# Search > Search all Japanese NLP resources (libraries, models, datasets, tutorials, dictionaries, Hugging Face). Accepts keywords or natural language questions in any language. - **Type:** Skill - **Install:** `agentstack add skill-taishi-i-awesome-japanese-nlp-resources-search` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [taishi-i](https://agentstack.voostack.com/s/taishi-i) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** CC0-1.0 - **Upstream author:** [taishi-i](https://github.com/taishi-i) - **Source:** https://github.com/taishi-i/awesome-japanese-nlp-resources/tree/main/plugins/awesome-japanese-nlp-resources/skills/search - **Website:** https://taishi-i.github.io/awesome-japanese-nlp-resources/ ## Install ```sh agentstack add skill-taishi-i-awesome-japanese-nlp-resources-search ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About 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` | 3. **Aim for 4–6 keywords.** Fewer miss items; more than 6 inflates low-quality partial matches. 4. **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 `${CLAUDE_PLUGIN_ROOT}` (Claude Code substitutes this inline in skill content), falling back to a scoped search only if the install is unusual: ```bash RESOURCES_PATH="${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: ```python 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](https://github.com/taishi-i) - **Source:** [taishi-i/awesome-japanese-nlp-resources](https://github.com/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. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-taishi-i-awesome-japanese-nlp-resources-search - Seller: https://agentstack.voostack.com/s/taishi-i - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.