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 yufengliu15/kotoba --skill kotobagit clone --depth 1 https://github.com/yufengliu15/kotobaWrote 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/yufengliu15/kotoba/kotoba)<a href="https://agentmods.dev/skills/yufengliu15/kotoba/kotoba"><img src="https://agentmods.dev/badge/skills/yufengliu15/kotoba/kotoba/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/yufengliu15/kotoba/kotoba"><img src="https://agentmods.dev/badge/skills/yufengliu15/kotoba/kotoba.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 | $0.00182 | $0.02000 |
| Opus 5 | $0.00091 | $0.01000 |
| Sonnet 5 | $0.00036 | $0.00400 |
| Haiku 4.5 | $0.00018 | $0.00200 |
Grade A, and why
kotoba 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 12d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kotoba — Language-Learning Deck Generator
Turn "make me 100 HSK4 travel words" into a polished, importable .apkg deck. The pipeline: plan → preview → generate → validate → package. Each phase exists for a reason — skipping one is how bad cards reach a user's collection.
Scope
Language learning only: vocabulary cards for any L1 → L2 pair. If the user asks for a non-language deck (history, medicine, code), explain this skill is purpose-built for linguistic data and offer to make cards in plain chat instead. High-stakes accuracy domains (medical, legal, certification) are explicitly out of scope.
Sandboxed output is non-negotiable. Always produce a standalone deck the user imports fresh and reviews. Never offer to merge generated cards into an existing deck — one bad batch mixed into a 5,000-card collection destroys trust permanently.
Phase 1 — Plan (conversation)
Before generating anything, lock the spec with the user. Ask only what you can't infer:
- Language pair — target language (L2) + the user's language (L1). Script variant if relevant (Simplified/Traditional, etc.).
- Scope — card count, level (HSK4, JLPT N3, CEFR B1...), topic focus, ordering (frequency / thematic / custom list).
- Extras — audio (default: yes, word + sentence via edge-tts), images (default: off; CC-licensed lookup by keyword when on).
Then read the matching schema reference — it carries the language-specific generation and validation rules:
- Mandarin Chinese →
references/mandarin.md(deep: pinyin ruby, tone rules, HSK conventions, voices) - Japanese →
references/japanese.md(deep: furigana chunking, on/kun reading verification, rendaku, jukujikun, JLPT conventions, counters, voices) - Everything else →
references/generic.md(universal schema baseline)
No deep reference for the target language? Write one before generating — automatically, without asking. generic.md is a fallback, not a substitute. Every language has reading and naturalness traps that a generic schema can't anticipate (the way Mandarin has dual-pronunciation characters and Japanese has on/kun and rendaku), and those traps are exactly what produces the embarrassing, well-formed-but-wrong cards this pipeline exists to prevent. So the first time a deck is requested in a language without its own references/<language>.md, treat creating that reference as the opening step of Phase 1, not optional polish:
What ships with it
7 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.
- 12d ago First seen · 90 lines · 182 tokens per session scan A 721dd030bdc0
kotoba is a skill published in the GitHub repository yufengliu15/kotoba (10 stars, last pushed 2mo ago), licensed MIT. It adds 182 tokens to every session and 2,000 once invoked, about $0.0009 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.
Other skills, from other repositories
ljg-word
Deep-dive English word mastery tool. Deconstructs a single English word into core semantics and epiphany. Use when user asks to explain/master a specific English word.
rust-learner
Learn Rust language features and crate updates. Use when user asks about Rust version changelog, what's new in Rust, crate updates, Cargo.toml dependencies, tokio/serde/axum features, or any Rust ecosystem questions.
golang-stay-updated
Golang ecosystem watch list — official sources (go.dev/blog, pkg.go.dev, tour.golang.org, golang-nuts), newsletters (Golang Weekly, Awesome Go Newsletter), communities (r/golang, gophers.slack.com, Go Forum, go.dev/wiki), blogs (Dave Cheney, Ardan Labs, Rob Pike), YouTube channels (Gopher Academy, GopherCon EU/UK)…
course
A Python helper that displays tutorials, group information, source-code links, and other resources for the python-office library in a terminal.
llvm-learning
Comprehensive learning resources and tutorials for LLVM, Clang, and compiler development. Use this skill when helping users learn LLVM internals, find educational resources, or understand compiler concepts.
notebook.create_api_intro
Create a notebook to present the API of a package.