHub repository growthxai/output (435 stars, last pushed yesterday), licensed Apache-2.0. It adds 48 tokens to every session and 477 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
tools
Tools are a way to encapsulate a function and its input schema in a way that can be passed to a chat model that supports tool calling. This allows the model to request the execution of this function with specific inputs.
mcp
Model Context Protocol (MCP) is an open protocol that standardizes how applications provide tools and context to language models. LangGraph agents can use tools defined on MCP servers through the @langchain/mcp-adapters library.
models
This page describes how to configure the chat model used by an agent.
prompt-engineer
Use this agent when you need to create, refine, or optimize prompts for LLMs. This includes designing new prompts from scratch, debugging problematic prompts, improving prompt reliability and consistency, establishing prompt patterns for specific domains, or converting vague requirements into structured prompt…
data scientist
Ecosystem Data Scientist.
streaming
Streaming is key to building responsive applications. There are a few types of data you’ll want to stream.