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.
npx skills add curiositech/some_claude_skills --skill drone-cv-expertgit 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/skills/curiositech/some_claude_skills/drone-cv-expert)<a href="https://agentmods.dev/skills/curiositech/some_claude_skills/drone-cv-expert"><img src="https://agentmods.dev/badge/skills/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/skills/curiositech/some_claude_skills/drone-cv-expert"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/drone-cv-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00149 | $0.02157 |
| Opus 5 | $0.00075 | $0.01078 |
| Sonnet 5 | $0.00030 | $0.00431 |
| Haiku 4.5 | $0.00015 | $0.00216 |
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 11d 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 — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Drone CV Expert
Expert in robotics, drone systems, and computer vision for autonomous aerial platforms.
Decision Tree: When to Use This Skill
User mentions drones or UAVs?
├─ YES → Is it about inspection/detection of specific things (fire, roof damage, thermal)?
│ ├─ YES → Use drone-inspection-specialist
│ └─ NO → Is it about flight control, navigation, or general CV?
│ ├─ YES → Use THIS SKILL (drone-cv-expert)
│ └─ NO → Is it about GPU rendering/shaders?
│ ├─ YES → Use metal-shader-expert
│ └─ NO → Use THIS SKILL as default drone skill
└─ NO → Is it general object detection without drone context?
├─ YES → Use clip-aware-embeddings or other CV skill
└─ NO → Probably not a drone question
Core Competencies
Flight Control & Navigation
- PID Tuning: Position, velocity, attitude control loops
- SLAM: ORB-SLAM, LSD-SLAM, visual-inertial odometry (VIO)
- Path Planning: A*, RRT, RRT*, Dijkstra, potential fields
- Sensor Fusion: EKF, UKF, complementary filters
- GPS-Denied Navigation: AprilTags, visual odometry, LiDAR SLAM
Computer Vision
- Object Detection: YOLO (v5/v8/v10), EfficientDet, SSD
- Tracking: ByteTrack, DeepSORT, SORT, optical flow
- Edge Deployment: TensorRT, ONNX, OpenVINO optimization
- 3D Vision: Stereo depth, point clouds, structure-from-motion
Hardware Integration
- Flight Controllers: Pixhawk, Ardupilot, PX4, DJI
- Protocols: MAVLink, DroneKit, MAVSDK
- Edge Compute: Jetson (Nano/Xavier/Orin), Coral TPU
- Sensors: IMU, GPS, barometer, LiDAR, depth cameras
Anti-Patterns to Avoid
1. "Simulation-Only Syndrome"
Wrong: Testing only in Gazebo/AirSim, then deploying directly to real drone. Right: Simulation → Bench test → Tethered flight → Controlled environment → Field.
2. "EKF Overkill"
Wrong: Using Extended Kalman Filter when complementary filter suffices. Right: Match filter complexity to requirements:
- Complementary filter: Basic stabilization, attitude only
- EKF: Multi-sensor fusion, GPS+IMU+baro
- UKF: Highly nonlinear systems, aggressive maneuvers
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 200 lines · 149 tokens per session scan A f2327c0c05b9
drone-cv-expert is a skill published in the GitHub repository curiositech/some_claude_skills (219 stars, last pushed 5d ago), licensed MIT. It adds 149 tokens to every session and 2,157 once invoked, about $0.0007 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 2026-08-30.
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