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 sinaptik-ai/starpod --skill image-gengit clone --depth 1 https://github.com/sinaptik-ai/starpodWrote 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/sinaptik-ai/starpod/image-gen)<a href="https://agentmods.dev/skills/sinaptik-ai/starpod/image-gen"><img src="https://agentmods.dev/badge/skills/sinaptik-ai/starpod/image-gen/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/sinaptik-ai/starpod/image-gen"><img src="https://agentmods.dev/badge/skills/sinaptik-ai/starpod/image-gen.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 | $0.00073 | $0.02164 |
| Opus 5 | $0.00036 | $0.01082 |
| Sonnet 5 | $0.00015 | $0.00433 |
| Haiku 4.5 | $0.00007 | $0.00216 |
Grade A, and why
image-gen 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 12d 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 — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Generation & Processing
Quick Reference
| Task | Tool | Script |
|---|---|---|
| Batch resize | Pillow | python scripts/resize.py img.jpg --width 800 |
| Add watermark | Pillow | python scripts/watermark.py img.jpg "DRAFT" |
| Data charts | matplotlib + seaborn | See below |
| Diagrams / flowcharts | matplotlib + patches, or SVG | See below |
| Image processing | Pillow (PIL) | See below |
| Social media graphics | Pillow with text + shapes | See below |
| Simple icons / logos | SVG (write XML directly) | See below |
| Animated GIFs | Pillow frame assembly | See below |
Pillow — Image Processing
Resize and crop
from PIL import Image
img = Image.open("input.jpg")
# Resize maintaining aspect ratio
img.thumbnail((800, 600))
img.save("resized.jpg")
# Exact resize (may distort)
img_resized = img.resize((800, 600))
# Crop (left, upper, right, lower)
cropped = img.crop((100, 50, 500, 400))
cropped.save("cropped.jpg")
# Center crop to square
w, h = img.size
size = min(w, h)
left = (w - size) // 2
top = (h - size) // 2
square = img.crop((left, top, left + size, top + size))
Filters and adjustments
from PIL import ImageFilter, ImageEnhance
# Blur / sharpen
blurred = img.filter(ImageFilter.GaussianBlur(radius=3))
sharpened = img.filter(ImageFilter.SHARPEN)
# Brightness, contrast, saturation
enhancer = ImageEnhance.Brightness(img)
bright = enhancer.enhance(1.3) # 1.0 = original
enhancer = ImageEnhance.Contrast(img)
contrast = enhancer.enhance(1.5)
# Convert to grayscale
gray = img.convert("L")
Compositing and watermarks
from PIL import Image, ImageDraw, ImageFont
img = Image.open("photo.jpg")
draw = ImageDraw.Draw(img)
# Text watermark
font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 36)
draw.text((20, img.height - 50), "© 2025", fill=(255, 255, 255, 128), font=font)
# Overlay image (logo in corner)
logo = Image.open("logo.png").resize((100, 100))
img.paste(logo, (img.width - 110, 10), logo) # third arg = mask for transparency
img.save("watermarked.jpg")
What ships with it
2 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.
- 12d ago First seen · 233 lines · 73 tokens per session scan A 3509be61f699
image-gen is a skill published in the GitHub repository sinaptik-ai/starpod (78 stars, last pushed 5mo ago), licensed MIT. It adds 73 tokens to every session and 2,164 once invoked, about $0.0004 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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