Find New Resources

Find new Japanese NLP GitHub repositories for topic: "$ARGUMENTS" that are not already in the awesome-japanese-nlp-resources list.

Instructions

Preamble — Establish the current date

Before doing anything else, run this once and remember the values — every step that mentions a year refers to them:

echo "YEAR_NOW=$(date +%Y)"
echo "YEAR_PREV=$(($(date +%Y) - 1))"

Substitute these everywhere this skill writes ${YEAR_NOW} or ${YEAR_PREV} below. Do not hardcode years — the skill must always reflect the current year.

Step 0 — Handle empty input

If $ARGUMENTS is empty or blank, treat it as a general search for the latest Japanese NLP resources. Use the following default settings for the rest of the steps:

• Topic label for output headings: "Latest Japanese NLP Resources" (use "最新の日本語NLPリソース" only when the user's query was written in Japanese)
• Keywords for Step 1: japanese nlp, 日本語 nlp, japanese language processing, japanese machine learning
• WebSearch queries for Step 4: focus on recency — add ${YEAR_PREV} ${YEAR_NOW} to every query, and include:
• japanese NLP new library github ${YEAR_NOW}
• 日本語 NLP 新しい ライブラリ github ${YEAR_NOW}
• awesome japanese nlp ${YEAR_PREV} ${YEAR_NOW} new
• japanese natural language processing tool released ${YEAR_PREV} ${YEAR_NOW}
• Output heading: "New Japanese NLP Resource Candidates" instead of New candidates for "$ARGUMENTS" (use "最近追加された日本語NLPリソース候補" only when the output language is Japanese)

Then continue normally from Step 1 using the above defaults.

Step 1 — Interpret the topic

The user's topic is: "$ARGUMENTS"

Translate the topic intent to English keywords for search. Aim for 3–5 keywords/phrases. Use the same stem + tool-name conventions as the search skill:

| Domain (Japanese hint) | English keywords / search phrases | |---|---| | 形態素解析 / morphological analysis | japanese morphological analyzer, japanese tokenizer, mecab, sudachi, janome | | 固有表現認識 / NER | japanese named entity recognition, japanese NER, ginza | | 係り受け解析 / dependency parsing | japanese dependency parser, cabocha, knp | | 文章分類 / text classification | japanese text classification, japanese sentiment | | 埋め込み / embeddings | japanese sentence embedding, japanese embedding model, ruri, sbert | | 事前学習モデル / pretrained model | japanese pretrained model, japanese LLM, japanese BERT, japanese GPT | | LLM / 大規模言語モデル | japanese llm, japanese language model, llama japanese, swallow, elyza | | テキスト生成 / generation | japanese text generation, japanese chatbot | | 機械翻訳 / translation | japanese machine translation, english japanese translation | | 音声認識 / speech | japanese speech recognition, japanese asr, whisper japanese | | 音声合成 / TTS | japanese text to speech, japanese tts, voicevox | | 質問応答 / QA | japanese question answering, japanese qa dataset | | 要約 / summarization | japanese summarization, japanese abstractive summarization | | 辞書 / dictionary・IME | japanese dictionary, japanese ime, mozc | | コーパス / corpus | japanese corpus, japanese dataset, japanese annotated | | OCR | japanese ocr, manga ocr | | RAG | japanese rag, japanese retrieval, japanese reranker | | ファインチューニング | japanese fine-tuning, japanese lora, japanese instruction tuning | | ベンチマーク / 評価 | japanese benchmark, japanese evaluation, jglue, llm-jp-eval |

If none fits, translate the topic literally to English and add japanese / 日本語 modifiers.

Step 2 — Locate the existing 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 below.

Step 3 — Build the existing-URL set

The plugin's resources.json may lag behind the repo's README.md — some entries exist only in the README. To avoid false "new resource" reports, prefer the pre-built data/existing_urls.txt (emitted by build_data.py) when present and fall back to a live README scan otherwise.

Create a temporary file for the merged URL set (so concurrent runs don't clobber each other):

EXISTING_URLS_FILE=$(mktemp -t awesome_ja_nlp_urls.XXXXXX)

Then run the following Python block (substituting RESOURCES_PATH and EXISTING_URLS_FILE):

python3 = 2:
                urls.add(f"https://github.com/{parts[0]}/{parts[1]}".lower())

    count_json = len(urls)
    try:
        p = os.path.abspath(data_dir)
        readme_files = []
        for _ in range(6):
            p = os.path.dirname(p)
            readme = os.path.join(p, "README.md")
            if os.path.exists(readme):
                readme_files.append(readme)
            if os.path.exists(os.path.join(p, "awesome-japanese-nlp-resources.json")):
                break  # reached repo root
        for readme in readme_files:
            before = len(urls)
            with open(readme) as f:
                content = f.read()
            for url in re.findall(r"https://github\.com/[A-Za-z0-9_.-]+/[A-Za-z0-9_.-]+", content):
                urls.add(url.lower().rstrip("/"))
            if len(urls) > before:
                print(f"Supplemented {len(urls)-before} URLs from {readme}")
    except Exception as e:
        print(f"README.md scan skipped ({e}), using resources.json only")
    source = f"derived ({count_json} from JSON, {len(urls)-count_json} from README walk)"

with open(OUTPUT_PATH, "w") as f:
    f.write("\n".join(sorted(urls)))
print(f"Loaded {len(urls)} existing URLs from {source} → {OUTPUT_PATH}")
EOF

Remember to clean up the temp file when the skill finishes (rm -f "$EXISTING_URLS_FILE").

Step 4 — Discover candidates via WebSearch

Run 4–6 WebSearch queries to find candidate GitHub repos. Mix English and Japanese; vary phrasing to widen coverage. Do not use the gh CLI in this project — rely on WebSearch + WebFetch only.

• japanese site:github.com
• japanese ${YEAR_NOW} site:github.com
• 日本語 github (Japanese-side phrasing)
• awesome japanese
• github (when a topic maps to known tool names like mecab, sudachi, voicevox)
• Optional: japanese language model ${YEAR_PREV} for LLM-flavored topics

From each result, extract every URL matching https://github.com// (ignore deeper paths like /issues, /pull/, /blob/, /tree/). Capture them into a candidate set, lowercased and with trailing slashes stripped.

Step 5 — Filter against the existing dataset

Drop any candidate URL whose lowercased form (or its https://github.com/owner/repo prefix) is in $EXISTING_URLS_FILE (the temp file from Step 3). Deduplicate by owner/repo pair.

After filtering, you should have at most a few dozen unique candidates. If more than 20, prioritize those that appear in multiple WebSearch result sets (signal of relevance) and those with English keyword matches in the search-result title/snippet.

Step 6 — Enrich top candidates via WebFetch

For each surviving candidate (cap at 10–15), call WebFetch on the repo URL to confirm it exists and extract the essentials:

WebFetch url="https://github.com//" prompt="Extract as JSON: name, one-line description, primary language, star count, last-updated date (in YYYY-MM format), whether the repo is archived, whether it is a fork, and whether the README / description mentions Japanese/日本語 NLP. If any field is unavailable, set it to null."

To keep latency manageable, issue up to 5 WebFetch calls in parallel (single message, multiple tool calls). If a candidate's page 404s or redirects unexpectedly, drop it.

From the WebFetch results, drop any candidate where:

• archived: true or fork: true
• The description / README does not mention Japanese / 日本語 / NLP
• stars category is most active in the "$ARGUMENTS" space
• Suggested section in README.md: ``
• Highlight: (⭐ N) — short reason why it stands out
• Follow-up suggestion:

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

"$ARGUMENTS" に関する追加候補

awesome-japanese-nlp-resources に未収録の GitHub リポジトリ N 件 を発見しました。

(検索キーワード: keyword1, keyword2, ...)

Python library

• repo-name - One-line English description. (⭐ 123, 最終更新: YYYY-MM)
• ...

Corpus

• ...

---

追加の検討メモ

• "$ARGUMENTS" 領域では `` のリポジトリが特に活発
• 推奨追加先: README.md の `` セクション
• 注目株: (⭐ N) — 短い推薦理由
• フォローアップ提案:

**Rules for the bullet lines:**
- Format `* [name](url) - description` matches the existing README.md contribution style exactly — paste-ready for a PR
- Keep the description ≤ 100 characters and in **English**. If the repo's own README/description is in Japanese, translate it to English for the bullet line (the awesome list keeps resource descriptions in English)
- For English output, append `(⭐ N, last updated: YYYY-MM)`; for Japanese output, append `(⭐ N, 最終更新: YYYY-MM)`
- If `last_updated` is null from WebFetch, write `last updated: unknown` (English) or `最終更新: 不明` (Japanese)
- If no candidates remain after filtering, output:

  English:
  ```
  ## New candidates for "$ARGUMENTS"

  No unlisted repositories found.

  Suggestions:
  - Retry with different keywords: ``
  - Check existing similar resources with `/awesome-japanese-nlp-resources:search "$ARGUMENTS"`
  ```

  Japanese:
  ```
  ## "$ARGUMENTS" に関する追加候補

  該当する未収録リポジトリは見つかりませんでした。

  検討事項:
  - 別キーワードで再試行: ``
  - 既存リスト内で類似カテゴリを `/awesome-japanese-nlp-resources:search "$ARGUMENTS"` で確認
  ```

### Step 9 — Sources

Append the `Sources:` section required by WebSearch — list the URLs of the WebSearch results you actually used.

Finally, clean up the temp file: `rm -f "$EXISTING_URLS_FILE"`

## 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.