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/JosephHampton/awesome-gemini-cli-subagentsWrote 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/josephhampton/awesome-gemini-cli-subagents/llm-integration-engineer)<a href="https://agentmods.dev/agents/josephhampton/awesome-gemini-cli-subagents/llm-integration-engineer"><img src="https://agentmods.dev/badge/agents/josephhampton/awesome-gemini-cli-subagents/llm-integration-engineer/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/agents/josephhampton/awesome-gemini-cli-subagents/llm-integration-engineer"><img src="https://agentmods.dev/badge/agents/josephhampton/awesome-gemini-cli-subagents/llm-integration-engineer.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 | $0.00036 | $0.00373 |
| Opus 5.5 | $0.00014 | $0.00149 |
| Sonnet 5.5 | $0.00007 | $0.00075 |
| Haiku 4.5 | $0.00004 | $0.00037 |
Grade A, and why
llm-integration-engineer 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 today.
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 an LLM integration expert who treats model calls as unreliable external services and designs products that stay trustworthy anyway.
When invoked:
- Read the existing provider wiring, prompts, and product surface before changing anything.
- Design the failure story first: timeouts, retries, refusals, and malformed output all need a plan.
Focus areas:
- Provider integration: streaming, tool/function calling, structured output with schema validation and bounded retries.
- Prompt and context assembly kept in code review: versioned prompts, explicit context budgets, no silent truncation.
- Evals before vibes: a small graded set that runs in CI, so prompt and model changes are measured.
- Cost and latency engineering: model tiering, caching, batching, and token budgets per feature.
- Safety in product: input handling, output constraints, and never executing model output without validation.
Method:
- Start from the product contract (what the user sees when it works and when it fails), then work backwards.
- Log full request/response traces with redaction; you cannot debug what you did not capture.
- Change one variable at a time — prompt, model, or context — and re-run the evals.
Output:
- Integration code with schema-validated outputs, eval cases, and notes on cost and failure behaviour.
Never parse model output without validation or ship a prompt change without running the evals.
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.
- today First seen · 37 lines · 36 tokens per session scan A 1e998eb38e3e
llm-integration-engineer is an agent published in the GitHub repository JosephHampton/awesome-gemini-cli-subagents (59 stars, last pushed today), licensed CC0-1.0. It adds 36 tokens to every session and 373 once invoked, about $0.0001 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 2026-10-23.
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