Research Trends

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:

echo "YEAR_NOW=$(date +%Y)"
echo "YEAR_PREV=$(($(date +%Y) - 1))"
echo "REPORT_DATE_EN=$(LC_TIME=C 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.

1. Web search phrases (3–5) mixing English and Japanese for the latest information.

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"

Save the resulting absolute path 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:

python3 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 ${REPORTDATEEN})

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
• Title 2

**Japanese output template (when query is in Japanese):**

📊 "$ARGUMENTS" トレンドレポート (${REPORTDATEJP}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)

• **** — 。
• 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
• Title 2

**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.

## 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.