skimage-regionprops-feature-extraction

skimage-regionprops-feature-extraction is a skill for Claude Code, Codex from ma-compbio-lab/SkillFoundry. It costs 0 tokens per session (337 once invoked), scanned A, original, Apache-2.0.

A reproducible scikit-image workflow that creates a toy grayscale image, separates connected bright objects, and records each object's shape and brightness measurements. scikit-image is a Python library for image processing.

In plain words
What is it for?
Use it to measure the morphology and intensity of labeled objects in a sample image and produce compact JSON for tests or starter workflows.
Why use it?
It provides a small, repeatable example for testing image-feature extraction before building a larger pipeline. The fixed input and JSON output make results easier to check.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is ./slurm/envs/scientific-python/bin/python \.

Good fit Use it to measure the morphology and intensity of labeled objects in a sample image and produce compact JSON for tests or starter workflows.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/ma-compbio-lab/SkillFoundry
agentmods
npx agentmods add skills/ma-compbio-lab/skillfoundry/skimage-regionprops-feature-extraction

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for skimage-regionprops-feature-extraction

README.md
[![agentmods](https://agentmods.dev/badge/skills/ma-compbio-lab/skillfoundry/skimage-regionprops-feature-extraction/github.svg)](https://agentmods.dev/skills/ma-compbio-lab/skillfoundry/skimage-regionprops-feature-extraction)
Your own site
<a href="https://agentmods.dev/skills/ma-compbio-lab/skillfoundry/skimage-regionprops-feature-extraction"><img src="https://agentmods.dev/badge/skills/ma-compbio-lab/skillfoundry/skimage-regionprops-feature-extraction/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.

agentmods 80×15 button for skimage-regionprops-feature-extraction

Your own site · 80×15
<a href="https://agentmods.dev/skills/ma-compbio-lab/skillfoundry/skimage-regionprops-feature-extraction"><img src="https://agentmods.dev/badge/skills/ma-compbio-lab/skillfoundry/skimage-regionprops-feature-extraction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 337 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.00337
Opus 5 $0.00000 $0.00169
Sonnet 5 $0.00000 $0.00067
Haiku 4.5 $0.00000 $0.00034

Measured 5d ago against content hash 28b547cdd9d7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

skimage-regionprops-feature-extraction 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 5d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/run_skimage_regionprops_features.py, tests/test_run_skimage_regionprops_features.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/imaging-and-phenotype-analysis/skimage-regionprops-feature-extraction/SKILL.md · 35 lines

What it actually says

scikit-image Regionprops Feature Extraction

Use this skill to extract deterministic morphology and intensity features from a toy image with scikit-image regionprops_table.

What it does

  • creates a reproducible grayscale toy image with three bright ellipse-like objects
  • thresholds and labels connected components locally
  • summarizes per-object morphology and intensity features into compact JSON
  • keeps the output stable enough for smoke tests and starter workflows

When to use it

  • You need a local feature-extraction starter in the scikit-image ecosystem.
  • You want a minimal handoff point before building larger imaging or phenotype-analysis pipelines.

Example

./slurm/envs/scientific-python/bin/python \
  skills/imaging-and-phenotype-analysis/skimage-regionprops-feature-extraction/scripts/run_skimage_regionprops_features.py \
  --threshold 0.35 \
  --out scratch/skimage-regionprops/summary.json

Verification

  • Skill-local tests: python3 -m unittest discover -s skills/imaging-and-phenotype-analysis/skimage-regionprops-feature-extraction/tests -p 'test_*.py'
  • Asset regeneration: ./slurm/envs/scientific-python/bin/python skills/imaging-and-phenotype-analysis/skimage-regionprops-feature-extraction/scripts/run_skimage_regionprops_features.py --out skills/imaging-and-phenotype-analysis/skimage-regionprops-feature-extraction/assets/toy_regionprops_summary.json

Notes

  • This is a starter for the taxonomy leaf feature-extraction.
  • The image is synthetic and deterministic; it is intended for scaffolding and verification rather than biological interpretation.
Files

What ships with it

8 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.

Changes

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.

  1. 5d ago First seen · 35 lines · 0 tokens per session scan A 28b547cdd9d7

Subscribe to this mod's changes

skimage-regionprops-feature-extraction is a skill published in the GitHub repository ma-compbio-lab/SkillFoundry (38 stars, last pushed 4mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 337 tokens. 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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