--- description: Analyze current trends in Japanese NLP for a topic. Surveys the existing awesome-japanese-nlp-resources dataset and augments it with up-to-the-minute web research to produce a digestible trend report. --- Research Japanese NLP trends for topic: "$ARGUMENTS" by combining the bundled dataset with the latest web information. ## Instructions ### Preamble — Establish the current date Before doing anything else, run this once and remember the values — every subsequent step that mentions a year, month, or report date refers to them: ```bash echo "YEAR_NOW=$(date +%Y)" echo "YEAR_PREV=$(($(date +%Y) - 1))" echo "REPORT_DATE_EN=$(date '+%B %Y')" echo "REPORT_DATE_JP=$(date '+%Y年%-m月')" ``` Substitute these values everywhere this skill writes `${YEAR_NOW}`, `${YEAR_PREV}`, `${REPORT_DATE_EN}`, or `${REPORT_DATE_JP}` below. **Do not hardcode dates** — the skill must always reflect the current month. ### Step 0 — Handle empty input If `$ARGUMENTS` is empty or blank, treat it as a request for a **general overview of current Japanese NLP trends**. Use the following defaults for the rest of the steps: - **Topic label** for output headings: "Japanese NLP Overall Trends" (use "日本語NLP 全体トレンド" only when the user's query was written in Japanese) - **Keywords for Step 1** (local dataset survey): `japanese nlp`, `llm`, `bert`, `embed`, `speech`, `morpholog`, `translat` — These broad keywords give a cross-category snapshot of the most popular resources - **WebSearch queries for Step 5**: cover multiple active sub-fields rather than one topic: - `japanese NLP trends ${YEAR_NOW} overview` - `日本語 NLP 最新動向 ${YEAR_NOW}` - `japanese LLM embedding benchmark ${YEAR_NOW} github` - `日本語 自然言語処理 注目 モデル ${YEAR_NOW}` - `huggingface japanese models trending ${YEAR_NOW}` - **Report title**: `## 📊 Japanese NLP Trend Report (as of ${REPORT_DATE_EN})` instead of `## 📊 Trend Report for "$ARGUMENTS"` (use `## 📊 日本語NLP 全体トレンドレポート (${REPORT_DATE_JP}時点)` only when output language is Japanese) - **Section 1 (Overview)**: write a broad 3–4 sentence overview covering the major active sub-fields (LLMs, embeddings/RAG, speech, morphological analysis, benchmarks) Then continue normally from Step 1 using the above defaults. ### Step 1 — Interpret the topic The user's topic is: "$ARGUMENTS" Generate **two keyword sets**: 1. **English stem keywords (4–6)** for searching the local dataset (descriptions are mostly English). Use stems like `morpholog`, `embed`, `classif`, `translat`, `generat`, `recogni`, etc. Add well-known Japanese-specific tool/model names where applicable: `mecab`, `sudachi`, `ginza`, `bert`, `gpt`, `llama`, `swallow`, `elyza`, `rinna`, `calm`, `ruri`, `whisper`, `voicevox`, `manga-ocr`, `jglue`, `llm-jp-eval`, etc. 2. **Web search phrases (3–5)** mixing English and Japanese for the latest information. ### Step 2 — Locate the data file ```bash find "${HOME}/.claude/plugins" "${PWD}" -type f -name "resources.json" 2>/dev/null | grep "awesome-japanese-nlp" | head -1 ``` If empty: ```bash find "${HOME}/.claude/plugins" -type f -name "resources.json" 2>/dev/null | grep "awesome-japanese-nlp" | head -1 ``` Save as `RESOURCES_PATH`. ### Step 3 — Survey existing resources (inline scoring) **Do NOT use the Read tool** on `resources.json` — it exceeds the read limit. Run this Python block in Bash, substituting `RESOURCES_PATH` and your English stem keywords: ```python python3 << 'EOF' import json from collections import Counter with open("RESOURCES_PATH") as f: data = json.load(f) keywords = ["keyword1", "keyword2", "keyword3"] # English stems from Step 1 results = [] for item in data: n = item.get("n", "").lower() d = item.get("d", "").lower() s = item.get("s", "").lower() c = item.get("c", "").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 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]) # Print top 10 for the trend snapshot print("=== TOP MATCHES ===") for combined, ts, item in results[:10]: st = item.get("st", 0) or 0 dl = item.get("dl", 0) or 0 print(f"score={combined:.1f} | {item.get('c','')} | ⭐{st} 📥{dl}") print(f" n={item['n']}") print(f" u={item['u']}") print(f" d={item.get('d','')[:120]}") print() # Print category distribution of full result set print("=== CATEGORY DISTRIBUTION ===") cats = Counter(item.get("c", "") for _, _, item in results) for cat, count in cats.most_common(8): print(f" {count:3d} {cat}") EOF ``` This gives you: - **Top 10** items currently in the dataset for the topic - **Category distribution** showing which resource types dominate - Popularity stats (stars / downloads) to gauge maturity ### Step 4 — Identify trend angles to investigate Before searching the web, decide what to look for. Based on the survey in Step 3, ask: - What's the dominant **architecture** in the top matches (BERT vs. GPT vs. T5 vs. LLaMA)? - What's the dominant **resource type** (libraries vs. models vs. corpora)? - Are the top items **recent (within the last 2 years)** or **older (>3 years ago)**? - Is there an apparent **gap** (e.g., no recent multimodal models, no public benchmarks)? Use these angles to shape Step 5's queries. ### Step 5 — Web research Use **WebSearch + WebFetch only — do not use the `gh` CLI in this project.** Run **4–6 WebSearch queries**. Always include `${YEAR_NOW}` (and optionally `${YEAR_PREV}`) to bias toward recency. Mix English and Japanese: - `Japanese NLP ${YEAR_NOW}` - `日本語 最新 モデル ${YEAR_NOW}` - `arxiv japanese ${YEAR_PREV} ${YEAR_NOW}` - `huggingface japanese new release` - ` github trending japanese ${YEAR_NOW}` - Optional domain-specific: ` jp benchmark ${YEAR_NOW}`, `日本語 評価` When a specific high-value URL surfaces (e.g. an arXiv abstract, a HuggingFace model card, a blog post announcing a release), use **WebFetch** to extract details: ``` WebFetch url="https://..." prompt="Extract: release date, model/library name, key contribution, GitHub/HuggingFace URL if any, parameter count or dataset size if applicable." ``` Limit WebFetch to **at most 3 calls** to keep latency in check. ### Step 6 — Cross-reference and synthesize Combine signals: 1. **Web items already in the dataset** — confirm the survey's top items remain relevant; note if anything new dethrones them. 2. **Web items NOT in the dataset** — these are candidates the user could also surface via `/awesome-japanese-nlp-resources:find-new-resources "$ARGUMENTS"`; mention this in section 4 of the output. 3. **Directional signals** — write 2–4 specific observations about *where the field is heading*. Examples: - "Parameter-count growth: 1B → 7B → 70B for Japanese LLMs since 2024" - "Shift from encoder-only (BERT) to decoder-only (LLaMA-derived) base models" - "Embedding models specialized for RAG dominate 2025–2026 releases (Ruri-v3, GLuCoSE)" - "Multimodal Japanese models (image+text) are emerging but still rare" 4. **Gaps** — what's missing from the existing list that the web shows exists? ### Step 7 — Format the trend report **Language detection rule (apply before writing any output):** - `$ARGUMENTS` is empty → **English** - `$ARGUMENTS` contains Japanese characters (hiragana / katakana / kanji) → **Japanese** - Otherwise → **English** Apply the detected language to all headings and prose. **English output template (default):** ``` ## 📊 Trend Report for "$ARGUMENTS" (as of ${REPORT_DATE_EN}) ### 1. Overview 2–3 sentence summary. "The current focus is X, the latest trend is Y, and the highlight is Z." ### 2. Current Resources (awesome-japanese-nlp-resources) Top 5 resources: | # | Resource | Category | Popularity | Summary | |---|---|---|---|---| | 1 | [name](url) | category | ⭐N or 📥N | 10–15 word summary | | 2 | ... | ... | ... | ... | Category distribution: Maturity comment: 1–2 sentences. Assessment of "mature / growing / sparse." ### 3. Latest Trends (from the web) - **** — . - **YYYY-MM** — `XYZ-LLM-7B` released, achieves SOTA on Japanese JGLUE, N downloads on HuggingFace. https://huggingface.co/... - ... (3–6 items) ### 4. Key Takeaways - **Direction**: - **Gaps**: important resources not yet in the list - (https://...) — short reason - ... (if any) - **Next step**: run `/awesome-japanese-nlp-resources:find-new-resources "$ARGUMENTS"` to get the full candidate list ### 5. References (See the Sources section below) Sources: - [Title 1](https://...) - [Title 2](https://...) ``` **Japanese output template (when query is in Japanese):** ``` ## 📊 "$ARGUMENTS" トレンドレポート (${REPORT_DATE_JP}時点) ### 1. 概要 2–3 文の要約。「現状の中心は X、最新の動向は Y、注目は Z」のように端的に。 ### 2. 既存リソースの現状 (awesome-japanese-nlp-resources) 代表的なリソース top 5: | # | リソース | カテゴリ | 人気度 | 一言 | |---|---|---|---|---| | 1 | [name](url) | category | ⭐N or 📥N | 10–15 字の要約 | | 2 | ... | ... | ... | ... | カテゴリ分布: 成熟度コメント: 1–2 文。「成熟・拡大期・空白期」の判定。 ### 3. 最新トレンド (Web より) - **<日付 or 年月>** — <発見事項>。<関連 URL> - **YYYY-MM** — `XYZ-LLM-7B` 公開、日本語 JGLUE で SOTA、HuggingFace で N DL。 https://huggingface.co/... - ... (3–6 項目) ### 4. 注目ポイント - **方向性**: - **ギャップ**: 既存リストに未収録の重要リソース - (https://...) — 短い理由 - ... (もしあれば) - **次の一手**: `/awesome-japanese-nlp-resources:find-new-resources "$ARGUMENTS"` を実行すると候補一覧を取得できる ### 5. 参考リンク (下の Sources セクションを参照) Sources: - [Title 1](https://...) - [Title 2](https://...) ``` **Rules:** - Keep total length under ~600 words. The report should be **scannable**, not exhaustive. - Each line in section 3 must include a **date** (or year-month) and a **URL**. No undated rumors. - Section 4 must include at least one *directional signal* and at least one *gap* (or explicitly note "no obvious gaps"). - The `Sources:` block at the very end is **mandatory** — WebSearch results require it. ### Step 8 — Edge cases - **No existing results** (Step 3 returns empty): Skip section 2's table; in section 2 write "既存リストにこのトピックの直接的なリソースは見つかりませんでした。" Then make section 3 + 4 the focus. - **No recent web results**: Note in section 3 that the field is quiet — that's itself a signal. - **Topic is too broad** (e.g. just "NLP"): Suggest in section 4 a narrower sub-topic to re-query.