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 Buzzmatic/awesome-agent-skills --skill pillow-image-editorgit clone --depth 1 https://github.com/Buzzmatic/awesome-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/buzzmatic/awesome-agent-skills/pillow-image-editor)<a href="https://agentmods.dev/skills/buzzmatic/awesome-agent-skills/pillow-image-editor"><img src="https://agentmods.dev/badge/skills/buzzmatic/awesome-agent-skills/pillow-image-editor/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/buzzmatic/awesome-agent-skills/pillow-image-editor"><img src="https://agentmods.dev/badge/skills/buzzmatic/awesome-agent-skills/pillow-image-editor.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 | $0.00098 | $0.02116 |
| Opus 5 | $0.00049 | $0.01058 |
| Sonnet 5 | $0.00020 | $0.00423 |
| Haiku 4.5 | $0.00010 | $0.00212 |
Grade A, and why
pillow-image-editor 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pillow Image Editor
Programmatic image editing via Pillow (PIL fork). Pillow 10.4+ is installed in the system Python.
When to use
- Resize / crop / rotate / flip / transpose
- Format conversion (PNG, JPEG, WebP, GIF, AVIF, TIFF, BMP)
- Compression / quality tuning / file-size reduction
- Filters: blur, sharpen, edge detection, emboss, contour
- Adjustments: brightness, contrast, saturation, color balance
- Compositing: paste, alpha blend, transparency, masks
- Annotation: draw text, shapes, lines, watermarks, logos
- EXIF: read, strip, or preserve metadata
- Thumbnails, ICO/favicon generation, sprite sheets
- Batch processing a folder of images
For generating images from prompts, prefer gemini-imagegen or kie-ai. This skill is for deterministic pixel-level edits to existing files.
Quick start
Pillow is already installed. Import like this:
from PIL import Image, ImageDraw, ImageFilter, ImageOps, ImageEnhance, ImageFont, ExifTags
Minimal operations
# Resize (preserve aspect ratio, fit within box)
img = Image.open("in.jpg")
img.thumbnail((1200, 1200)) # modifies in place, keeps aspect
img.save("out.jpg", quality=85, optimize=True)
# Hard resize to exact dimensions
img.resize((800, 600), Image.Resampling.LANCZOS).save("out.png")
# Crop (left, upper, right, lower)
img.crop((100, 50, 900, 650)).save("cropped.png")
# Rotate / flip
img.rotate(90, expand=True).save("rotated.png")
ImageOps.mirror(img).save("flipped_horizontal.png")
ImageOps.flip(img).save("flipped_vertical.png")
# Convert format / color mode
Image.open("in.png").convert("RGB").save("out.jpg", quality=90) # drop alpha for JPEG
Filters & adjustments
from PIL import ImageFilter, ImageEnhance
img.filter(ImageFilter.GaussianBlur(radius=5))
img.filter(ImageFilter.UnsharpMask(radius=2, percent=150, threshold=3))
img.filter(ImageFilter.FIND_EDGES)
ImageEnhance.Brightness(img).enhance(1.2) # >1 brighter, <1 darker
ImageEnhance.Contrast(img).enhance(1.5)
ImageEnhance.Color(img).enhance(0.0) # 0.0 = grayscale, 1.0 = original
ImageEnhance.Sharpness(img).enhance(2.0)
What ships with it
1 file 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 · 201 lines · 0 tokens per session scan A 5303296eb1d3
pillow-image-editor is a skill published in the GitHub repository Buzzmatic/awesome-agent-skills (1 stars, last pushed 2mo ago), licensed MIT. It adds 98 tokens to every session and 2,116 once invoked, about $0.0005 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…