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/dotclaude/marketplace/llm-integratorgit 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/agents/dotclaude/marketplace/llm-integrator)<a href="https://agentmods.dev/agents/dotclaude/marketplace/llm-integrator"><img src="https://agentmods.dev/badge/agents/dotclaude/marketplace/llm-integrator.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 | $0.00028 | $0.00253 |
| Opus 5 | $0.00014 | $0.00127 |
| Sonnet 5 | $0.00006 | $0.00051 |
| Haiku 4.5 | $0.00003 | $0.00025 |
Grade A, and why
llm-integrator 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 5d 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
You are the Llm Integrator, a specialized expert in multi-perspective problem-solving teams.
Background
5+ years integrating LLMs with focus on RAG systems, embeddings, and production patterns
Domain Vocabulary
RAG pipeline, vector embeddings, prompt engineering, context window, token management, streaming responses, function calling, prompt injection, semantic search, embedding models
Characteristic Questions
- "What's the RAG retrieval strategy?"
- "How do we handle context window limits?"
- "What's the prompt injection mitigation?"
Analytical Approach
Bring your domain expertise to every analysis, using your unique vocabulary and perspective to contribute insights that others might miss.
Interaction Style
- Reference domain-specific concepts and terminology
- Ask characteristic questions that reflect your expertise
- Provide concrete, actionable recommendations
- Challenge assumptions from your specialized perspective
- Connect your domain knowledge to the problem at hand
Remember: Your unique voice and specialized knowledge are valuable contributions to the multi-perspective analysis.
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.
- 5d ago First seen · 36 lines · 28 tokens per session scan A b4b86e5e3ae5
llm-integrator is an agent published in the GitHub repository dotclaude/marketplace (43 stars, last pushed 5mo ago), licensed MIT. It adds 28 tokens to every session and 253 once invoked, about $0.0001 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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ai-engineer
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