fairy_tale: Skill for Claude Code

.agents/skills/japanese-wordplay-humor-detection/SKILL.md

japanese-wordplay-humor-detection is a skill for Claude Code, Codex from bonginkan/fairy_tale. It costs 150 tokens per session (2,981 once invoked), scanned A, original, Apache-2.0.

A Japanese-language text tool that finds and labels hidden word patterns and forms of humour, such as acrostics, puns, palindromes, parody and joke structures.

In plain words
What is it for?
Use it to inspect Japanese posts or articles for hidden messages, puns, word games and humour techniques, then explain how they work.
Why use it?
Japanese wordplay can be easy to miss or mistake for coincidence. It checks the exact characters behind a possible pattern and labels the result by confidence.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is bonginkan/fairy_tale's own configuration. It tells Claude Code and Codex how to work on fairy_tale itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything fairy_tale configures →

Reuse

Borrowing it

Nothing to install: this file belongs to bonginkan/fairy_tale. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/bonginkan/fairy_tale/main/.agents/skills/japanese-wordplay-humor-detection/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/bonginkan/fairy_tale

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for japanese-wordplay-humor-detection

README.md
[![agentmods](https://agentmods.dev/badge/skills/bonginkan/fairy_tale/japanese-wordplay-humor-detection/github.svg)](https://agentmods.dev/skills/bonginkan/fairy_tale/japanese-wordplay-humor-detection)
Your own site
<a href="https://agentmods.dev/skills/bonginkan/fairy_tale/japanese-wordplay-humor-detection"><img src="https://agentmods.dev/badge/skills/bonginkan/fairy_tale/japanese-wordplay-humor-detection/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.

agentmods 80×15 button for japanese-wordplay-humor-detection

Your own site · 80×15
<a href="https://agentmods.dev/skills/bonginkan/fairy_tale/japanese-wordplay-humor-detection"><img src="https://agentmods.dev/badge/skills/bonginkan/fairy_tale/japanese-wordplay-humor-detection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 150 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,981 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00150 $0.02981
Opus 5 $0.00075 $0.01491
Sonnet 5 $0.00030 $0.00596
Haiku 4.5 $0.00015 $0.00298

Measured 12d ago against content hash 5b96092a8674, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

japanese-wordplay-humor-detection 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.

.agents/skills/japanese-wordplay-humor-detection/SKILL.md · 135 lines

How it starts

The opening of the file, as written. The whole thing — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.

日本語 言葉遊び・ユーモア検出スキル

文章に仕込まれた言葉遊びユーモア技法を検出・分類する。隠された縦読みのような「仕掛け」から、ダジャレ・回文のような語の遊び、ボケ/ツッコミ・天丼のような笑いの構造まで、日本語ユーモアを横断的に拾う。

発動トリガー:

  • 「この文に仕掛け(縦読み等)ある?」「縦読み/斜め読みして」
  • 「ダジャレ/言葉遊びを拾って」「回文になってる?」
  • 「この記事/投稿のユーモアを分析・分類して」「何系の笑い?」
  • メッセージが妙に整っている・行頭が不自然・語呂が良すぎる、と感じたとき

最重要原則 — 誤検出を出さない(grounding)

言葉遊び検出は偽陽性が出やすい。特に縦読み・斜め読み・アナグラムは、ランダムな文字列が偶然それらしく見えることがある。だから:

  1. 抽出した読みは必ず verbatim で示す。 「各行頭:マ・ほ・う・つ・か・い=魔法使い」のように、根拠の文字を1つずつ並べて初めて「検出」と言える。並べられないなら検出ではない。
  2. 抽出結果が実在の語・句として有意かを確認する。 意味の通らない文字列は仕掛けではなくノイズ。短い偶然一致(2〜3文字)は原則採用しない。
  3. 文脈的な意図の裏付けを見る。 仕込みは多くの場合テーマと響き合う(批判文に「魔法使い」を仕込む等)。意味+文脈が揃って確度が上がる。
  4. 断定の格付けを付ける:確実(読みが明確に成立し意味も通る)/ 可能性(成立するが偶然の余地)/ 否定(成立しない)。怪しいだけで「検出した」と言わない。

検出対象一覧

A. 仕込み系(隠しメッセージ)

技法 定義 検出の起点
縦読み 各行・各文・各箇条書きの先頭文字を縦に連結すると語句になる 行頭文字の連結
斜め読み n 行目の n 文字目を辿る(対角)/ 一定オフセットの斜め 対角・等間隔抽出
折句(おりく) 和歌・短歌などで各句の頭字が語を成す古典技法(例: かきつばた) 句頭の連結
尻読み / 末尾読み 各行の末尾文字を連結 行末文字の連結
飛ばし読み 一定間隔(偶数文字目等)を拾うと別の文が出る 等間隔抽出

B. 語の遊び系

技法 定義 検出の起点
ダジャレ / 地口 同音・近似音を別義で重ねる(「布団が吹っ飛んだ」) 仮名読みの反復・近似
同音異義 / 「これじゃない」系 同じ読みで意味をすり替える掛詞的遊び 読み一致+語義差
回文 仮名読みが前後対称(「たけやぶやけた」) 仮名正規化→対称判定
アナグラム 文字の並べ替えで別語になる 文字多重集合の一致
もじり / 捩り 既知の諺・名言・作品名・歌詞を少し変えて笑いにする 既知句との編集距離
替え歌 既知曲の譜割りに別歌詞を載せる(多くはパロディ) 音数・リズムの一致+元曲想起
枕詞・縁語 古典的な定型修飾・連想語の使用 定型辞書照合

C. 笑いの構造系

  • ボケ/ツッコミ:逸脱(ボケ)と訂正(ツッコミ)の対。漫才・コントの基本骨格。
  • 天丼:同じネタ・フレーズを間を置いて再登場させる反復ギャグ(callback)。
  • 緊張と緩和(桂枝雀):張り詰めた前提を一気に緩めて笑いにする。
  • 不条理 / ナンセンス:論理の破綻そのものを笑いにする。
  • 誇張 / 過小:スケールのズレ。
  • 自虐 / 自己言及:自分(や記事自体)をネタにする。メタ。
  • シニカル / 皮肉 / アイロニー:表と裏の落差。
  • あるある / 共感:日常の機微への共感。
  • パロディ:既存作品・様式の模倣と転倒(例: Wikipedia パロディ)。
  • 下ネタ / 不謹慎 / おバカ / 脱力:系統別の笑い(後述の分類規定参照)。

詳細な検出アルゴリズム・近似音の扱い・偽陽性ガードは references/detection-techniques.md を参照。


検出ワークフロー

入力テキスト
  ↓
Step 1: 正規化      行/文/箇条書きに分割。仮名読みを併記(漢字→読み)。記号・空白を保持した版も残す。
  ↓
Step 2: 仕込み走査   行頭・行末・対角・等間隔を機械的に抽出 → 候補文字列を得る。
  ↓
Step 3: 語の遊び走査  仮名読み列で反復/近似/対称/並べ替え/既知句近接を探す。
  ↓
Step 4: 構造走査     ボケ-ツッコミ対・反復(天丼)・落差・パロディ枠組みを見る。
  ↓
Step 5: 検証(必須)   候補ごとに「読みが実在し有意か」「文脈と響くか」を確認。verbatim で根拠を並べる。
  ↓
Step 6: 分類と確度   技法名+系統+確度(確実/可能性/否定)を付け、根拠とともに出力。

Read the full file on GitHub · 135 lines

Files

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.

Changes

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.

  1. 12d ago First seen · 135 lines · 150 tokens per session scan A 5b96092a8674

Subscribe to this mod's changes

japanese-wordplay-humor-detection is a skill published in the GitHub repository bonginkan/fairy_tale (18 stars, last pushed 4d ago), licensed Apache-2.0. It adds 150 tokens to every session and 2,981 once invoked, about $0.0007 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 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens