Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ComeOnOliver/skillshub --skill axiom-ios-visiongit clone --depth 1 https://github.com/ComeOnOliver/skillshubWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/comeonoliver/skillshub/axiom-ios-vision)<a href="https://agentmods.dev/skills/comeonoliver/skillshub/axiom-ios-vision"><img src="https://agentmods.dev/badge/skills/comeonoliver/skillshub/axiom-ios-vision/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/comeonoliver/skillshub/axiom-ios-vision"><img src="https://agentmods.dev/badge/skills/comeonoliver/skillshub/axiom-ios-vision.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00034 | $0.01193 |
| Opus 5 | $0.00017 | $0.00596 |
| Sonnet 5 | $0.00007 | $0.00239 |
| Haiku 4.5 | $0.00003 | $0.00119 |
Grade A, and why
axiom-ios-vision scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
iOS Computer Vision Router
You MUST use this skill for ANY computer vision work using the Vision framework.
When to Use
Use this router when:
- Analyzing images or video
- Detecting objects, faces, or people
- Tracking hand or body pose
- Segmenting people or subjects
- Lifting subjects from backgrounds
- Recognizing text in images (OCR)
- Detecting barcodes or QR codes
- Scanning documents
- Using VisionKit or DataScannerViewController
- Integrating with Visual Intelligence (iOS 26+ system camera feature)
Routing Logic
Vision Work
Implementation patterns → /skill axiom-vision
- Subject segmentation (VisionKit)
- Hand pose detection (21 landmarks)
- Body pose detection (2D/3D)
- Person segmentation
- Face detection
- Isolating objects while excluding hands
- Text recognition (VNRecognizeTextRequest)
- Barcode/QR detection (VNDetectBarcodesRequest)
- Document scanning (VNDocumentCameraViewController)
- Live scanning (DataScannerViewController)
- Structured document extraction (RecognizeDocumentsRequest, iOS 26+)
API reference → /skill axiom-vision-ref
- Complete Vision framework API
- VNDetectHumanHandPoseRequest
- VNDetectHumanBodyPoseRequest
- VNGenerateForegroundInstanceMaskRequest
- VNRecognizeTextRequest (fast/accurate modes)
- VNDetectBarcodesRequest (symbologies)
- DataScannerViewController delegates
- RecognizeDocumentsRequest (iOS 26+)
- Coordinate conversion patterns
Visual Intelligence integration → /skill axiom-vision-ref (see Visual Intelligence Integration section)
- Making app content discoverable to Visual Intelligence camera
IntentValueQueryandSemanticContentDescriptor- Deep linking from Visual Intelligence results
Diagnostics → /skill axiom-vision-diag
- Subject not detected
- Hand pose missing landmarks
- Low confidence observations
- Performance issues
- Coordinate conversion bugs
- Text not recognized or wrong characters
- Barcodes not detected
- DataScanner showing blank or no items
- Document edges not detected
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 153 lines · 34 tokens per session scan A c881f0a3c243
axiom-ios-vision is a skill published in the GitHub repository ComeOnOliver/skillshub (63 stars, last pushed 2mo ago), licensed MIT. It adds 34 tokens to every session and 1,193 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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