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 cxcscmu/SkillLearnBench --skill pillow-grayscalegit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote 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/cxcscmu/skilllearnbench/pillow-grayscale)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/pillow-grayscale"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/pillow-grayscale.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high Rogue Agent · line 20 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
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 | $0.00023 | $0.00312 |
| Opus 5 | $0.00012 | $0.00156 |
| Sonnet 5 | $0.00005 | $0.00062 |
| Haiku 4.5 | $0.00002 | $0.00031 |
Grade A, and why
pillow-grayscale 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 4d 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
Pillow Grayscale Conversion
Installation
pip install Pillow
Convert Single Image In-Place
from PIL import Image
def convert_to_grayscale_inplace(image_path):
img = Image.open(image_path)
gray = img.convert("L") # "L" mode = 8-bit grayscale
gray.save(image_path) # overwrite the original file
Convert Multiple Images
from PIL import Image
import glob
def batch_convert_grayscale(pattern):
for path in glob.glob(pattern):
img = Image.open(path).convert("L")
img.save(path)
Modes Reference
| Mode | Description |
|---|---|
"L" |
8-bit grayscale (0-255) |
"RGB" |
24-bit color |
"RGBA" |
32-bit color with alpha |
"1" |
1-bit black & white |
Notes
.convert("L")handles all source formats (RGB, RGBA, etc.)- Saving back to PNG preserves lossless quality
- After conversion, the file on disk is grayscale but retains the
.pngextension - OpenCV's
cv2.imread()will still read the file; usecv2.IMREAD_GRAYSCALEor the image will have 3 identical channels
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.
- 4d ago First seen · 47 lines · 23 tokens per session scan A 2dcdfa866b51
pillow-grayscale is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 2mo ago), licensed MIT. It adds 23 tokens to every session and 312 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 2026-09-03.
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