annotate
Skill8 starsChanged 8 months agoMITView on GitHub
Claude visually inspects images and corrects/creates YOLO bounding box annotations using multimodal vision.
6 other projects use a version of this.
Adopted by
Most recently taken up by
- jazzpujols34/claude-skillstheir own version, first seen Oct 13, 2026
- dolanmiu/docxtheir own version, first seen Oct 8, 2026
- tance-mang/chinese-webnovel-skillstheir own version, first seen Jun 12, 2026
- plustar35/echo-readingtheir own version, first seen Jun 5, 2026
- tranhieutt/software_development_departmenttheir own version, first seen Apr 30, 2026
- marmutapp/superbased-claude-code-plugintheir own version, first seen Apr 12, 2026
Other versions6 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 6
- dolanmiu/docx★ 6ksame name
- plustar35/echo-reading★ 142same name
- tance-mang/chinese-webnovel-skills★ 97same name
- tranhieutt/software_development_department★ 71same name
- marmutapp/superbased-claude-code-plugin★ 1same name
- jazzpujols34/claude-skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository15 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- active-learningSkill
- analyze-resultsSkill
- auto-experimentSkill
- benchmarkSkill
- compare-modelsSkill
- cvat-deploySkill
- cvat-pullSkill
- cvat-pushSkill
- explain-resultsSkill
- monitor-trainingSkill
- review-annotationsSkill
- review-datasetSkill
- setup-projectSkill
- training-loopSkill
The file
We split this skill into the rules it gives an agent
2 rules. 0 are common, 1 is distinctive, and 1 is very specific to this project.
0
Common
Most of the 4 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 19
Read yolo-project.yaml for class names
- Distinctive: line 34
Prefer correcting existing boxes over creating from scratch
“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Annotate
Claude vision annotation correction using multi-pass refinement.
STATUS: EXPERIMENTAL
Approach
- YOLO inference at low confidence (0.15) → precise boxes
- Claude vision review → keeps good, removes bad, identifies missed
- For missed objects → YOLO on cropped region
- Final Claude validation
Workflow
- Read
yolo-project.yamlfor class names - For each image:
a. Draw existing annotations:
python scripts/draw_annotations.pyb. Claude reads annotated image c. Evaluate: keep / correct / remove / add d. Write corrected YOLO label
Coordinate Guide
- (0.0, 0.0) = top-left, (1.0, 1.0) = bottom-right
- (0.5, 0.5) = center
- Width/height relative to image
Limitations
- ~10-15% coordinate error
- Best for objects > 5% of image area
- Prefer correcting existing boxes over creating from scratch
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.