geometry-solver

geometry-solver is an agent for coding agents from jchildree/Claud-itect-Skill. It costs 53 tokens per session (452 once invoked), scanned A, original, no licence file.

A specialist for mathematical geometry and physics calculations in NgonENGINE’s Ren solver layer.

In plain words
What is it for?
Use it for Newell normals, Mean Value Coordinates, GJK/EPA physics, SubRegion generation, and planarity checks.
Why use it?
It handles the geometry calculations needed to check shapes, coordinates, surfaces, and physics interactions.

Agent

Install

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.

agentmods
npx agentmods add agents/jchildree/claud-itect-skill/geometry-solver
Clone the repo
git clone --depth 1 https://github.com/jchildree/Claud-itect-Skill

Wrote 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.

agentmods badge for geometry-solver

README.md
[![agentmods](https://agentmods.dev/badge/agents/jchildree/claud-itect-skill/geometry-solver.svg)](https://agentmods.dev/agents/jchildree/claud-itect-skill/geometry-solver)
Your own site
<a href="https://agentmods.dev/agents/jchildree/claud-itect-skill/geometry-solver"><img src="https://agentmods.dev/badge/agents/jchildree/claud-itect-skill/geometry-solver.svg" alt="Measured on agentmods" height="20"></a>
Per session 53 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 452 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00053 $0.00452
Opus 5 $0.00026 $0.00226
Sonnet 5 $0.00011 $0.00090
Haiku 4.5 $0.00005 $0.00045

Measured 4d ago against content hash 2421b91449ec, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

geometry-solver 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 4d 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.

agents/geometry-solver.md · 45 lines

The source is not reproduced here

No licence file

A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.

Read it on GitHub

Changes

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.

  1. 4d ago First seen · 45 lines · 53 tokens per session scan A 2421b91449ec

Subscribe to this mod's changes

geometry-solver is an agent published in the GitHub repository jchildree/Claud-itect-Skill (3 stars, last pushed 1mo ago), with no licence file. It adds 53 tokens to every session and 452 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 2026-08-31.

Related

Other agents, from other repositories

godot-performance-profiler

Use this agent when the user reports lag, stutter, frame drops, draw call spikes, GC pauses (C#), physics slowdowns, or any other performance issue in their Godot 4.x project. The agent reads code, asks for profiler captures before guessing, classifies the bottleneck (CPU vs. GPU, draw calls vs. fillrate, physics vs.…

jame581/GodotPrompter · 326 tokens

engine-programmer

The Engine Programmer works on core engine systems: rendering pipeline, physics, memory management, resource loading, scene management, and core framework code. Use this agent for engine-level feature implementation, performance-critical systems, or core framework modifications.

striderZA/OpenCodeGameStudios · 46 tokens

engine-programmer

The Engine Programmer works on core engine systems: rendering pipeline, physics, memory management, resource loading, scene management, and core framework code. Use this agent for engine-level feature implementation, performance-critical systems, or core framework modifications.

TraftG/opencode-game-studio · 46 tokens

editor

Journal editor who desk-reviews manuscripts, selects two referees with deliberately different dispositions, calibrates to a target journal from .claude/references/journal-profiles.md, and synthesizes an editorial decision (FATAL / ADDRESSABLE / TASTE). Used by /review-paper --peer [journal].

pedrohcgs/claude-code-my-workflow · 64 tokens

Geoprocessing Specialist

ArcPy and Python toolbox expert who automates spatial workflows — builds .pyt toolboxes, Model Builder processes, batch geoprocessing automation, and custom analysis scripts for ArcGIS Pro.

SHAdd0WTAka/Zen-Ai-Pentest · 45 tokens

research-scout

Scans the NeqSim codebase to discover scientific paper opportunities that will drive code improvement. Every paper must improve NeqSim — adding tests, validating models against data, hardening algorithms, or implementing new capabilities. Produces ranked, actionable topics that feed into the planner agent.

equinor/neqsim · 61 tokens