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 OpenLAIR/OpenSkill --skill evo-mesh-geometry-analysisgit clone --depth 1 https://github.com/OpenLAIR/OpenSkillWrote 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/openlair/openskill/evo-mesh-geometry-analysis)<a href="https://agentmods.dev/skills/openlair/openskill/evo-mesh-geometry-analysis"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-mesh-geometry-analysis/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/openlair/openskill/evo-mesh-geometry-analysis"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-mesh-geometry-analysis.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 | $0.00039 | $0.00337 |
| Opus 5 | $0.00019 | $0.00169 |
| Sonnet 5 | $0.00008 | $0.00067 |
| Haiku 4.5 | $0.00004 | $0.00034 |
Grade A, and why
evo-mesh-geometry-analysis 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.
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.
What it actually says
evo-mesh-geometry-analysis
Mesh topology and volume computation.
Key Functions
get_largest_connected_component(triangles, rounding_decimals=4)- Find largest connected component via vertex-based BFS adjacency with 4-decimal quantizationcompute_mesh_volume(triangles)- Volume via signed tetrahedron method: abs(sum(v1.(v2×v3)))/6.0convert_volume_mm3_to_cm3(volume_mm3)- Divide by 1000calculate_mass(volume_cm3, density_g_cm3)- Mass = Volume × Density
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-mesh-geometry-analysis/scripts')
from utils import get_largest_connected_component, compute_mesh_volume, convert_volume_mm3_to_cm3, calculate_mass
largest = get_largest_connected_component(triangles)
vol_mm3 = compute_mesh_volume(largest)
vol_cm3 = convert_volume_mm3_to_cm3(vol_mm3)
mass = calculate_mass(vol_cm3, density)
Critical Details
- Vertex quantization: 4 decimal places
- Adjacency: vertex-based (share ANY vertex = connected)
- Volume formula: V = abs(Σ v1·(v2×v3)) / 6.0
- Unit conversion: mm³ / 1000 = cm³
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.
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.
- today First seen · 35 lines · 39 tokens per session scan A a79687e7e506
evo-mesh-geometry-analysis is a skill published in the GitHub repository OpenLAIR/OpenSkill (88 stars, last pushed yesterday), licensed Apache-2.0. It adds 39 tokens to every session and 337 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.
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