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Agent skill

Image Processing

by aipoch in aipoch/medical-research-skills

Batch-convert and compress local images with Pillow; use when you need an offline, scriptable pipeline for directory-based processing.

MITAuto-check passed

Install Image Processing

skills CLI
$ npx skills add aipoch/medical-research-skills --skill image-processing -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aipoch/medical-research-skills image-processing --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/scientific-skills/Other/image-processing .claude/skills/image-processing && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
image-processing
GitHub stars
2k
Token cost
~1.4k tokens
SKILL.md length
620 words
Files
6 (incl. scripts, references)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

Batch-convert and compress local images with Pillow; use when you need an offline, scriptable pipeline for directory-based processing.

  • Works in 4 steps: Validate the request against the skill… → Select the documented execution path and… → Produce the expected output using the… → …
  • You need an offline
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 8 more sections
  • Runs Python scripts from its folder; calls pip and python

What it does

Image Processing is an agent skill from aipoch/medical-research-skills. Batch-convert and compress local images with Pillow; use when you need an offline, scriptable pipeline for directory-based processing.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `image-processing_audit_result_v2.json`, `references/examples.md` and `scripts/convert_images.py`).

The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • You need an offline
  • Scriptable pipeline for directory-based processing

Example prompts

  • “/image-processing”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Validate the request against the skill boundary and confirm all required inputs are present.
  2. Select the documented execution path and prefer the simplest supported command or procedure.
  3. Produce the expected output using the documented file format, schema, or narrative structure.
  4. Run a final validation pass for completeness, consistency, and safety before returning the result.

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • pip
    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Image Processing loads about 1.4k tokens when it runs, and up to ~1.6k if it reads all its reference files. Until then it costs about 38 tokens; SKILL.md has 620 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~38
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 620 words, ~1,444 tokens.

Download SKILL.mdSave it as .claude/skills/image-processing/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
image-processing
description
Batch-convert and compress local images with Pillow; use when you need an offline, scriptable pipeline for directory-based processing.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

  • You need to batch convert a folder of images into a single target format (e.g., WebP) for distribution.
  • You want to reduce file sizes via compression while keeping processing fully offline (no network calls).
  • You need a repeatable, scriptable pipeline for CI/local automation (e.g., preparing assets for a website/app).
  • You want to preserve the source directory structure in the output directory during conversion.
  • You need best-effort batch processing where individual file errors are reported but do not stop the entire run.

Key Features

  • Batch conversion of common image formats using Pillow.
  • Configurable output format (default: webp) and quality (default: 80).
  • Optional recursive traversal of subdirectories while preserving folder structure in output.
  • Overwrite policy control to prevent accidental replacement of existing outputs.
  • Summary reporting (counts, errors) printed to standard output.
  • Local-only operation: reads from a specified source directory and writes to a specified output directory.

Dependencies

  • Python >= 3.9
  • Pillow (installed via requirements file):
    • pip install -r scripts/requirements.txt

Example Usage

For additional examples, see references/examples.md.

bash
# 1) Install dependencies
pip install -r scripts/requirements.txt

# 2) Convert all images under <src> to WebP with quality 80, writing to <out>
python scripts/convert_images.py \
  --source-dir "<src>" \
  --output-dir "<out>" \
  --format webp \
  --quality 80

# 3) (Optional) Typical variants (flags may vary by implementation)
# - Enable recursion
# python scripts/convert_images.py --source-dir "<src>" --output-dir "<out>" --format webp --quality 80 --recursive
#
# - Allow overwriting existing outputs
# python scripts/convert_images.py --source-dir "<src>" --output-dir "<out>" --format webp --quality 80 --overwrite

Implementation Details

  • Processing engine: All conversions are performed via Pillow (no external binaries).
  • I/O boundaries:
    • Reads only from --source-dir.
    • Writes only to --output-dir.
    • No network access; no external APIs; no credentials required.
  • Batch behavior:
    • The script continues processing remaining files even if some files fail.
    • Errors are collected and summarized at the end.
  • Directory structure:
    • The relative path under --source-dir is preserved under --output-dir.
  • Format-specific save rules:
    • JPG/JPEG: converted/saved in RGB; uses the provided quality; enables progressive output.
    • PNG: uses a compression level derived from the quality parameter (higher quality typically implies lower compression and vice versa, depending on the mapping used by the script).
    • WebP: uses the provided quality and sets method=6 for encoding.
  • Default parameters:
    • Output format: webp
    • Quality: 80

When Not to Use

  • Do not use this skill when the required source data, identifiers, files, or credentials are missing.
  • Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
  • Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.

Required Inputs

  • A clearly specified task goal aligned with the documented scope.
  • All required files, identifiers, parameters, or environment variables before execution.
  • Any domain constraints, formatting requirements, and expected output destination if applicable.
Show full SKILL.md (243 more words)Show less
  1. Validate the request against the skill boundary and confirm all required inputs are present.
  2. Select the documented execution path and prefer the simplest supported command or procedure.
  3. Produce the expected output using the documented file format, schema, or narrative structure.
  4. Run a final validation pass for completeness, consistency, and safety before returning the result.

Output Contract

  • Return a structured deliverable that is directly usable without reformatting.
  • If a file is produced, prefer a deterministic output name such as image_processing_result.md unless the skill documentation defines a better convention.
  • Include a short validation summary describing what was checked, what assumptions were made, and any remaining limitations.

Validation and Safety Rules

  • Validate required inputs before execution and stop early when mandatory fields or files are missing.
  • Do not fabricate measurements, references, findings, or conclusions that are not supported by the provided source material.
  • Emit a clear warning when credentials, privacy constraints, safety boundaries, or unsupported requests affect the result.
  • Keep the output safe, reproducible, and within the documented scope at all times.

Failure Handling

  • If validation fails, explain the exact missing field, file, or parameter and show the minimum fix required.
  • If an external dependency or script fails, surface the command path, likely cause, and the next recovery step.
  • If partial output is returned, label it clearly and identify which checks could not be completed.

Quick Validation

Run this minimal verification path before full execution when possible:

bash
python scripts/convert_images.py --help

Expected output format:

text
Result file: image_processing_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any

© aipoch, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (scripts, references) in scientific-skills/Other/image-processing of aipoch/medical-research-skills.

  • SKILL.md
  • image-processing_audit_result_v2.json
  • references/examples.md
  • sample.ppm
  • scripts/convert_images.py
  • scripts/requirements.txt

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Image Processing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Image Processing compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Image Processing this skillaipoch/medical-research-skills2k—~1.4kAutomated safety check: PassMIT
Convertremotion-dev/remotion62k—~247Automated safety check: PassCustom licence
Bio Batch ProcessingFreedomIntelligence/OpenClaw-Medical-Skills3.1k1 repos~2.2kAutomated safety check: PassNone
Compressionthedaviddias/Front-End-Checklist74k—~421Automated safety check: PassMIT
Batchasgeirtj/system_prompts_leaks69k—~1.3kAutomated safety check: PassCC0-1.0
Batch ProcessZJU-REAL/Easel3.2k—~555Automated safety check: PassApache-2.0

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Questions about Image Processing

What does Image Processing do?

Batch-convert and compress local images with Pillow; use when you need an offline, scriptable pipeline for directory-based processing. Image Processing is an agent skill from aipoch/medical-research-skills. Batch-convert and compress local images with Pillow; use when you need an offline, scriptable pipeline for directory-based processing.

When should I use Image Processing?

Image Processing fits situations like: you need an offline; scriptable pipeline for directory-based processing.

How do I install Image Processing in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill image-processing -a claude-code`. Or copy the skill folder (scientific-skills/Other/image-processing in aipoch/medical-research-skills) into .claude/skills/image-processing in your project. Claude Code loads it when a task matches its description.

How do I install Image Processing in Codex?

Run `npx skills add aipoch/medical-research-skills --skill image-processing -a codex`. Or copy the skill folder (scientific-skills/Other/image-processing in aipoch/medical-research-skills) into .agents/skills/image-processing in your project. Codex loads it when a task matches its description.

Can I use Image Processing in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aipoch/medical-research-skills --skill image-processing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/image-processing, .gemini/skills/image-processing, .github/skills/image-processing and .opencode/skills/image-processing in your project.

What does Image Processing need to run?

Going by SKILL.md and its folder, Image Processing needs Python for the scripts in its folder and the command-line tools its instructions call (pip and python). Our summary lists: Python 3.

Does Image Processing access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Image Processing safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Image Processing use?

Image Processing is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Image Processing use?

About 1.4k tokens (SKILL.md is roughly 5.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 122 tokens, read only when the agent opens those files.

What are the alternatives to Image Processing?

Skills that share tags, products or a category with Image Processing: Convert (remotion-dev/remotion, 62k stars), Bio Batch Processing (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars), Compression (thedaviddias/Front-End-Checklist, 74k stars) and Batch (asgeirtj/system_prompts_leaks, 69k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Image Processing?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,974 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.