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/lingochunk/mcp/lingochunk-lessonnpx skills add lingochunk/mcp --skill lingochunk-lessongit clone --depth 1 https://github.com/lingochunk/mcpWrote 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/lingochunk/mcp/lingochunk-lesson)<a href="https://agentmods.dev/skills/lingochunk/mcp/lingochunk-lesson"><img src="https://agentmods.dev/badge/skills/lingochunk/mcp/lingochunk-lesson.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 | $0.00169 | $0.06877 |
| Opus 5 | $0.00084 | $0.03438 |
| Sonnet 5 | $0.00034 | $0.01375 |
| Haiku 4.5 | $0.00017 | $0.00688 |
Grade A, and why
lingochunk-lesson 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 4d 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 — 464 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LingoChunk lesson builder
Build a lesson.v1 document grounded in the learner's real LingoChunk
content. The division of labour is the point: LingoChunk supplies the
materials (sentences with positions and translations, vocabulary with FSRS
maturity) and renders the lesson natively in the app; you do the pedagogy on
the user's own tokens. No server-side LLM spend. The app plays the audio
straight from the original episode (never send or embed audio), resolves
word knowledge live, links every word and sentence back to its Words/Listen
tabs, and mounts an Ask AI tutor on the grammar box and glossary.
This skill uses the lingochunk MCP tools. If they are not available, tell
the user to add the LingoChunk MCP server (see the plugin README) and stop.
When to use
- "Make me a lesson / worksheet / quiz from yesterday's episode."
- "Build a lesson on the subjunctive using examples from my episodes."
- "Create a lesson from the first five minutes of episode X."
- "Quiz me on the words I'm learning in German."
Options to settle first (ask only what the user left open)
- Source: a named episode (
list_library), or a topic/grammar point gathered across episodes (search_examples,get_vocabulary) - in v1 a lesson has ONE source episode, so for cross-episode topics pick the richest single episode. - Time range: "the first five minutes" maps to
get_transcriptfrom_time/to_time; a chapter or scene works too. Default: a coherent 3 to 8 minute slice, not a whole 45-minute episode. - Level: the CEFR level to pitch at. If the user does not say, infer it
from the CEFR mix of their
get_vocabulary(status=known)words and say which level you picked. Level drives:- instruction language: A1/A2 instructions in the learner's language; B1+ in the target language (keep them one short sentence);
- exercise mix: more recognition (MCQ, matching) at low levels, more cued recall and production at high levels;
- which grammar point qualifies (see below), glossary depth, and which archetypes you pick from the menu.
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.
- 4d ago First seen · 464 lines · 169 tokens per session scan A 9f1f4302c8c1
lingochunk-lesson is a skill published in the GitHub repository lingochunk/mcp (0 stars, last pushed 1mo ago), licensed MIT. It adds 169 tokens to every session and 6,877 once invoked, about $0.0008 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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