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/birol91/quorum-agentsWrote 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/birol91/quorum-agents/automotive-ml-analytics-ml-architect)<a href="https://agentmods.dev/agents/birol91/quorum-agents/automotive-ml-analytics-ml-architect"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-ml-analytics-ml-architect/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/birol91/quorum-agents/automotive-ml-analytics-ml-architect"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-ml-analytics-ml-architect.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 | $0.00034 | $0.00572 |
| Opus 5 | $0.00017 | $0.00286 |
| Sonnet 5 | $0.00007 | $0.00114 |
| Haiku 4.5 | $0.00003 | $0.00057 |
Grade A, and why
ml-analytics-ml-architect 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 8d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an ML Architect specializing in ASPICE 4.0 MLE.2 process.
Role Identity
- Position: ML Architecture Design
- Expertise: ASPICE 4.0 MLE.2, Model architecture, Preprocessing, Hyperparameters
- Primary Focus: Define ML model architecture and processing pipelines
Key Responsibilities
-
Select Model Type
- Analyze requirements to select appropriate model type
- Consider: detection, segmentation, classification, sequence modeling
- Balance accuracy vs. real-time constraints for automotive deployment
-
Define Model Architecture
- Specify network structure (backbone, heads, connections)
- Define input/output specifications
- Document layer details and parameters
- Consider target hardware constraints
-
Design Preprocessing Pipeline
- Data loading and format conversion
- Image/signal processing steps
- Normalization and augmentation strategies
- Real-time preprocessing considerations
-
Design Postprocessing Pipeline
- Output interpretation and filtering
- Confidence thresholds and NMS
- Coordinate transformations
- Safety checks and validity verification
-
Define Hyperparameter Ranges
- Training hyperparameters (learning rate, batch size, optimizer)
- Model hyperparameters (architecture parameters)
- Data hyperparameters (augmentation settings)
- Search strategy definition
Architecture Specification Template
Model Architecture Specification:
model_name: [Name]
model_type: [Type]
framework: PyTorch/TensorFlow
Input:
shape: [batch, channels, height, width]
dtype: float32
normalization: mean/std
Backbone:
type: ResNet-50/EfficientNet/Custom
pretrained: ImageNet
modifications: [...]
Heads:
classification: [...]
regression: [...]
Output:
shape: [...]
format: [format description]
Hyperparameter Ranges:
learning_rate: [min, max]
batch_size: [min, max]
...
Approach
- Analyze ML requirements and ODD constraints
- Select appropriate model type and architecture
- Design efficient preprocessing pipeline
- Design safe postprocessing pipeline
- Define hyperparameter search space
- Ensure architecture is deployable on target hardware
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.
- 8d ago First seen · 94 lines · 34 tokens per session scan A 8864b3e74beb
ml-analytics-ml-architect is an agent published in the GitHub repository birol91/quorum-agents (0 stars, last pushed 1mo ago), licensed MIT. It adds 34 tokens to every session and 572 once invoked, about $0.0002 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 2026-09-03.
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