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 lingochunk/mcp --skill lingochunk-annotategit 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-annotate)<a href="https://agentmods.dev/skills/lingochunk/mcp/lingochunk-annotate"><img src="https://agentmods.dev/badge/skills/lingochunk/mcp/lingochunk-annotate/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/lingochunk/mcp/lingochunk-annotate"><img src="https://agentmods.dev/badge/skills/lingochunk/mcp/lingochunk-annotate.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 | $0.00107 | $0.02088 |
| Opus 5.5 | $0.00043 | $0.00835 |
| Sonnet 5.5 | $0.00021 | $0.00418 |
| Haiku 4.5 | $0.00011 | $0.00209 |
Grade A, and why
lingochunk-annotate 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.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LingoChunk expression annotator
Read one of the creator's own episodes and mark the expressions a learner genuinely cannot decode word by word, attaching a short markdown note to each. LingoChunk keeps the transcript, timings and word analysis; the only work here is judgement (which spans are worth a note) and writing (a tight explanation for the creator's students). The app renders every note natively: the owner sees an iris tint on the span plus the note in a bottom sheet, and followers of the collection get the note as a forward-only card. No new audio, no UI to build.
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
- "Find the useful expressions in this episode and annotate them."
- "Highlight the idioms and phrasal verbs in episode X."
- "Add creator notes to my lesson so my students get the phrasing, not just the words."
- "Go through this recording and explain the fixed phrases."
Who and what this is for
- Owner + creator tier only. Every annotation endpoint is owner-scoped; an
episode the user does not own is a 404. Creating notes needs the
annotations:writescope (edit-transcripts feature); reading needscontent:read. The user's data never goes to any other service. - Write for the students, not the creator. These notes become follower cards and the owner's own study sheet. Pitch them as if teaching a learner who is a level below the episode's audience, not as notes-to-self.
What counts as a useful expression (annotate these)
Annotate a span only when a learner who knows the individual words would still miss the meaning:
- Idioms and figurative phrases ("den Ball flach halten", "hit the nail on the head").
- Phrasal verbs whose meaning is not the sum of the parts ("give up", "auf jemanden zugehen").
- Strong collocations and fixed phrases the learner should learn as a unit ("make a decision" not "do a decision"; "eine Entscheidung treffen").
- Discourse markers and fillers doing real conversational work ("doch", "na ja", "I mean", "you know") - the ones that steer the turn, not literal content.
- Culture-bound references a learner cannot look up word by word (a TV show, an institution, a local custom named in passing).
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 · 164 lines · 107 tokens per session scan A 4c50ada93b9a
lingochunk-annotate is a skill published in the GitHub repository lingochunk/mcp (0 stars, last pushed 2mo ago), licensed MIT. It adds 107 tokens to every session and 2,088 once invoked, about $0.0004 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 2026-08-31.
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