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/eliornl/rolemuleWrote 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/eliornl/rolemule/llm-integration)<a href="https://agentmods.dev/rules/eliornl/rolemule/llm-integration"><img src="https://agentmods.dev/badge/rules/eliornl/rolemule/llm-integration/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/eliornl/rolemule/llm-integration"><img src="https://agentmods.dev/badge/rules/eliornl/rolemule/llm-integration.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 | $0.00000 | $0.01942 |
| Opus 5.5 | $0.00000 | $0.00777 |
| Sonnet 5.5 | $0.00000 | $0.00388 |
| Haiku 4.5 | $0.00000 | $0.00194 |
Grade A, and why
llm-integration 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Integration Patterns
Architecture
utils/llm/
client.py # LLMClient facade (cache + retry + route by provider=); generate + generate_stream
availability.py # resolve_user_llm_context / CFG_6001 readiness
registry.py # provider name → factory
speak_stream.py # SpeakFieldStreamer — progressive JSON "speak" extraction
providers/
gemini.py # Google AI Studio + optional Vertex + Google Search grounding (+ stream)
openai.py # Chat Completions + Responses web_search when grounded (+ stream)
anthropic.py # Messages API + web_search server tool when grounded (+ stream)
ollama.py # Local /api/chat (no tools / no key) (+ stream)
utils/llm_client.py # backward-compatible re-exports
utils/llm_context.py # require_user_llm_context() for API gates
Prefer get_llm_client() for new code. get_gemini_client() remains an alias.
from utils.llm_client import get_llm_client
llm = await get_llm_client()
response = await llm.generate(
prompt=prompt,
system=system_message,
temperature=0.7,
max_tokens=16000,
user_api_key=user_api_key, # decrypted BYOK for the resolved provider
provider=llm_provider, # required for multi-user correctness
model=user_model, # None → provider default
)
text = response.get("response", "")
Stable response shape: { "response": str, "done": bool, "model"?: str, "filtered"?: bool, "from_cache"?: bool }.
Streaming (generate_stream)
Use for progressive UI (Practice Interview interviewer speak). Not cached and not retried like generate().
async for chunk in llm.generate_stream(
prompt=prompt,
system=system_message,
temperature=0.55,
max_tokens=16000,
user_api_key=user_api_key,
provider=llm_provider,
model=user_model,
):
# chunk is a text delta (str)
...
Providers implement generate_stream on Gemini / OpenAI / Anthropic / Ollama. On stream failure, callers should fall back to non-stream generate().
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 · 155 lines · 0 tokens per session scan A e70d0026c5d0
llm-integration is a cursor rule published in the GitHub repository eliornl/rolemule (38 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,942 tokens. 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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