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 w1ne/kernelCAD-web --skill photo-to-devicegit clone --depth 1 https://github.com/w1ne/kernelCAD-webWrote 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/w1ne/kernelcad-web/photo-to-device)<a href="https://agentmods.dev/skills/w1ne/kernelcad-web/photo-to-device"><img src="https://agentmods.dev/badge/skills/w1ne/kernelcad-web/photo-to-device/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/w1ne/kernelcad-web/photo-to-device"><img src="https://agentmods.dev/badge/skills/w1ne/kernelcad-web/photo-to-device.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00037 | $0.00777 |
| Opus 5 | $0.00018 | $0.00388 |
| Sonnet 5 | $0.00007 | $0.00155 |
| Haiku 4.5 | $0.00004 | $0.00078 |
Grade A, and why
photo-to-device 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 9d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
photo-to-device
Build portable kernelCAD from photo evidence: one image establishes scale, not hidden construction.
Use this route
Use for a simple front-on e-reader, remote, meter, controller, display housing, or passive enclosure with one known dimension. Escalate robots and mechanisms to the dedicated assembly workflow; require multi-view contracts for joints, transmissions, hardware, and unknown internals.
Current active Studio/server boundary
The current active Studio sends a typed photo reference to the hosted server, validating MIME/size, computing SHA-256 provenance, and removing it after the run. Do not use legacy chat/HeadlessKernel or a client-side hash.
Reference asset rule — overrides generic overlay guidance
This rule controls generic referenceImage() advice from
use-the-available-kernel and blockout-model.
- An active Studio/server uploaded photo is managed. Never emit
referenceImage()or its temporary/managed asset path in.kcad.ts. Instead add a source comment with filename and server SHA-256 (never a path), then model only from the validated photo brief. - A checked-in/local source-owned asset may use
referenceImage('./reference.jpg', opts)as a durable overlay.
Record photo reference provenance in the source comment and validated photo brief: filename, MIME, server SHA-256, known dimension in millimetres, observed facts, and inferred assumptions.
Core flow
- In
// Real Object Brief, record observed facts (outline, screen, seams, controls, ports, ratios, measured span) and inferred facts (depth, back, wall, internals), marking every inference as an assumption. - Parameterize the known dimension and derived front face; fit the housing envelope before controls or fillets.
- Model real assemblies/parts: use a named static assembly for distinct housing, screen/lens, controls, bezels, or inserts—not cosmetic overlaps. A physically one-piece case may remain one part.
- Add detail only after front, top, and iso views agree with the brief.
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.
- 9d ago First seen · 82 lines · 37 tokens per session scan A 5b5413fdfa24
photo-to-device is a skill published in the GitHub repository w1ne/kernelCAD-web (19 stars, last pushed 11d ago), licensed MIT. It adds 37 tokens to every session and 777 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 2026-08-30.
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