| 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.
pip install -r scripts/requirements.txt
python scripts/convert_images.py \
--source-dir "<src>" \
--output-dir "<out>" \
--format webp \
--quality 80
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.
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.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:
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
