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 agentmods add skills/seandavi/scriptorium/terminology-normalizationnpx skills add seandavi/scriptorium --skill terminology-normalizationgit clone --depth 1 https://github.com/seandavi/scriptoriumWrote 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/seandavi/scriptorium/terminology-normalization)<a href="https://agentmods.dev/skills/seandavi/scriptorium/terminology-normalization"><img src="https://agentmods.dev/badge/skills/seandavi/scriptorium/terminology-normalization.svg" alt="Measured on agentmods" 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 | $0.00050 | $0.03220 |
| Opus 5 | $0.00025 | $0.01610 |
| Sonnet 5 | $0.00010 | $0.00644 |
| Haiku 4.5 | $0.00005 | $0.00322 |
Grade A, and why
terminology-normalization 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Terminology normalization
You are running scriptorium's terminology-normalization skill. Your
job is to detect terminology drift in a manuscript and enforce the
terminology.preferred / terminology.forbidden / terminology.synonyms
lists declared in MANUSCRIPT_STATE.yaml. This is a normalization
skill: you may suggest concrete one-pass edits, but you must not
apply them without the author's explicit consent.
Critical constraints — read before doing anything else
- Never modify the manuscript without explicit author consent. The default output is a markdown report that surfaces drift, names the suggested normalization, and lets the author apply it (or invoke a follow-up edit pass). Auto-applying terminology rewrites is the exact failure mode this skill exists to avoid — see DESIGN.md's conservative-edit posture.
- Read the bibliography first. Author names, paper titles, and
other bibliographic metadata frequently contain tokens that look
like forbidden terms or like variants of preferred terms but are
not manuscript prose. Read every file in
MANUSCRIPT_STATE.yaml#bibliography.pathsbefore scanning the manuscript so tokens appearing in author / title / journal / container metadata are excluded from flagging. This is a load-bearing failure mode in the issue spec: missing this step produces false positives that erode trust in the entire skill. - Ignore inflection by default. "cell" vs. "cells", "gene" vs. "genes", "method" vs. "methods" — these are not drift; they are English morphology. Flag stem-level variation only (e.g. "cohort" vs. "cohorts" is not drift; "cohort" vs. "subjects" is, given preferred terms are declared).
- Respect quoted contexts and term-as-subject contexts. A
forbidden term inside
"quotes"or in a passage critiquing or defining the term itself is not an enforcement target. A sentence like we deliberately avoid the term "subjects" is the author doing exactly what the skill would want; do not flag it as a violation. - Surface, don't decide. Undeclared near-variants — clusters of
tokens the author has not yet expressed a preference about —
are surfaced as a question: should one of these be promoted to
terminology.preferred, or are they intentionally distinct? The skill does not pick a winner.
What ships with it
3 files 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.
- 3d ago First seen · 315 lines · 50 tokens per session scan A 5e7f9ce1496c
terminology-normalization is a skill published in the GitHub repository seandavi/scriptorium (3 stars, last pushed 1mo ago), licensed MIT. It adds 50 tokens to every session and 3,220 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.
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