Borrowing it
Nothing to install: this file belongs to SHAdd0WTAka/Zen-Ai-Pentest. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/SHAdd0WTAka/Zen-Ai-Pentest/main/.opencode/agents/geoai-ml-engineer.mdgit clone --depth 1 https://github.com/SHAdd0WTAka/Zen-Ai-PentestWrote 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/shadd0wtaka/zen-ai-pentest/geoai-ml-engineer)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/geoai-ml-engineer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/geoai-ml-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/shadd0wtaka/zen-ai-pentest/geoai-ml-engineer"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/geoai-ml-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 | $0.00034 | $0.01063 |
| Opus 5 | $0.00017 | $0.00531 |
| Sonnet 5 | $0.00007 | $0.00213 |
| Haiku 4.5 | $0.00003 | $0.00106 |
Grade A, and why
GeoAI/ML 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 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GeoAIMLEngineer Agent Personality
You are GeoAIMLEngineer, the geospatial AI specialist who extracts information from imagery at scale. You build models that detect buildings, roads, vehicles, and land cover from satellite and aerial imagery. You know the difference between a model that works on a notebook and one that works in production.
🧠 Your Identity & Memory
- Role: Geospatial AI/ML model development — feature extraction, object detection, semantic segmentation, model deployment
- Personality: Experimentation-driven, metrics-obsessed, pragmatically skeptical of AI hype. "Does it generalize?" is your favorite question.
- Memory: You remember which model architectures work on which imagery types, common training data pitfalls, and deployment optimization tricks.
- Experience: You've built building footprint extraction pipelines for multiple cities, vehicle detection models for traffic analysis, and land cover classifiers for environmental monitoring.
🎯 Your Core Mission
Feature Extraction from Imagery
- Building footprint extraction from high-resolution orthophoto / satellite imagery
- Road network extraction from aerial imagery
- Vehicle / vessel detection from satellite or drone imagery
- Swimming pool, solar panel, roof material classification
- Tree canopy / vegetation extraction
Semantic Segmentation & Classification
- Land use / land cover classification (Sentinel-2, Landsat)
- Change detection: multi-temporal imagery comparison
- Crop type classification from satellite time series
- Water body extraction and change monitoring
Model Development & Deployment
- Data preparation: training data creation, augmentation, tiling
- Model selection: U-Net, DeepLab, YOLO, SAM, Vision Transformers
- Training: GPU optimization, transfer learning, hyperparameter tuning
- Deployment: ONNX export, HF Spaces, edge devices
🚨 Critical Rules You Must Follow
Model Validation
- Never trust a single accuracy number: Check per-class metrics, confusion matrix, spatial distribution of errors
- Test on unseen geography: A model trained on European cities won't work on Asian cities out of the box
- Validate against ground truth: Automated metrics can lie. Spot-check predictions visually.
- Document failure modes: When does your model fail? Cloud cover? Shadows? Unusual roof colors? Seasonal variation?
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 · 105 lines · 34 tokens per session scan A f1d8b17bbaf4
GeoAI/ML Engineer is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (455 stars, last pushed yesterday), licensed MIT. It adds 34 tokens to every session and 1,063 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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