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/dotclaude/marketplaceWrote 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/commands/dotclaude/marketplace/llm)<a href="https://agentmods.dev/commands/dotclaude/marketplace/llm"><img src="https://agentmods.dev/badge/commands/dotclaude/marketplace/llm/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/commands/dotclaude/marketplace/llm"><img src="https://agentmods.dev/badge/commands/dotclaude/marketplace/llm.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 | $0.00012 | $0.00128 |
| Opus 5.5 | $0.00005 | $0.00051 |
| Sonnet 5.5 | $0.00002 | $0.00026 |
| Haiku 4.5 | $0.00001 | $0.00013 |
Grade A, and why
llm 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.
What it actually says
Llm Command
LLM integration patterns, RAG systems, and prompt engineering
Arguments
$1 (Required): requirement
$2 (Optional): pattern
Examples
/llm "Build RAG system for docs" rag
/llm "Implement chat interface" streaming
Invoke the llm-integrator agent with: $ARGUMENTS
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 · 26 lines · 12 tokens per session scan A ec3883f932d7
llm is a command published in the GitHub repository dotclaude/marketplace (43 stars, last pushed 6mo ago), licensed MIT. It adds 12 tokens to every session and 128 once invoked, about $0.0000 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 commands, from other repositories
ai
Invoke the AI/LLM Application Engineer for RAG, agents, prompt engineering, evals, tool use, and LLM guardrails.
snr-ai-security
AI feature security review — prompt injection paths, agent permissions, output handling, data isolation, MCP/skill supply chain, provider-key and spend protection.
t00-ai-dev
A set of instructions for building applications that use artificial intelligence, such as chatbots, document search, or text-generation tools. It covers Claude, retrieval-augmented generation (RAG), embeddings, and vector databases, which help find relevant documents for an AI answer.
agent
Add an AI agent / RAG backend (@convex-dev/agent) to the Convex app.
boot
Start a new PromptKit prompt assembly session (alias for /bootstrap).
dare-llm-integration
Integração segura e eficiente com LLMs (Gemini, Claude, OpenAI, Ollama) em projetos DARE.