菜单
Computer vision and media processing: CLI interface stability, coordinate alignment (row-major vs column-major), shape consistency after transformations, and output validation. Use for image processing, video analysis, object detection, segmentation.
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基于 SOC 职业分类
| name | vision-media |
| description | Computer vision and media processing: CLI interface stability, coordinate alignment (row-major vs column-major), shape consistency after transformations, and output validation. Use for image processing, video analysis, object detection, segmentation. |
Leading word: alignment — coordinates, dimensions, channels, and color spaces must be aligned between input and output. A misaligned pixel is a silent bug.
ffmpeg, ImageMagick, OpenCV CLI), verify the output format before further processing. Don't assume the tool produced what you asked for.pip install opencv-python pillow matplotlib numpy
Output validation procedures: references/output-validation.md
SKILL vision-media
INPUTS:
mediaType: string // image, video, mask, segmentation
inputFormat: string // png, jpg, mp4, nifti, dicom
processingOp: string // transform, detect, segment, classify
dimensions?: object // Expected width, height, channels
OUTPUTS:
processedMedia: binary // Processed image/video data
validationReport: object
// dimensions_match: bool
// color_channels: int
// file_integrity: bool
coordinateSystem: string // row-major or col-major, y-up or y-down
PRECONDITIONS:
- Input file integrity confirmed before processing
- Coordinate system explicitly declared
- Output format compatible with intended use
POSTCONDITIONS:
- Output dimensions match expected specification
- Color channels preserved or explicitly converted
- File integrity verified after processing
- Coordinate convention consistent between input and output
ERROR_HANDLING:
- Input file corrupt → abort with integrity report
- Dimension mismatch → reshape or error with details
- CLI tool produces unexpected output → re-verify format
Smart commit with Conventional Commits: groups logical changes, infers type + scope from file paths, and generates messages in `<type>(<scope>): <description>` format.
Iterative quality gate hardening loop — run the gate, parse failures, fix each failing category with a fresh-context delegate, re-run until all gates pass or diminishing returns are hit.
Relentless quality loop: alternate between a fresh-context implementer and a fresh-context reviewer until only diminishing-return issues remain. The reviewer is advisory-only (no state changes). The implementer starts fresh each cycle, unanchored from prior attempts. Terminates when max severity ≤ 3 for two consecutive cycles or the severity trend is flat.
Compact the current conversation into a handoff document for a fresh agent to pick up next session. Includes current phase, workspace state, memory state, and suggested skills.
Investigate a question against primary sources (official docs, arXiv papers, Semantic Scholar, source code, specs, first-party APIs) using the web_search tool. Writes structured findings as a Markdown file. Use when the user wants a topic researched, docs gathered, API facts investigated, or reading legwork delegated. Maps to P1 Explore phase.
Meta-cognitive improvement loop: reflect on errors, learn patterns, codify guardrails. The skill that rewrites skills. Always loaded.