Search

Search the awesome-japanese-nlp-resources database for: "$ARGUMENTS"

Instructions

Step 0 — Validate input

If $ARGUMENTS is empty or blank, stop immediately and output:

Usage: /awesome-japanese-nlp-resources:search 

Examples:
  /awesome-japanese-nlp-resources:search morphological analysis
  /awesome-japanese-nlp-resources:search BERT
  /awesome-japanese-nlp-resources:search named entity recognition
  /awesome-japanese-nlp-resources:search text classification dataset
  /awesome-japanese-nlp-resources:search sentence embedding

Please pass the keyword(s) you want to search for as the argument.

---

使い方: /awesome-japanese-nlp-resources:search 

クエリ例:
  /awesome-japanese-nlp-resources:search 形態素解析
  /awesome-japanese-nlp-resources:search BERT
  /awesome-japanese-nlp-resources:search 固有表現認識
  /awesome-japanese-nlp-resources:search テキスト分類 データセット
  /awesome-japanese-nlp-resources:search 文埋め込み

検索したいキーワードを引数に指定してください。

Do not proceed to Step 1 if $ARGUMENTS is empty.

Step 1 — Interpret the query

The user's query is: "$ARGUMENTS"

The data descriptions are in English, so always convert the query intent to English keywords before searching.

Keyword rules — read before choosing keywords:

1. Use stems, not full words. Substring match is used, so morpholog catches "morphology", "morphological", "morphological analyzer". Other examples: embed → embedding/embeddings, classif → classification/classifier, translat → translation/translate, generat → generation/generative, segment → segmentation/segmenter, recogni → recognition/recognizer, extract → extraction/extractor, retriev → retrieval/retrieve.
2. Add domain-specific tool names. When the query maps to a known NLP domain, include the well-known tool names present in the database:

| Domain (Japanese query hint) | Stem keywords | Tool names to add | |---|---|---| | 形態素解析 / morphological analysis | morpholog, segment | mecab, janome, sudachi, kytea, kuromoji, jumanpp, nagisa | | 固有表現認識 / NER | named entit, NER, recogni | ginza, spacy, knp | | 係り受け解析 / dependency parsing | depend, parse, syntax | cabocha, knp, ginza, spacy | | 文章分類 / text classification | classif, sentiment, categor | bert, fasttext | | 感情分析 / sentiment analysis | sentiment, emotion, opinion | oseti, wrime | | 埋め込み / word vectors / embeddings | embed, vector, represent | word2vec, fasttext, bert, sbert | | 事前学習モデル / pretrained model | pretrain, language model, bert, gpt | bert, gpt, llama, rinna, elyza, calm, swallow | | テキスト生成 / text generation | generat, language model | gpt, llm, llama, rinna, elyza | | 機械翻訳 / machine translation | translat, machine translation | opus, marian, fairseq | | 音声認識 / speech recognition | speech, recogni, audio, asr | whisper, julius, espnet | | 音声合成 / text-to-speech | speech, synthesis, tts | voicevox, espnet | | 質問応答 / QA | question, answer, qa | bert, t5 | | 要約 / summarization | summari, abstract | bart, t5, pegasus | | 辞書・IME / dictionary | dict, lexicon, ime | mecab, sudachi, mozc | | コーパス・データセット / corpus | corpus, dataset, annot | (rely on stems) | | チュートリアル / learning | tutorial, introduc, learn | (rely on stems) | | OCR / 光学文字認識 | ocr, optical character, recogni | manga-ocr, donut, tesseract | | RAG / 検索拡張生成 | retriev, rag, embed | ruri, glucose, faiss | | ファインチューニング / fine-tuning | fine-tun, finetun, lora, peft | lora, peft, qlora | | ベンチマーク・評価 / benchmark | benchmark, evaluat, jglue | llm-jp-eval, jglue, nejumi |

1. Aim for 4–6 keywords. Fewer miss items; more than 6 inflates low-quality partial matches.
2. If none of the above domains fit, translate the query intent literally to English stems.

Step 2 — Locate the data file

The data file ships with the plugin. Resolve its path via ${CLAUDE_PLUGIN_ROOT} (Claude Code substitutes this inline in skill content), falling back to a scoped search only if the install is unusual:

RESOURCES_PATH="${CLAUDE_PLUGIN_ROOT}/data/resources.json"
[ -f "$RESOURCES_PATH" ] || RESOURCES_PATH="$(find "${HOME}/.claude/plugins" -type f -name resources.json 2>/dev/null | grep "awesome-japanese-nlp-resources/" | head -1)"
echo "RESOURCES_PATH=$RESOURCES_PATH"

Use the resulting absolute RESOURCES_PATH wherever Step 3 opens the data file.

Step 3 — Search and score via Bash

Do NOT use the Read tool — the file exceeds the Read tool's size limit and would consume ~64K tokens unnecessarily. Instead, run the scoring in a single Bash call using Python.

Each item in the JSON array has:

• u: GitHub or Hugging Face URL
• n: repository/model name
• d: description (English for most items; some Japanese-only items have Japanese descriptions)
• c: category (e.g. Python library, HuggingFace Model (Text Generation), Corpus, Tutorial, ...)
• s: subcategory / semantic labels (comma-separated)
• st: GitHub star count (GitHub items only; absent or 0 otherwise)
• ns: normalized star score 0–10 (log-scaled, GitHub items only)
• dl: Hugging Face download count (HF items only; absent or 0 otherwise)
• nd: normalized download score 0–10 (log-scaled, HF items only)
• sc: pre-computed quality score (higher = more popular/active)

Run the following, substituting KEYWORDS with your English keywords list from Step 1:

python3  subcategory
**Popularity:** ⭐ {st} stars  (or  📥 {dl} downloads for HF)
Description text here.

### 2. ...

If no results, suggest alternate keywords and link to: https://github.com/taishi-i/awesome-japanese-nlp-resources

Step 6 — Output use-case selection guide table

After the search results list, append a guide table that helps the user pick the right resource for their specific situation.

Match the section heading and table language to the query language — translate the heading and column headers into the query language (e.g. Japanese query → Japanese heading and headers).

## Use-case Selection Guide

| Use case | Recommended | Popularity | Why |
|---|---|---|---|
| ... | [name](url) | ⭐N or 📥N | short reason |

Rules:

• List 3–6 distinct use cases derived from the top 10 results. Each row should represent a meaningfully different scenario (e.g., "fine-tune an LLM" vs "evaluate an LLM"), not just a restatement of the search query.
• For each row, select the single best resource from the top 10 results.
• Popularity column: use ⭐{st} for GitHub stars, 📥{dl} for HuggingFace downloads. If both are 0, omit.
• Why: write a 10–15 word reason in the query language explaining why this resource is the best fit for that use case. Do not copy the description verbatim. Focus on the practical benefit.
• If two use cases would map to the same resource, merge them into one row or drop the weaker one.
• If there are fewer than 3 meaningfully distinct use cases in the results, output as many rows as make sense (minimum 1).

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: taishi-i
• Source: taishi-i/awesome-japanese-nlp-resources
• License: CC0-1.0
• Homepage: https://taishi-i.github.io/awesome-japanese-nlp-resources/

Install and usage instructions live in the source repository linked above.