Review Annotations
AI-assisted annotation review — uses YOLO inference + Claude vision to auto-approve, correct, or flag images for human review in CVAT.
Install
$ agentstack add skill-macroman5-autotrain-yolo-review-annotations ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-macroman5-autotrain-yolo-review-annotations) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Review Annotations
Multi-pass annotation review: YOLO precision + Claude intelligence.
STATUS: EXPERIMENTAL
Workflow
1. Setup
Read yolo-project.yaml for classes, model path. Get review folder from user.
2. Prepare Output
Create: review_output/approved/, needs_review/, rejected/
3. Process Images (batches of 5-10)
For each image:
- Run YOLO at low confidence:
python scripts/multi_pass_annotate.py --image --model --conf 0.15 - Draw existing annotations:
python scripts/draw_annotations.py - Claude reads annotated image (multimodal)
- Judge: AUTO-APPROVE / AUTO-CORRECT / NEEDS HUMAN / REJECT
4. Generate Report
Write review_output/review_report.md with counts, corrections, common issues.
Guardrails
- Batch size 5-10 to avoid context overflow
- Never auto-approve if uncertain
- When uncertain → needs_review
- Generate CVAT export command for needs_review
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: MacroMan5
- Source: MacroMan5/autotrain-yolo
- License: MIT
Install and usage instructions live in the source repository linked above.
Reviews
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Versions
- v0.1.0 Imported from the upstream source.