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Get Started Free →Analyze images, screenshots, diagrams, and visual content - Use when you need to understand visual content like screenshots, architecture diagrams, UI mockups, or error screenshots.
.claude/skills/aiskillstore-vision/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 41% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 27% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 58% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -23% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -11% | 0% |
Analyze images using GPT-4o Vision through the verging.ai proxy API. Supports both image URL and file upload.
All requests require an API key in the Authorization header:
Authorization: ApiKey vrg_sk_xxxReplace vrg_sk_xxx with your verging.ai API key. You can generate one at https://verging.ai under your account settings.
POST https://verging.ai/api/v1/ai/vision
Content-Type: application/jsonPOST https://verging.ai/api/v1/ai/vision/upload
Content-Type: multipart/form-data| Parameter | Type | Required | Default | Description | |------------|--------|----------|---------|------------------------------------------| | prompt | string | Yes | — | Analysis instruction / question | | image_url | string | Yes | — | URL of the image to analyze | | model | string | No | gpt-4o | Vision model | | max_tokens | int | No | 1024 | Maximum tokens in the response |
| Parameter | Type | Required | Default | Description | |------------|--------|----------|---------|------------------------------------------| | prompt | string | Yes | — | Analysis instruction / question | | file | file | Yes | — | Image file to analyze | | model | string | No | gpt-4o | Vision model | | max_tokens | int | No | 1024 | Maximum tokens in the response |
Requires a minimum balance of 5 credits before calling (post-deduct billing).
bashcurl -X POST https://verging.ai/api/v1/ai/vision \ -H "Authorization: ApiKey vrg_sk_xxx" \ -H "Content-Type: application/json" \ -d '{ "prompt": "Describe what you see in this image in detail.", "image_url": "https://example.com/photo.jpg", "max_tokens": 512 }'
bashcurl -X POST https://verging.ai/api/v1/ai/vision/upload \ -H "Authorization: ApiKey vrg_sk_xxx" \ -F "prompt=What objects are in this image?" \ -F "file=@photo.jpg"
json{ "content": "The image shows a modern office space with several desks...", "usage": { "prompt_tokens": 1100, "completion_tokens": 95, "total_tokens": 1195 }, "credits_consumed": 2 }
content — The analysis result text.usage — Token usage breakdown.credits_consumed — Credits deducted for this request.| Component | Rate | |------------|-------------------------| | Base fee | 2 credits | | Input tokens | 1 credit / 10K tokens | | Output tokens | 3 credits / 10K tokens |
Minimum charge: 2 credits per request.
| HTTP Status | Error Code | Description | |-------------|------------|--------------------------------------------------| | 402 | 40001 | Insufficient credits (minimum 5 required) | | 429 | — | Rate limit exceeded (check X-RateLimit-Reset) | | 502 | — | Upstream provider error | | 503 | — | Service unavailable or upstream timeout |
Error response format:
json{ "error_code": 40001, "message": "Insufficient credits. Required: 5, available: 2", "upstream_error": null }
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 5,647 | 10,314 | +83% | 1 | 1 | 0% | 1,271 | 1,788 | +41% | 0 | 0 | — |
case-02 | fail→pass | 5,941 | 3,116 | -48% | 1 | 1 | 0% | 1,292 | 1,640 | +27% | 0 | 0 | — |
case-03 | fail→pass | 6,773 | 5,457 | -19% | 1 | 1 | 0% | 1,542 | 2,439 | +58% | 0 | 0 | — |
case-04 | fail→pass | 9,169 | 1,463 | -84% | 1 | 1 | 0% | 1,571 | 1,207 | -23% | 0 | 0 | — |
case-05 | fail→pass | 8,225 | 1,896 | -77% | 1 | 1 | 0% | 1,440 | 1,278 | -11% | 0 | 0 | — |
case-06 | fail→pass | 8,498 | 2,476 | -71% | 1 | 1 | 0% | 1,470 | 1,509 | +3% | 0 | 0 | — |
case-07 | fail→pass | 7,739 | 1,259 | -84% | 1 | 1 | 0% | 1,328 | 1,212 | -9% | 0 | 0 | — |
case-08 | fail→pass | 16,605 | 2,833 | -83% | 1 | 1 | 0% | 3,187 | 1,608 | -50% | 0 | 0 | — |
case-09 | fail→pass | 9,595 | 1,919 | -80% | 1 | 1 | 0% | 1,697 | 1,332 | -22% | 0 | 0 | — |
case-10 | fail→pass | 4,797 | 1,656 | -65% | 1 | 1 | 0% | 882 | 1,235 | +40% | 0 | 0 | — |
case-11 | fail→pass | 6,687 | 1,885 | -72% | 1 | 1 | 0% | 1,176 | 1,332 | +13% | 0 | 0 | — |
case-12 | pass→pass | 8,590 | 1,609 | -81% | 1 | 1 | 0% | 1,553 | 1,292 | -17% | 0 | 0 | — |
case-13 | fail→pass | 7,680 | 1,608 | -79% | 1 | 1 | 0% | 1,511 | 1,293 | -14% | 0 | 0 | — |
case-14 | fail→pass | 5,325 | 1,723 | -68% | 1 | 1 | 0% | 997 | 1,295 | +30% | 0 | 0 | — |
case-15 | fail→pass | 10,397 | 1,888 | -82% | 1 | 1 | 0% | 1,409 | 1,352 | -4% | 0 | 0 | — |
case-16 | pass→pass | 12,266 | 2,034 | -83% | 1 | 1 | 0% | 2,176 | 1,352 | -38% | 0 | 0 | — |
case-17 | fail→pass | 16,345 | 1,738 | -89% | 1 | 1 | 0% | 3,023 | 1,330 | -56% | 0 | 0 | — |
case-18 | pass→pass | 6,884 | 1,972 | -71% | 1 | 1 | 0% | 1,225 | 1,372 | +12% | 0 | 0 | — |
case-19 | fail→pass | 4,716 | 1,395 | -70% | 1 | 1 | 0% | 675 | 1,221 | +81% | 0 | 0 | — |
case-20 | pass→pass | 3,729 | 3,862 | +4% | 1 | 1 | 0% | 624 | 1,684 | +170% | 0 | 0 | — |
case-21 | pass→pass | 2,441 | 3,205 | +31% | 1 | 1 | 0% | 412 | 1,553 | +277% | 0 | 0 | — |
case-22 | pass→pass | 3,445 | 3,792 | +10% | 1 | 1 | 0% | 368 | 1,650 | +348% | 0 | 0 | — |
case-23 | fail→pass | 8,711 | 3,103 | -64% | 1 | 1 | 0% | 1,496 | 1,589 | +6% | 0 | 0 | — |
case-24 | pass→pass | 8,267 | 2,159 | -74% | 1 | 1 | 0% | 1,377 | 1,355 | -2% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted. The headline lift of +71 percentage points is the difference between those two pass rates over the 24 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 8/21/2026 | +9% |
Other measured skills in the registry, with their headline benchmark lift.