image-processing
Batch-convert and compress local images with Pillow; use when you need an offline, scriptable pipeline for directory-based processing.
Install / Use
npx skills add aipoch/medical-research-skills --skill image-processingInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
AutomationSupported Platforms
Our assessment of image-processing
image-processing scores 88/100 on our quality scale, 1143rd of 2,464 Automation skills we index (top 47%).
Its SKILL.md is 5.6 KB long, well organised into 19 sections with 3 code examples: a solid amount of guidance for an agent.
With 1,916 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 12 days ago, so image-processing is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
image-processing compared with similar skills
All 4 of these similar skills score higher than image-processing; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| image-processing (this skill)by aipoch | 88 | 1.9k | 12d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 86.2k | 14d ago | CLAUDE.md |
| rufloby ruvnet | 100 | 73.5k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 84.6k | today | MCP Server |
| LocalAIby mudler | 100 | 49.3k | today | MCP Server |
Frequently asked questions
- How do I install image-processing?
- Run
npx skills add aipoch/medical-research-skills --skill image-processing. The install tabs above show the steps for each supported agent. - Which AI agents does image-processing work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is image-processing safe to use?
- It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is image-processing still maintained?
- The repository was last updated 12 days ago, so image-processing is actively maintained.
Skill content
View source on GitHubname: 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
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.
# 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.
- Reads only from
- 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-diris preserved under--output-dir.
- The relative path under
- 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
qualityparameter (higher quality typically implies lower compression and vice versa, depending on the mapping used by the script). - WebP: uses the provided
qualityand setsmethod=6for encoding.
- JPG/JPEG: converted/saved in RGB; uses the provided
- Default parameters:
- Output format:
webp - Quality:
80
- Output format:
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.
Recommended Workflow
- Validate the request against the skill boundary and confirm all required inputs are present.
- Select the documented execution path and prefer the simplest supported command or procedure.
- Produce the expected output using the documented file format, schema, or narrative structure.
- 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.mdunless 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:
python scripts/convert_images.py --help
Expected output format:
Result file: image_processing_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
