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 skills add sawrus/agent-guides --skill inference-servinggit clone --depth 1 https://github.com/sawrus/agent-guidesWrote 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/skills/sawrus/agent-guides/inference-serving)<a href="https://agentmods.dev/skills/sawrus/agent-guides/inference-serving"><img src="https://agentmods.dev/badge/skills/sawrus/agent-guides/inference-serving.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00246 |
| Opus 5 | $0.00000 | $0.00123 |
| Sonnet 5 | $0.00000 | $0.00049 |
| Haiku 4.5 | $0.00000 | $0.00025 |
Grade A, and why
inference-serving 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 4d 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
Skill: Inference Serving
When to load
When deploying a model to an API endpoint or optimizing inference latency.
FastAPI Inference Endpoint
@app.on_event("startup")
def load_model():
app.state.model = mlflow.pyfunc.load_model("models:/churn-predictor/Production")
app.state.preprocessor = load_preprocessor()
@app.post("/predict", response_model=PredictionResponse)
def predict(request: PredictionRequest):
try:
features = app.state.preprocessor.transform([request.features])
probability = app.state.model.predict(features)[0]
log_prediction(request.user_id, request.features, float(probability))
return PredictionResponse(
user_id=request.user_id,
churn_probability=float(probability),
)
except Exception as e:
logger.error("Inference failed", error=str(e))
return PredictionResponse(user_id=request.user_id, churn_probability=FALLBACK_PROBABILITY)
Latency Checklist
- Model loaded at startup, not per request
- Input preprocessing vectorized (batch)
- ONNX conversion for framework-agnostic optimization
- Batch inference enabled for high-throughput
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.
- 4d ago First seen · 36 lines · 0 tokens per session scan A 9157c8930b94
inference-serving is a skill published in the GitHub repository sawrus/agent-guides (17 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 246 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 2026-09-03.
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