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 agentmods add skills/gaelic-ghost/socket/arkit-spatial-sensing-workflownpx skills add gaelic-ghost/socket --skill arkit-spatial-sensing-workflowgit clone --depth 1 https://github.com/gaelic-ghost/socketWhat 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 | $0.00081 | $0.01253 |
| Opus 5 | $0.00041 | $0.00626 |
| Sonnet 5 | $0.00016 | $0.00251 |
| Haiku 4.5 | $0.00008 | $0.00125 |
Grade A, and why
arkit-spatial-sensing-workflow 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 2d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ARKit Spatial Sensing Workflow
Purpose
Guide ARKit environment sensing while preserving the platform-specific session model, typed spatial data, tracking quality, authorization, privacy, and rendering/scanning handoffs.
When To Use
- Use for world tracking, planes, anchors, ray casting, scene depth, LiDAR reconstruction, meshes, environment understanding, maps, relocalization, and visionOS ARKit providers.
- Recommend
camera-capture-depth-workflowfor camera controls, photo/video capture, calibrated camera depth, or synchronized camera outputs. - Recommend
arkit-face-body-tracking-workflowfor TrueDepth face geometry or body skeleton tracking.
Single-Path Workflow
- Classify the spatial task:
- world tracking and tracking-state repair
- plane, image, object, or geographic detection
- ray casting, hit testing, feature points, or measurement
- scene depth or smoothed scene depth
- scene reconstruction, mesh geometry, or classification
- environment texturing or probes
- world-map persistence, sharing, or relocalization
- visionOS authorization and provider lifecycle
- Apply the Apple docs gate:
- read current ARKit documentation for the selected platform and capability
- state the documented behavior relied on
- apply
../../shared/references/apple-spatial-data-privacy-contract.md - check
supportsFrameSemantics,supportsSceneReconstruction, configuration support, provider support, authorization, and device capability before configuration
- Choose the platform shape:
- on iOS/iPadOS, configure and own
ARSessionwith the appropriateARConfiguration - on visionOS, request the documented
ARKitSessionauthorization and run only the required data providers - do not hide those different lifecycles behind a generic spatial session
- on iOS/iPadOS, configure and own
- Preserve spatial evidence:
- keep anchor identifiers, transforms, timestamps, tracking state, world origin, coordinate conventions, depth confidence, mesh geometry/classification, map state, and provider events typed
- distinguish estimated geometry from measured truth
- Define presentation and scanning handoffs:
- use RealityKit for entity/scene presentation and interaction when appropriate
- use RoomPlan for documented room-capture and structured-room workflows
- use SceneKit only for an existing SceneKit rendering surface or a documented requirement
- use Metal for advanced rendering or compute that RealityKit/Core Image cannot express
- Return documented behavior, capability and authorization evidence, platform session/provider plan, coordinate and data lifecycle, privacy policy, diagnostics, validation, and handoffs.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 89 lines · 81 tokens per session scan A ce3510d00f8c
arkit-spatial-sensing-workflow is a skill published in the GitHub repository gaelic-ghost/socket (7 stars, last pushed 7d ago), licensed Apache-2.0. It adds 81 tokens to every session and 1,253 once invoked, about $0.0004 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 2026-08-31.
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