evo-3d-scan-calc

evo-3d-scan-calc is a skill for Claude Code, Codex from OpenLAIR/OpenSkill. It costs 35 tokens per session (727 once invoked), scanned A, original, Apache-2.0.

A complete calculator for binary STL scan files, a common 3D-model format. It finds the largest connected part, reads its material ID, looks up density, and writes a JSON mass result.

In plain words
What is it for?
Use it to calculate the mass and material ID of the main part in a binary STL scan.
Why use it?
It avoids assembling separate file-parsing, geometry, material-lookup, and report-writing steps for each scanned part.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to calculate the mass and material ID of the main part in a binary STL scan.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/openlair/openskill/evo-3d-scan-calc
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.

Any agent
npx skills add OpenLAIR/OpenSkill --skill evo-3d-scan-calc
Clone the repo
git clone --depth 1 https://github.com/OpenLAIR/OpenSkill

Made for: Claude Code, Codex.

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 evo-3d-scan-calc

README.md
[![agentmods](https://agentmods.dev/badge/skills/openlair/openskill/evo-3d-scan-calc/github.svg)](https://agentmods.dev/skills/openlair/openskill/evo-3d-scan-calc)
Your own site
<a href="https://agentmods.dev/skills/openlair/openskill/evo-3d-scan-calc"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-3d-scan-calc/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.

agentmods 80×15 button for evo-3d-scan-calc

Your own site · 80×15
<a href="https://agentmods.dev/skills/openlair/openskill/evo-3d-scan-calc"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-3d-scan-calc.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 727 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original 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 5.1 $0.00035 $0.00727
Opus 5 $0.00017 $0.00364
Sonnet 5 $0.00007 $0.00145
Haiku 4.5 $0.00003 $0.00073

Measured today against content hash e09d17b0d665, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

evo-3d-scan-calc 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 today.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/utils.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

tasks-evolved/3d-scan-calc/environment/skills/evo-3d-scan-calc/SKILL.md · 72 lines

What it actually says

evo-3d-scan-calc

End-to-end skill for computing the mass of a 3D printed part from a binary STL scan file.

Pipeline Overview

  1. Parse binary STL — Decode 80-byte header, 4-byte triangle count, and 50-byte facet records. Extract vertices and Material ID (uint16 attribute field) per triangle.
  2. Find connected components — Build vertex-based adjacency (triangles sharing any vertex are connected) using vertices quantized to 4 decimal places. BFS to find all components.
  3. Select main part — Compute volume for each component. The largest by volume is the main part.
  4. Extract Material ID — Use the attribute from the first triangle of the largest component.
  5. Lookup density — Reference the material density table: {1: 0.10, 10: 7.85, 25: 2.70, 42: 5.55, 99: 11.34}.
  6. Calculate massmass = volume * density. No unit conversion needed (density table matches mesh coordinate units).
  7. Write report — Save JSON with main_part_mass and material_id.

Critical Details

  • Vertex quantization: Round to exactly 4 decimal places for vertex matching (matches verifier).
  • Adjacency: Vertex-based — two triangles are connected if they share at least one vertex (NOT edge-based).
  • Volume formula: Signed tetrahedron method: V = abs(Σ v1·(v2×v3)) / 6.0. No mm³-to-cm³ conversion.
  • Component selection: By largest volume, not by material ID count.
  • Material ID: From the first triangle of the component (attribute byte count field).

Usage

import sys
sys.path.insert(0, '/app/environment/skills/evo-3d-scan-calc/scripts')
from utils import run_full_pipeline

result = run_full_pipeline()

Or step by step:

import sys
sys.path.insert(0, '/app/environment/skills/evo-3d-scan-calc/scripts')
from utils import (
    parse_binary_stl, find_connected_components,
    compute_signed_volume, lookup_density, write_mass_report
)

# 1. Parse STL
triangles = parse_binary_stl('/root/scan_data.stl')

# 2. Find all connected components (vertex-based adjacency, 4-decimal quantization)
components = find_connected_components(triangles)

# 3. Compute volume for each component, pick largest
best_comp = None
best_vol = -1
for comp in components:
    vol = compute_signed_volume(comp)
    if vol > best_vol:
        best_vol = vol
        best_comp = comp

# 4. Get material ID from first triangle of largest component
material_id = best_comp[0][3]

# 5. Lookup density and compute mass (NO unit conversion)
density = lookup_density(material_id)
mass = best_vol * density

# 6. Write report
write_mass_report('/root/mass_report.json', mass, material_id)
Files

What ships with it

1 file 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.

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. today First seen · 72 lines · 35 tokens per session scan A e09d17b0d665

Subscribe to this mod's changes

evo-3d-scan-calc is a skill published in the GitHub repository OpenLAIR/OpenSkill (88 stars, last pushed yesterday), licensed Apache-2.0. It adds 35 tokens to every session and 727 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 2026-09-11.

Related

Other skills, from other repositories

fba-simulator

Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.

aiming-lab/AutoResearchClaw · 69 tokens

gsmm-validator

Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule formatting. Outputs a structured validation report with errors and warnings.

aiming-lab/AutoResearchClaw · 52 tokens

gsmm-builder

Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.

aiming-lab/AutoResearchClaw · 45 tokens

stat-result-validator

Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.

aiming-lab/AutoResearchClaw · 36 tokens

statistical-theory-analysis

Analyze theoretical properties of statistical methods under the formal formulation: identifiability, bias, variance, consistency, asymptotics, coverage, error bounds, robustness, and limitations.

aiming-lab/AutoResearchClaw · 41 tokens

meta-analysis

Statistical methods for combining results across multiple studies. Use when aggregating cross-study or cross-experiment results.

aiming-lab/AutoResearchClaw · 25 tokens