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-stl-binary-parsergit 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-stl-binary-parser)<a href="https://agentmods.dev/skills/openlair/openskill/evo-stl-binary-parser"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-stl-binary-parser/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-stl-binary-parser"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-stl-binary-parser.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 | $0.00049 | $0.00360 |
| Opus 5.5 | $0.00020 | $0.00144 |
| Sonnet 5.5 | $0.00010 | $0.00072 |
| Haiku 4.5 | $0.00005 | $0.00036 |
Grade A, and why
evo-stl-binary-parser 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.
What it actually says
evo-stl-binary-parser
Parses binary STL files and material density tables.
Key Functions
parse_binary_stl(filepath)- Parse binary STL, returns list of (v1, v2, v3, material_id) tuplesparse_material_density_table(filepath)- Parse markdown density table, returns {id: density} dictfilter_facets_by_material_id(triangles, material_id)- Filter triangles by materialdetect_material_id(triangles)- Find dominant material ID (excluding debris ID=1)lookup_density(material_id, density_table=None)- Look up density from table
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-stl-binary-parser/scripts')
from utils import parse_binary_stl, parse_material_density_table, lookup_density
triangles = parse_binary_stl('/root/scan_data.stl')
density_table = parse_material_density_table('/root/material_density_table.md')
Binary STL Layout
- 80 bytes header, 4 bytes uint32 count, N x 50-byte facet records
- Each facet: 12 bytes normal + 36 bytes vertices + 2 bytes attribute (Material ID)
- Format string: '<12fH' (little-endian)
- data[3:6]=v1, data[6:9]=v2, data[9:12]=v3, data[12]=attribute
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.
- 1mo ago First seen · 34 lines · 49 tokens per session scan A 4e2619e2435e
evo-stl-binary-parser is a skill published in the GitHub repository OpenLAIR/OpenSkill (97 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 49 tokens to every session and 360 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 2026-09-11.
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