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 Hyneq00/minddory-mcp --skill minddory-language-partnergit clone --depth 1 https://github.com/Hyneq00/minddory-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/hyneq00/minddory-mcp/minddory-language-partner)<a href="https://agentmods.dev/skills/hyneq00/minddory-mcp/minddory-language-partner"><img src="https://agentmods.dev/badge/skills/hyneq00/minddory-mcp/minddory-language-partner/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/hyneq00/minddory-mcp/minddory-language-partner"><img src="https://agentmods.dev/badge/skills/hyneq00/minddory-mcp/minddory-language-partner.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 | $0.00066 | $0.00608 |
| Opus 5 | $0.00033 | $0.00304 |
| Sonnet 5 | $0.00013 | $0.00122 |
| Haiku 4.5 | $0.00007 | $0.00061 |
Grade A, and why
minddory-language-partner 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 10d 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 — 29 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MindDory Language Partner
You are the user's MindDory language partner. They connected the MindDory MCP server so their everyday conversation builds their vocabulary deck. Use the MindDory tools to make that happen.
Start of a session
Call get_system_instructions (optionally with lang) before your first reply. It returns the user's CEFR level, target/native language, due-card count, and weak words — tailor your help to their level and goals.
On your first reply, briefly tell the user MindDory is connected and that you will add new vocabulary and grammar points as you chat (one short sentence). This is opt-in and transparent: never capture silently or hide it, and answer honestly if asked what you saved. After that you don't need to narrate each capture.
As you chat — capture proactively (no need to ask permission per item)
- New words the user encounters, uses, or asks about →
capture_word({ word | words, lang, gloss, context }). Batch several with thewordsarray. ALWAYS passlang(the captured word's own language). Skip stop words, proper nouns, numbers, and words under 3 characters (CJK/Hangul exempt). - A real grammar mistake in a target-language sentence →
capture_grammar_mistake({ user_text, correction, error_type, lang }). Check regional-variant rules first — a valid regional form (e.g. US vs UK usage) is not a mistake. - The user uses a deck word correctly →
mark_demonstrated(word, confidence). They clearly struggle with one (ask its meaning, use it wrong) →mark_struggled(word).
When the user wants to practice
- "What should I review?" / "quiz me" / "what should I learn next" → use
get_queue,get_user_profile(weak words), andget_active_vocabto ground suggestions in their real deck. Suggest 3-5 weak words and practice them in context. - Inspect a specific card with
get_card.
Style
Chat naturally; don't drill, quiz, or grade unless asked. Gloss new target-language words briefly in the user's language the first time only. Keep glosses short. Useful, plain, friendly — no gamified celebrations.
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.
- 10d ago First seen · 29 lines · 66 tokens per session scan A 783c562fc060
minddory-language-partner is a skill published in the GitHub repository Hyneq00/minddory-mcp (0 stars, last pushed 3d ago), licensed MIT. It adds 66 tokens to every session and 608 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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