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 aeren23/image-processing-skills --skill 01-image-fundamentalsgit clone --depth 1 https://github.com/aeren23/image-processing-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/aeren23/image-processing-skills/01-image-fundamentals)<a href="https://agentmods.dev/skills/aeren23/image-processing-skills/01-image-fundamentals"><img src="https://agentmods.dev/badge/skills/aeren23/image-processing-skills/01-image-fundamentals/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/aeren23/image-processing-skills/01-image-fundamentals"><img src="https://agentmods.dev/badge/skills/aeren23/image-processing-skills/01-image-fundamentals.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 | $0.00024 | $0.01528 |
| Opus 5.5 | $0.00010 | $0.00611 |
| Sonnet 5.5 | $0.00005 | $0.00306 |
| Haiku 4.5 | $0.00002 | $0.00153 |
Grade A, and why
image-fundamentals 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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Fundamentals
When to Use This Skill
- Starting any new image processing pipeline
- Choosing between color spaces (BGR, RGB, HSV, Gray, LAB)
- Deciding image format for saving/loading
- Debugging coordinate-related bugs in OpenCV
- Calculating memory requirements for image data
Decision Framework
Color Space Selection
| Goal | Convert To | OpenCV Code | Why |
|---|---|---|---|
| Display with matplotlib | RGB | cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
Matplotlib expects RGB, OpenCV loads BGR |
| Grayscale processing | Gray | cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
Single channel, faster computation |
| Color-based object filtering | HSV | cv2.cvtColor(img, cv2.COLOR_BGR2HSV) |
Isolate hue independently from brightness |
| Perceptual color difference | LAB | cv2.cvtColor(img, cv2.COLOR_BGR2LAB) |
L=lightness, A/B=color, perceptually uniform |
| Print/publishing output | CMYK | External library | Subtractive color model for ink |
Format Selection
| Format | Use When | Compression | Quality Loss |
|---|---|---|---|
.jpg/.jpeg |
Web, general photos | Lossy | Yes — artifacts at low quality |
.png |
Transparency needed, lossless required | Lossless | No |
.tiff |
Medical/scientific, archival | Both | Depends on setting |
.dcm (DICOM) |
Clinical X-ray, MR, CT | Lossless | No |
.nii (NIfTI) |
Neuroimaging (brain MRI) | Lossless | No |
.gif |
Animations, indexed color | Lossless (256 colors) | Color palette limited |
Bit Depth Decision
| Bit Depth | Colors | Use Case | Memory per Pixel |
|---|---|---|---|
| 1-bit | 2 (B/W) | Binary masks, documents | 0.125 bytes |
| 8-bit gray | 256 shades | Standard grayscale processing | 1 byte |
| 24-bit (3×8) | 16.7M | Standard color (BGR/RGB) | 3 bytes |
| 32-bit float | Continuous | HDR, scientific computation | 4 bytes |
| 48-bit (3×16) | 281T | Medical, raw camera | 6 bytes |
Memory formula: width × height × channels × bytes_per_channel
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 · 150 lines · 24 tokens per session scan A 1cb22971d9a6
image-fundamentals is a skill published in the GitHub repository aeren23/image-processing-skills (7 stars, last pushed 5mo ago), licensed MIT. It adds 24 tokens to every session and 1,528 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
srt-whiteboard-animation
A workflow that turns SRT subtitle files into whiteboard-style hand-drawn animation videos. It plans scenes from the subtitles, draws them in flowing strokes, and renders an MP4.
crypto-fundamental
Crypto Fundamental Analysis — evaluates project viability, team quality, technology, adoption metrics, and competitive moat with a composite Fundamental Score (0-100).
agentic-patterns
Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent"…
cross-asset-relationships
Cross-asset and quantitative analysis: pair correlations, correlation heatmaps, currency strength, cross-timeframe divergence, intermarket analysis, market breadth, carry trades, swap rates, risk premia, and multi-pair baskets. USE FOR: correlation, currency strength, intermarket, market breadth, carry trade, swap…
research-synthesizer
Synthesizes technical, fundamental, macro, and sentiment signals into conviction-scored trade ideas across forex, stocks, and crypto. Use when scanning markets for opportunities, ranking trade ideas by conviction, deciding what to trade, or asking for a buy/sell signal or setup on an instrument.
edgrapi-full
All five US government sources via Edgrapi.com. Nine tools — SAM.gov contract opportunities, USAspending awards, Grants.gov funding and US House STOCK Act trades, plus normalized SEC EDGAR fundamentals, ratios, company profiles and filings, on one key and one JSON schema.