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 commands/adityaraj0421/naksha-studio/design-spatialgit clone --depth 1 https://github.com/Adityaraj0421/naksha-studioWrote 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/commands/adityaraj0421/naksha-studio/design-spatial)<a href="https://agentmods.dev/commands/adityaraj0421/naksha-studio/design-spatial"><img src="https://agentmods.dev/badge/commands/adityaraj0421/naksha-studio/design-spatial.svg" alt="Measured on agentmods" 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 | $0.00032 | $0.02829 |
| Opus 5 | $0.00016 | $0.01414 |
| Sonnet 5 | $0.00006 | $0.00566 |
| Haiku 4.5 | $0.00003 | $0.00283 |
Grade A, and why
design-spatial 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 5d 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/design-spatial
You are designing a complete spatial computing interface spec. Your output is a single structured document covering window type selection, depth hierarchy, ornament placement, spatial typography, and interaction model — all calibrated to the parsed app type, platform, and brief.
Input
Arguments: $ARGUMENTS
Parse the following from $ARGUMENTS:
- App type: productivity / media / utility / game (default: productivity)
- Platform: visionOS / WebXR (default: visionOS)
- Brief: any description of the app's purpose, primary task, or audience
Step 1: Load Knowledge Base
Read ${CLAUDE_PLUGIN_ROOT}/skills/design/references/spatial-designer.md to apply the full Spatial Designer knowledge base to every section below.
MCP Fallback: If the file cannot be read, proceed using inline knowledge. The inline knowledge for this command covers: visionOS window types (Window 2D for productivity, Volume for 3D objects, Immersive Space Full/Mixed for experiences); depth layers (Background 2–4m, Content 1–2m, Foreground 0.5–1m, Overlay <0.5m); ornament system (attachmentAnchor positioning, narrower than window, max 1 bar per edge); spatial typography (distance-size formula: recommended = 44pt × distance_m; SF Pro for visionOS, system-ui for WebXR; minimum weight Regular 400); gaze+pinch primary input for visionOS with 44pt minimum targets; vestibular comfort rules (no automatic locomotion, fade transitions 0.3s, stable horizon, no oscillating motion); eye strain prevention (no pure white backgrounds, WCAG AA minimum, rest areas in layout); dynamic type capped at .accessibility2 for body, .accessibility3 for display.
Step 2: Generate the Spec
Produce all five sections below as a single continuous output. Do not pause or ask for confirmation between sections.
Section 1: Window Type Selection
Based on the parsed app type and brief, recommend the primary window type from the visionOS or WebXR model.
For visionOS, select from:
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.
- 5d ago First seen · 183 lines · 32 tokens per session scan A b37a94c99065
design-spatial is a command published in the GitHub repository Adityaraj0421/naksha-studio (316 stars, last pushed 2mo ago), licensed MIT. It adds 32 tokens to every session and 2,829 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.
Other commands, from other repositories
full-diagnostic
Comprehensive design system health sweep.
describe-component
Generate an AI-optimised component description.
docs-coverage
Audit whether your documentation surface keeps pace with your components.
drift-check
Detect where teams are diverging from the system.
governance-review
Quarterly governance review package.
release-check
Pre-release validation pipeline for a component.