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-text-normalizegit 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-text-normalize)<a href="https://agentmods.dev/skills/openlair/openskill/evo-text-normalize"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-text-normalize/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-text-normalize"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-text-normalize.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 | $0.00054 | $0.00254 |
| Opus 5 | $0.00027 | $0.00127 |
| Sonnet 5 | $0.00011 | $0.00051 |
| Haiku 4.5 | $0.00005 | $0.00025 |
Grade A, and why
evo-text-normalize 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 yesterday.
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-text-normalize
Text preprocessing for noisy manufacturing defect logs with mixed Chinese-English content.
Key Functions
normalize_width(text)- NFKC normalization + Chinese punctuation mappingpreprocess_text(text)- Full pipeline: width norm, lowercase, whitespace, abbreviationsexpand_abbreviations(text)- Expand common manufacturing abbreviationssplit_mixed_text(text)- Split mixed Chinese/English into contiguous blockssplit_compound_reasons(raw_text)- Split compound reasons by delimiters, preserving spans
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-text-normalize/scripts')
from utils import preprocess_text, split_compound_reasons, normalize_width
clean = preprocess_text("ＰＣＢ Ｆａｉｌ")
segments = split_compound_reasons("scratch on panel, button stuck; 屏幕坏了")
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.
- yesterday First seen · 28 lines · 54 tokens per session scan A 4d453ff73f89
evo-text-normalize is a skill published in the GitHub repository OpenLAIR/OpenSkill (90 stars, last pushed 2d ago), licensed Apache-2.0. It adds 54 tokens to every session and 254 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 2026-09-11.
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