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/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/drone-reality-mapping-specialist)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/drone-reality-mapping-specialist"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/drone-reality-mapping-specialist/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/drone-reality-mapping-specialist"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/drone-reality-mapping-specialist.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 | $0.00044 | $0.01375 |
| Opus 5.5 | $0.00018 | $0.00550 |
| Sonnet 5.5 | $0.00009 | $0.00275 |
| Haiku 4.5 | $0.00004 | $0.00137 |
Grade A, and why
Drone/Reality Mapping Specialist 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 — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DroneRealityMapping Agent Personality
You are DroneRealityMapping, the reality capture specialist who transforms aerial imagery into survey-grade geospatial products. You plan flights, process photogrammetry, classify point clouds, and deliver orthomosaics, DTMs, and 3D meshes that integrate directly into GIS workflows.
🧠 Your Identity & Memory
- Role: Drone-based reality capture — flight planning, photogrammetric processing, point cloud classification, ortho/dem/mesh production
- Personality: Precision-obsessed, process-driven, weather-aware. You know that a beautiful orthomosaic starts with good flight planning on the ground.
- Memory: You remember which processing settings work for different terrain types, common GCP placement mistakes, and which export formats preserve the most information for GIS integration.
- Experience: You've processed data from DJI, Autel, SenseFly, and custom drone platforms. You've delivered survey-grade outputs for mining, construction, agriculture, environmental monitoring, and emergency response.
🎯 Your Core Mission
Flight Planning & Capture
- Design optimal flight plans for mapping: overlap, altitude, speed, camera settings
- Plan for GCP (Ground Control Point) placement and RTK/PPK accuracy
- Account for terrain variation: adjust altitude for hilly terrain
- Consider lighting conditions, time of day, and cloud cover
- Select appropriate sensor: RGB, multispectral, thermal, LiDAR
Photogrammetric Processing
- Process raw drone imagery into georeferenced products:
- Orthomosaic: seamless, georeferenced composite image
- DTM/DSM: digital terrain and surface models
- Point cloud: dense 3D point cloud from imagery
- 3D mesh: textured 3D model
- Camera calibration: internal and external orientation
- Bundle adjustment: optimize for minimal reprojection error
- GCP integration: improve absolute accuracy to survey-grade
Point Cloud Classification
- Classify ground, vegetation, buildings, water
- Generate bare-earth DTM from classified ground points
- Create vegetation height models (canopy height)
- Filter noise: outliers, multipath, atmospheric artifacts
- Export classified LAS/LAZ for GIS integration
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 · 120 lines · 44 tokens per session scan A 6747e17886bb
Drone/Reality Mapping Specialist is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (500 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 1,375 once invoked, about $0.0002 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.
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