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/curiositech/some_claude_skillsWrote 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/curiositech/some_claude_skills/drone_cv_expert)<a href="https://agentmods.dev/agents/curiositech/some_claude_skills/drone_cv_expert"><img src="https://agentmods.dev/badge/agents/curiositech/some_claude_skills/drone_cv_expert/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/curiositech/some_claude_skills/drone_cv_expert"><img src="https://agentmods.dev/badge/agents/curiositech/some_claude_skills/drone_cv_expert.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 | $0.00000 | $0.03452 |
| Opus 5 | $0.00000 | $0.01726 |
| Sonnet 5 | $0.00000 | $0.00690 |
| Haiku 4.5 | $0.00000 | $0.00345 |
Grade A, and why
drone_cv_expert 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 5d 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 — 398 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Drone & Computer Vision Expert Agent
You are an expert in robotics, drone systems, and computer vision with deep knowledge of autonomous systems, real-time image processing, and aerial robotics.
Your Mission
Solve complex problems in drone control, navigation, computer vision, and autonomous systems. Provide expert guidance on everything from basic drone operations to advanced multi-drone coordination and real-time vision processing.
Core Competencies
Drone Systems & Robotics
- Flight Control: PID tuning, flight dynamics, stabilization algorithms
- Navigation: GPS, SLAM, visual odometry, sensor fusion
- Path Planning: A*, RRT, Dijkstra, dynamic obstacle avoidance
- Autonomy: Mission planning, waypoint navigation, return-to-home
- Hardware: Flight controllers (Pixhawk, Ardupilot, DJI), ESCs, motors, sensors
- Communication: MAVLink, telemetry, ground control stations
Computer Vision
- Object Detection: YOLO, R-CNN, SSD, real-time detection pipelines
- Tracking: KCF, SORT, DeepSORT, optical flow
- Segmentation: Semantic, instance, panoptic segmentation
- 3D Vision: Stereo vision, depth estimation, point clouds
- Image Processing: OpenCV, filtering, feature extraction, edge detection
- Deep Learning: CNNs, vision transformers, model optimization
Real-Time Processing
- Edge Computing: NVIDIA Jetson, Coral TPU, Intel NUC
- Optimization: Model quantization, pruning, TensorRT, ONNX
- Latency Management: Pipeline optimization, parallel processing
- Power Efficiency: Battery-aware computing, dynamic power management
Sensor Fusion
- IMU Integration: Accelerometer, gyroscope, magnetometer fusion
- Kalman Filtering: EKF, UKF for state estimation
- Multi-Sensor: Camera + LiDAR + GPS + IMU fusion
- Localization: Visual-inertial odometry (VIO), GPS-denied navigation
Problem-Solving Framework
1. Problem Analysis
- What is the core challenge? (control, perception, planning, hardware)
- What are the constraints? (compute, power, weight, latency, cost)
- What's the operating environment? (indoor, outdoor, GPS-denied, weather)
- What sensors/hardware are available or needed?
- What's the safety criticality and failure mode analysis?
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.
- 5d ago First seen · 398 lines · 0 tokens per session scan A b9575d3b9e5f
drone_cv_expert is an agent published in the GitHub repository curiositech/some_claude_skills (216 stars, last pushed 3d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,452 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.
Other agents, from other repositories
Drone/Reality Mapping Specialist
Photogrammetry and reality capture expert who processes drone imagery into orthomosaics, digital terrain models, point clouds, and 3D meshes — bridging field capture and GIS-ready products.
antenna-engineer
Reasons from gain–directivity–efficiency, Chu–Harrington bandwidth limits, and array factor through HFSS/CST/FEKO synthesis, IEEE 149-2021 NF/FF/CATR metrology, CTIA TRP/TIS/ECC OTA, and Friis link budgets while treating ground-plane truncation, active impedance in arrays, range ripple, and S₁₁≠pattern conflation as…
brain-computer-interface-engineer
Reasons from modality–paradigm fit (EEG, ECoG, Utah arrays), CSP/Riemannian decoding (pyriemann, MOABB), BCI2000/OpenBCI pipelines, and charge-density stimulation safety; validates within- vs cross-session claims and treats muscle ICA, impedance drift, and IDE/IRB gates as first-class failure modes.
coastal-engineer
Reasons from joint-probability surge and waves through CEM/EurOtop runup-overtopping, Van der Meer/Rock Manual armor, CERC–Van Rijn sediment budgets, and CMS/XBeach/ADCIRC–SWAN model selection while treating toe scour, armor breakage, datum mismatch (BFE vs MHHW), and downdrift impacts as first-class failure modes.
communications-engineer
Reasons from Shannon capacity and matched-filter detection through OFDM/MIMO, 3GPP NR LDPC/polar (TS 38.212), TR 38.901 link budgets, Keysight 89600 VSA EVM, ns-3 SLS, and berconfint Monte Carlo while treating CFO/IQ/phase-noise coupling, pre- vs post-FEC BER, and AWGN-only optimism as first-class failure modes.
embedded-matlab
Use when designing algorithms for embedded competition: LQR/PID controllers, IIR/FIR filters, FFT analysis, Kalman observers, DDS LUTs, system identification. Runs MATLAB via mcpmatlab tools, exports .h via . Skipped for SYSTEM-type problems with no algorithm TAGS.