inference-pipeline-engineer

inference-pipeline-engineer is an agent for Claude Code from birol91/quorum-agents. It costs 22 tokens per session (325 once invoked), scanned A, original, no licence file.

An engineer for connecting sensor data to AI predictions and vehicle-control outputs. It designs the stages that capture, prepare, process, filter, synchronize, and monitor AI results in real time.

In plain words
What is it for?
Use it to build video and sensor inference pipelines, connect multiple AI models, reduce data-copying and processing delays, combine sensor inputs, filter predictions, measure latency and throughput, and design degraded modes.
Why use it?
It helps keep multi-sensor AI processing fast, coordinated, and reliable. It also exposes bottlenecks and provides fallback behavior when the full pipeline cannot run normally.

Agent for Claude Code

Written for Claude Code: installed under .claude/.

Good fit Use it to build video and sensor inference pipelines, connect multiple AI models, reduce data-copying and processing delays, combine sensor inputs, filter predictions, measure latency and throughput, and design degraded modes.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/birol91/quorum-agents/automotive-inference-pipeline-engineer
Install

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.

Clone the repo
git clone --depth 1 https://github.com/birol91/quorum-agents

Made for: Claude Code.

Wrote 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.

agentmods badge for inference-pipeline-engineer

README.md
[![agentmods](https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-inference-pipeline-engineer/github.svg)](https://agentmods.dev/agents/birol91/quorum-agents/automotive-inference-pipeline-engineer)
Your own site
<a href="https://agentmods.dev/agents/birol91/quorum-agents/automotive-inference-pipeline-engineer"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-inference-pipeline-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.

agentmods 80×15 button for inference-pipeline-engineer

Your own site · 80×15
<a href="https://agentmods.dev/agents/birol91/quorum-agents/automotive-inference-pipeline-engineer"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-inference-pipeline-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 22 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 325 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00022 $0.00325
Opus 5.5 $0.00009 $0.00130
Sonnet 5.5 $0.00004 $0.00065
Haiku 4.5 $0.00002 $0.00032

Measured 1mo ago against content hash 9760f1db17c7, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

inference-pipeline-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 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.

.claude/agents/automotive--inference-pipeline-engineer.md · 43 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

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.

  1. 1mo ago First seen · 43 lines · 22 tokens per session scan A 9760f1db17c7

Subscribe to this mod's changes

inference-pipeline-engineer is an agent published in the GitHub repository birol91/quorum-agents (0 stars, last pushed 3d ago), with no licence file. It adds 22 tokens to every session and 325 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 2026-09-03.

Related

Other agents, from other repositories

edge-ai-engineer

Edge AI deployment specialist for on-device inference using Google AI Edge Gallery, TFLite, ONNX Runtime, and MediaPipe with model quantization and hardware delegate optimization.

pjt222/agent-almanac · 38 tokens

rag-architect

Expert in retrieval-augmented generation systems. Use for chunking, embeddings, vector search, hybrid retrieval, and answer grounding with citations.

JosephHampton/awesome-gemini-cli-subagents · 32 tokens

Architekt aplikacji LLM

Inżynier AI buduje aplikacje oparte o duże modele jezykowe: systemy RAG, asystentów, pipeline promptów i integracje z API modeli. Nie trenuje modeli od zera - składa działające systemy z gotowych komponentów i pilnuje trzech rzeczy naraz: kosztu, latencji i jakości. Każde twierdzenie o jakości popiera ewaluacja, nie…

TheJacksonCode/Agent-Architecture · 103 tokens

iot-data-specialist

IoT data architecture, BLE communication, health metrics processing for pet devices.

atretyak1985/swarmery · 20 tokens

ai-ml-engineer

AI/ML Engineer specialising in prompt engineering, RAG architecture, LLM evaluation, AI safety, and agent orchestration. Use when: "build an AI feature", "LLM", "ChatGPT", "Claude API", "prompt engineering", "RAG", "vector database", "embeddings", "fine-tuning", "AI agent", "LangChain", "LangGraph", "evaluation"…

Ghosteken/agent-harness · 0 tokens

ai-engineer

Use this agent to build AI/LLM features - Claude API integration, prompt design, tool use, RAG/embeddings, streaming, evals, cost and latency tuning. Give it the feature and the quality bar.

T4LEL/Claude-Arsenal · 50 tokens