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 <query>

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 <query>

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. When the query contains Japanese text, also keep 2–4 raw Japanese terms/phrases lifted directly from the query (not translated) as a separate ja_keywords list. Aliases and some descriptions (al, d_ja — see Step 3) are Japanese-only, so a literal Japanese substring catches entries an English-only translation would miss entirely — nicknames like ボイボ (VOICEVOX), めかぶ (mecab), or a Japanese technical term that never got glossed into the English description. Leave ja_keywords empty for English queries.
2. Aim for 4–6 keywords. Fewer miss items; more than 6 inflates low-quality partial matches.
3. 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: English description
• d_ja: Japanese description (GitHub-origin items only; match your ja_keywords against this)
• al: curated alternate names / kana nicknames, e.g. ["VOICEVOX", "ボイスボックス", "ボイボ"] (array of strings, only ~40 items have this — treat a hit here as strong as a name match)
• c: category (e.g. Python library, HuggingFace Model (Text Generation), Corpus, Tutorial, ...)
• s: subcategory / semantic labels (array of strings)
• 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)
• status: "ok" or "not_found" — items whose repo 404s (~8 of ~1200) are filtered out below; never recommend one

Run the following, substituting KEYWORDS with your English keywords and JA_KEYWORDS with your raw Japanese terms, both from Step 1 (ja_keywords may be []):

python3 << 'EOF'
import json

with open("RESOURCES_PATH") as f:    # absolute path from Step 2
    data = json.load(f)

keywords = ["keyword1", "keyword2", "keyword3"]  # English stems, from Step 1
ja_keywords = []  # raw Japanese terms from Step 1 -- [] for English queries

results = []
for item in data:
    if item.get("status") == "not_found":
        continue  # dead repo -- never recommend it

    n = item.get("n", "").lower()
    d = item.get("d", "").lower()
    d_ja = item.get("d_ja") or ""
    s = " ".join(item.get("s") or []).lower()
    c = item.get("c", "").lower()
    al = " ".join(item.get("al") or []).lower()

    text_score = 0
    for kw in keywords:
        kw = kw.lower()
        if n == kw:       text_score += 20
        elif kw in n:     text_score += 10
        if kw in d:       text_score += 5
        if kw in s:       text_score += 3
        if kw in c:       text_score += 2
        if kw in al:      text_score += 10  # alias hit is name-equivalent

    for kw in ja_keywords:
        if kw in n:       text_score += 10
        if kw in d_ja:    text_score += 5
        if kw in al:      text_score += 10

    if text_score < 8:
        continue

    ns = item.get("ns") or 0
    nd = item.get("nd") or 0
    sc = item.get("sc") or 0
    pop = (ns if ns else nd) * 2.5
    qual = min(5, sc * 5 / 21)
    combined = text_score + pop + qual

    results.append((combined, text_score, item))

results.sort(key=lambda x: -x[0])
seen = {item['n'] for _, _, item in results}

# Supplemental pass: surface high-popularity items from matching categories
# that may have been missed because their descriptions are in Japanese.
# Keys are stems to match against user keywords; values are category prefixes
# (prefix match covers "HuggingFace Model (Text Generation)" etc.).
CATEGORY_KEYWORDS = {
    "tutorial": "Tutorial", "introduc": "Tutorial", "learn": "Tutorial",
    "morpholog": "Python library", "segment": "Python library",
    "mecab": "Python library", "janome": "Python library", "sudachi": "Python library",
    "spacy": "Python library", "ginza": "Python library",
    "corpus": "Corpus", "dataset": "Corpus",
    "bert": "HuggingFace Model", "gpt": "HuggingFace Model",
    "llm": "HuggingFace Model", "llama": "HuggingFace Model",
    "pretrain": "HuggingFace Model", "embed": "HuggingFace Model",
    "model": "Pretrained model",
}
supplement_cats = set()
for kw in keywords:
    for ck, cat in CATEGORY_KEYWORDS.items():
        if ck in kw.lower():
            supplement_cats.add(cat)

if supplement_cats:
    def cat_match(c):
        return any(c == cat or c.startswith(cat + " ") for cat in supplement_cats)
    extras = [
        item for item in data
        if cat_match(item.get("c", ""))
        and (item.get("st", 0) or item.get("dl", 0))
        and item["n"] not in seen
        and item.get("status") != "not_found"
    ]
    extras.sort(key=lambda x: -max(x.get("ns") or 0, x.get("nd") or 0))
    for item in extras[:5]:
        ns = item.get("ns") or 0
        nd = item.get("nd") or 0
        sc = item.get("sc") or 0
        # base 8 = category-match credit (same as the text_score threshold)
        combined = 8 + max(ns, nd) * 2.5 + min(5, sc * 5 / 21)
        results.append((combined, 0, item))
        seen.add(item["n"])

results.sort(key=lambda x: -x[0])
for combined, text_score, item in results[:20]:
    st = item.get("st", 0) or 0
    dl = item.get("dl", 0) or 0
    flag = " [supplemental]" if text_score == 0 else ""
    print(f"score={combined:.1f} text={text_score} st={st} dl={dl}{flag}")
    print(f"  n={item['n']}")
    print(f"  u={item['u']}")
    print(f"  c={item['c']}")
    print(f"  s={item.get('s','')}")
    if item.get('al'):
        print(f"  al={item['al']}")
    print(f"  d={item.get('d','')[:120]}")
    if item.get('d_ja'):
        print(f"  d_ja={item['d_ja'][:120]}")
    print()
EOF

This returns up to 20 candidates. Items marked [supplemental] were added by the category-based pass to recover high-star resources whose descriptions are in Japanese. In Step 4, evaluate supplemental items on semantic fit before including them in the final list.

Step 4 — Re-rank with your judgment

You now have up to 20 candidates. Apply your semantic judgment to produce the final ordered list of up to 10 results.

Re-rank by evaluating each candidate on:

1. Semantic centrality — how directly does this resource address the query's core intent? A BERT model is more central to "BERT fine-tuning" than a generic transformer library.
2. Popularity as a proxy for quality — high stars/downloads generally signal battle-tested, well-documented tools. Prefer them when candidates are otherwise equivalent.
3. Category fit — match the resource type to the implied need:

• "how to learn / 勉強" → prefer Tutorial, Research summary
• "I need a model" → prefer Pretrained model, HuggingFace Model
• "find a dataset / コーパス" → prefer Corpus, HuggingFace Dataset
• "build an app / ライブラリ" → prefer Python library, language-specific libs

1. Specificity — a resource specialized for the exact task beats a general one.
2. Recency signal — when sc is significantly higher among otherwise-similar items, it usually reflects more recent activity; prefer those.

Do not mechanically follow the combined score from Step 3 — use it as a starting point, then move items up or down based on the criteria above.

Step 5 — Format the output

Language detection rule (apply before writing any output):

• $ARGUMENTS contains Japanese characters (hiragana / katakana / kanji) → Japanese
• Otherwise → English (default)

Apply the detected language to all headings and prose.

Present the final re-ranked results:

## Search results for "$ARGUMENTS"

*(Searched for: keyword1, keyword2, ...)*

Found N result(s).

### 1. [repository-name](url)
**Category:** category > 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).