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 smileynet/teach-me --skill jargongit clone --depth 1 https://github.com/smileynet/teach-meWrote 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/smileynet/teach-me/jargon)<a href="https://agentmods.dev/skills/smileynet/teach-me/jargon"><img src="https://agentmods.dev/badge/skills/smileynet/teach-me/jargon.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 5.1 | $0.00041 | $0.01181 |
| Opus 5 | $0.00020 | $0.00590 |
| Sonnet 5 | $0.00008 | $0.00236 |
| Haiku 4.5 | $0.00004 | $0.00118 |
Grade A, and why
jargon 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 8d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jargon
Review a written lesson and annotate domain-specific terms with glossary tooltips so newcomers can look up jargon without leaving the page.
Hard Rules
- Never annotate terms inside SVG elements. HTML
<span>is invalid inside SVG<text>— browsers eject content, creating orphaned text nodes. Theis_inside_svg()check injargon-annotate.pyenforces this mechanically.
When to run
After a lesson is written and content is finalized. This is a post-processing pass — don't annotate while writing (it interrupts flow).
Input
A lesson HTML file. If no path is given, operate on the most recent lesson in lessons/.
Concept list (optional): If .scratch/concepts/{slug}.json exists (produced by concept_hints.py during generate-topic), read it and use concepts[].term as the candidate list. These are statistically important terms from the source material, ranked by foundational-ness. Still apply all three gates; the list provides candidates, not decisions.
If no concept hints file exists but source-chunks/{domain}.json does, run python tools/concept_hints.py source-chunks/{domain}.json --topic {slug} --domain {domain} to generate it.
Process
1. Read the lesson
Read the full HTML content. Identify the domain being taught (from the lesson title, content, and MISSION.md if available).
2. Extract candidate terms
Find words and phrases that are domain-specific jargon. Look for:
- Terms with a specific meaning in this domain
- Acronyms used without expansion
- Familiar words used with an unfamiliar domain-specific meaning
- Compound terms unique to this domain (e.g., "manifest list", "partition spec")
3. Filter ruthlessly
Apply these three gates. ALL must pass for a term to be annotated:
Gate 1: Is this term specific to the domain being taught? Skip general computing terms (API, JSON, server, database), general cloud terms (S3, region), product names (Athena, Spark), and basic vocabulary the audience already has.
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.
- 8d ago First seen · 110 lines · 41 tokens per session scan A 59a2de100477
jargon is a skill published in the GitHub repository smileynet/teach-me (3 stars, last pushed today), licensed MIT. It adds 41 tokens to every session and 1,181 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 2026-08-31.
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