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 arden-span-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/arden-span-inventor)<a href="https://agentmods.dev/skills/autonomous-ai/autonomous-workshop/arden-span-inventor"><img src="https://agentmods.dev/badge/skills/autonomous-ai/autonomous-workshop/arden-span-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/arden-span-inventor"><img src="https://agentmods.dev/badge/skills/autonomous-ai/autonomous-workshop/arden-span-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 | $0.00038 | $0.00735 |
| Opus 5.5 | $0.00015 | $0.00294 |
| Sonnet 5.5 | $0.00008 | $0.00147 |
| Haiku 4.5 | $0.00004 | $0.00073 |
Grade A, and why
arden-span-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 1mo 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arden Span Inventor
Use the exact identity and Taste embedded in the developer instructions of
.codex/agents/arden-span.toml. Read the current STAGE.json, accept a bounded
task from the root Workshop Manager and return precise artifacts and evidence.
Construction method
Begin with the action and the architectural reward. Name what the person places, what changes after placement, how completion is recognized and why another build is worthwhile. Choose the spatial grammar that supports the Wish: mass and void, packing, routes, level changes or a combination whose rules remain legible.
Define the actual piece inventory, legal orientations, spatial interfaces, bearing faces and access for placement. Respect upright architectural features; do not assume the unconstrained rotations of an abstract puzzle remain possible after adding walls, roofs or terrain. Keep scene detail within each part's legal envelope unless the full state model accounts for it.
For finite puzzles, enumerate legal states with a small deterministic model. Verify representative solutions, impossible states and misleading near-solutions. Count symmetry-equivalent or interchangeable-piece states honestly. Select challenges for different reasoning opportunities; solution count alone does not measure human difficulty. Keep state search a deterministic craft tool, never an agent scheduler or a loop that chooses lifecycle actions.
Translate abstract interfaces into measurable geometry: endpoint position, height, width, direction, clear walking region or bearing patch. Build the most constrained interface before repeating it across a full inventory. Trace those features in the exact CAD, rather than treating matching labels as evidence of contact or access. Check stairs and supported surfaces between endpoints too.
Treat every advertised arrangement as an exact assembly with inventory, placements, build order, contacts, envelope and support assumptions. Use actual views to inspect whether the spatial idea and architectural identity survive scale, color changes and alternate arrangements. Physical friction, handling, durability and enjoyment require corresponding physical evidence.
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.
- 1mo ago First seen · 69 lines · 38 tokens per session scan A eb429460a501
arden-span-inventor is a skill published in the GitHub repository autonomous-ai/autonomous-workshop (21 stars, last pushed today), licensed Apache-2.0. It adds 38 tokens to every session and 735 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-15.
Other skills, from other repositories
pygame-core
Structure a pygame (pygame-ce) game in Python: the init/event/update/draw loop, delta-time movement, Surface/Rect blitting, keyboard/mouse input, and Sprite/Group management with collision. Use when building or debugging a pygame game — when the user mentions pygame, pygame-ce, the game loop, blit, Surface, Rect…
vibeue
Unreal Engine 5 development using the VibeUE Python API. Use when working in Unreal Engine — blueprints, state trees, materials, actors, landscapes, animation, niagara, widgets, sound, foliage, gameplay tags, enhanced input, skeletons, PCG (procedural content generation), and more. VibeUE is an extension of Unreal's…
demo-1-terminal-arcade
Demo skill that builds a colorful, playable terminal Snake game in a single pure-stdlib Python curses file — score HUD, speed ramp, eat/death animations, and a game-over screen.
godot-development
Godot 4 reference patterns: signal-vs-call and physics-body decision tables, resource sharing traps, input handling, a common mistakes catalog, a GDScript to C# rosetta, typed class structure, pooling, autoloads, GUT tests, and collision layer constants. Use when designing, writing, or reviewing Godot 4 code.
glyphs-mcp-development
Use this skill to create, extend, or review reusable workspace-first Glyphs Python scripts and Python plug-ins, including general, reporter, filter, palette, select-tool, and file-format plug-ins, while grounding API and template choices in the bundled Glyphs documentation.
game-ai-patterns
Game AI code patterns: behavior tree node types, FSM transition tables, utility AI consideration curves, GOAP action definitions, Godot NavMesh agent setup, and field-of-view sensing. Use when writing or reviewing NPC decision-making, pathfinding, or perception code.