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 G1Joshi/Agent-Skills --skill opencvgit clone --depth 1 https://github.com/G1Joshi/Agent-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/g1joshi/agent-skills/opencv)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/opencv"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/opencv/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/g1joshi/agent-skills/opencv"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/opencv.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 | $0.00013 | $0.00283 |
| Opus 5 | $0.00006 | $0.00142 |
| Sonnet 5 | $0.00003 | $0.00057 |
| Haiku 4.5 | $0.00001 | $0.00028 |
Grade A, and why
opencv 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.
What it actually says
OpenCV
OpenCV is the fundamental library for Image Processing. v5.0 (2025) modernizes deep learning support and licensing.
When to Use
- Image Manipulation: Resizing, cropping, color space conversion (BGR -> RGB).
- Classic CV: Edge detection (Canny), Feature matching (SIFT/ORB).
- Video I/O: Reading/Writing webcams or video files.
Core Concepts
BGR
OpenCV reads images as Blue-Green-Red (not RGB) by default. History quirks.
cv::Mat
The core matrix structure (in C++). In Python, it's just a NumPy array.
DNN Module
Running darknet/onnx models directly in OpenCV (lightweight inference).
Best Practices (2025)
Do:
- Use it for Preprocessing:
cv2.resize()is highly optimized. - Use
headless:pip install opencv-python-headlessfor server deployments (smaller, no GUI deps).
Don't:
- Don't implement Deep Learning training: Use PyTorch. Use OpenCV only for inference/preprocessing.
References
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 · 44 lines · 13 tokens per session scan A d62353362b50
opencv is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 7mo ago), licensed MIT. It adds 13 tokens to every session and 283 once invoked, about $0.0001 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.
Other skills, from other repositories
nature-downloader
Use when a user needs lawful academic full text, CNKI institutional access, English OA retrieval, publisher API access, institutional browser fallback, or supporting information downloads.
nature-ref-verifier
A reference-checking workflow for academic papers, proposals, and BibTeX files. It compares citation details such as authors, titles, dates, pages, and DOIs across available sources.
nature-experiment-log
A system for turning raw experiment material—images, voice transcripts, or text—into standardised experiment logs with YAML metadata in an Obsidian vault. Obsidian is a note-taking application that stores linked files as plain text.
researchwrite
Proposal-first scientific writing pipeline. Three modes (compose/revise/hybrid) with four-layer QA pipeline. Enforces evidence-before-prose, argument-before-sections, and contracts-before-paragraphs.
nature-reviewer
A reviewer-style assessment guide for Nature submissions. It evaluates a manuscript from a referee's perspective, including originality, importance, technical soundness, and readability for non-specialists.
dicom-medical-imaging
Production DICOM medical imaging standards, DICOMweb RESTful services (WADO-RS, STOW-RS, QIDO-RS), PACS integration, anonymization/de-identification, and Cornerstone.js web rendering.