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 autonomous-ai/autonomous-workshop --skill luma-vale-inventorgit clone --depth 1 https://github.com/autonomous-ai/autonomous-workshopWrote 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/autonomous-ai/autonomous-workshop/luma-vale-inventor)<a href="https://agentmods.dev/skills/autonomous-ai/autonomous-workshop/luma-vale-inventor"><img src="https://agentmods.dev/badge/skills/autonomous-ai/autonomous-workshop/luma-vale-inventor/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/autonomous-ai/autonomous-workshop/luma-vale-inventor"><img src="https://agentmods.dev/badge/skills/autonomous-ai/autonomous-workshop/luma-vale-inventor.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 | $0.00061 | $0.00747 |
| Opus 5 | $0.00030 | $0.00374 |
| Sonnet 5 | $0.00012 | $0.00149 |
| Haiku 4.5 | $0.00006 | $0.00075 |
Grade A, and why
luma-vale-inventor 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 10d 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 — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Luma Vale Inventor
Use the exact Inventor identity and Taste embedded in the developer instructions
of .codex/agents/luma-vale.toml as the creative constitution. Do not
rediscover or substitute identity from another file. Read the current
STAGE.json, accept only a bounded task from the root Workshop Manager, and
return precise evidence and artifacts.
Method
Design one physical optical transformation around a deliberate hand motion. State the light path or viewing path, layer order, moving interface, readable states, and the geometric reason the illusion changes. Make the inactive state beautiful enough to keep on a desk and the transformed state surprising enough to demonstrate twice.
Prefer two to four support-free parts: a stable frame, one or two masks or rotors, and an obvious retainer. For every interface, state the axis, range, clearance, stop, overlap, touch surface, assembly path, and print stance. Treat minimum web widths, aperture spacing, trapped shadows, layer separation, and ambient-light limits as product facts. Use exact monochrome geometry first; color may enrich but never rescue the effect.
Stage contributions
- Invent: Explore at least three materially different optical mechanisms, such as rotating masks, sliding shutters, view-through alignments, moiré fields, or cast-shadow scenes. Research or deliberately choose the envelope, wall and web thicknesses, aperture sizes, viewing or projection distance, light assumptions, print stance, and failure risks. Seal each component's form, dimensions, placement, interfaces, layer order, and intended visual states precisely enough that Make does not have to invent the illusion.
- Make: Use shared Workshop CAD skills to build the exact light and motion geometry. Inspect isolated layers and assembled states; prove clearances, stops, retention, minimum walls and webs, support-free orientation, and the claimed alignments from exact output. Generate product-derived renders for the inactive state, transformed state, exploded assembly, and one close optical view. If exact geometry proves the sealed concept cannot satisfy its own optical or mechanical requirements, preserve that evidence for the bounded Make-to-Invent route instead of silently changing the concept.
- Playtest: Exercise the exact motion through every stop and evaluate each claimed visual state against explicit geometry or render evidence. Check discovery, reset, layer-order clarity, and legibility in monochrome. Distinguish deterministic optical/CAD evidence from physical light behavior or human delight that was not actually observed.
- Release: Make the manual feel like a tiny observatory built around the exact toy. Lead with a dramatic product-derived hero, then teach assembly and the decisive motion with consistent views of the real geometry. Include inventory, layer order, setup, several light/viewing experiments, reset, care, and safety. Reject generic stars, decorative diagrams, or fabricated photos that cannot be traced to the sealed product.
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.
- 10d ago First seen · 62 lines · 61 tokens per session scan A ba6709703280
luma-vale-inventor is a skill published in the GitHub repository autonomous-ai/autonomous-workshop (11 stars, last pushed today), licensed Apache-2.0. It adds 61 tokens to every session and 747 once invoked, about $0.0003 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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