tomevault-io Find new Japanese NLP GitHub repositories and Hugging Face models/datasets 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} newjapanese natural language processing tool released ${YEAR_PREV} ${YEAR_NOW}japanese NLP new model huggingface ${YEAR_NOW}huggingface japanese nlp ${YEAR_PREV} ${YEAR_NOW} new
- Output heading: "New Japanese NLP Resource Candidates" instead of
New candidates for "$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 << 'EOF'
import json, re, os
RESOURCES_PATH = "RESOURCES_PATH" # from Step 2
OUTPUT_PATH = "EXISTING_URLS_FILE" # from the mktemp above
data_dir = os.path.dirname(os.path.abspath(RESOURCES_PATH))
prebuilt = os.path.join(data_dir, "existing_urls.txt")
urls = set()
source = ""
# Fast path: use pre-built existing_urls.txt if present
if os.path.exists(prebuilt):
with open(prebuilt) as f:
urls = {line.strip().lower() for line in f if line.strip()}
source = f"pre-built {prebuilt}"
# Fallback: derive from resources.json + walk to repo-root README.md / docs/huggingface.md
if not urls:
with open(RESOURCES_PATH) as f:
data = json.load(f)
for item in data:
u = (item.get("u") or "").lower().rstrip("/")
if not u:
continue
urls.add(u)
if "github.com/" in u:
parts = u.split("github.com/", 1)[1].split("/")
if len(parts) >= 2:
urls.add(f"https://github.com/{parts[0]}/{parts[1]}".lower())
elif "huggingface.co/" in u:
tail = u.split("huggingface.co/", 1)[1]
is_dataset = tail.startswith("datasets/")
parts = tail[len("datasets/"):].split("/") if is_dataset else tail.split("/")
if len(parts) >= 2:
prefix = "datasets/" if is_dataset else ""
urls.add(f"https://huggingface.co/{prefix}{parts[0]}/{parts[1]}".lower())
count_json = len(urls)
try:
p = os.path.abspath(data_dir)
scan_files = []
for _ in range(6):
p = os.path.dirname(p)
readme = os.path.join(p, "README.md")
if os.path.exists(readme):
scan_files.append(readme)
if os.path.exists(os.path.join(p, "awesome-japanese-nlp-resources.json")):
# reached repo root — Hugging Face models/datasets aren't listed in
# README.md, so scan their dedicated doc too
hf_doc = os.path.join(p, "docs", "huggingface.md")
if os.path.exists(hf_doc):
scan_files.append(hf_doc)
break
url_pattern = re.compile(
r"https://(?:github\.com|huggingface\.co)/(?:datasets/)?[A-Za-z0-9_.-]+/[A-Za-z0-9_.-]+"
)
for scan_file in scan_files:
before = len(urls)
with open(scan_file) as f:
content = f.read()
for url in url_pattern.findall(content):
urls.add(url.lower().rstrip("/"))
if len(urls) > before:
print(f"Supplemented {len(urls)-before} URLs from {scan_file}")
except Exception as e:
print(f"README.md/docs scan skipped ({e}), using resources.json only")
source = f"derived ({count_json} from JSON, {len(urls)-count_json} from doc 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 6–9 WebSearch queries to find candidate GitHub repos and Hugging Face models/datasets. Mix English and Japanese, and mix both platforms — do not run only GitHub-flavored queries. Vary phrasing to widen coverage. Do not use the gh CLI in this project — rely on WebSearch + WebFetch only.
GitHub-oriented:
<english-keyword> japanese site:github.comjapanese <english-keyword> ${YEAR_NOW} site:github.com<topic> 日本語 github(Japanese-side phrasing)awesome japanese <english-keyword><japanese-tool-name> github(when a topic maps to known tool names likemecab,sudachi,voicevox)
Hugging Face-oriented (always include at least 2–3 of these, even for library-flavored topics — a model or dataset candidate is often just as good a contribution):
<english-keyword> japanese site:huggingface.cohuggingface japanese <english-keyword> ${YEAR_NOW}japanese <english-keyword> model huggingface<topic> 日本語 huggingface
Optional: <english-keyword> japanese language model ${YEAR_PREV} for LLM-flavored topics.
From each result, extract every URL matching:
https://github.com/<owner>/<repo>(ignore deeper paths like/issues,/pull/,/blob/,/tree/)https://huggingface.co/<owner>/<name>for models,https://huggingface.co/datasets/<owner>/<name>for datasets (ignore deeper paths like/tree/,/blob/,/discussions/,/commits/)
Capture them into a candidate set, lowercased and with trailing slashes stripped, tagging each with its kind (github, hf_model, or hf_dataset) — you'll need this in Steps 6–8.
Step 5 — Filter against the existing dataset
Drop any candidate URL whose lowercased form (or its https://github.com/owner/repo / https://huggingface.co/owner/name / https://huggingface.co/datasets/owner/name prefix) is in $EXISTING_URLS_FILE (the temp file from Step 3). Deduplicate by owner/repo (or owner/name) pair within each kind — a GitHub repo and a same-named Hugging Face model are different resources and both may be kept.
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. Keep a mix of both kinds rather than letting one platform crowd out the other.
Step 6 — Enrich top candidates via WebFetch
For each surviving candidate (cap at 10–15 total, spread across both kinds — don't let GitHub crowd out all the Hugging Face slots or vice versa), call WebFetch on the URL to confirm it exists and extract the essentials.
For github candidates:
WebFetch url="https://github.com/<owner>/<repo>" 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."
For hf_model / hf_dataset candidates:
WebFetch url="https://huggingface.co/<owner>/<name>" prompt="Extract as JSON: name, one-line description / model card summary, pipeline or task tag, whether it is a model or a dataset, downloads count, likes count, last-updated date (in YYYY-MM format), and whether the card mentions Japanese/日本語. 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:
- (GitHub only)
archived: trueorfork: true - The description / card does not mention Japanese / 日本語 / NLP
- (GitHub)
stars < 3ANDlast_updatedis more than 24 months ago - (Hugging Face)
likes < 3ANDlast_updatedis more than 24 months ago
Keep the metadata for the survivors — you'll cite stars (GitHub) or downloads/likes (Hugging Face) and last-updated date in the output.
Step 7 — Categorize
Group the final survivors (target 5–15) under awesome-japanese-nlp-resources section headings.
Hugging Face candidates (hf_model / hf_dataset) go straight into one of two headings, inferred from whether the WebFetch result said "model" or "dataset":
| Heading | Cues |
|---|---|
| Hugging Face model | hf_model kind; pipeline tag like text-generation, fill-mask, sentence-similarity, automatic-speech-recognition, translation, etc. |
| Hugging Face dataset | hf_dataset kind; URL contains /datasets/ |
GitHub candidates (github) are grouped by lang and description, inferred as before:
| Heading | Cues |
|---|---|
| Python library | Python primary, library/SDK description |
| C++ | C++ primary, native library |
| Rust crate | Rust primary |
| JavaScript | JavaScript / TypeScript |
| Go | Go primary |
| Java | Java primary |
| Pretrained model | "model", "weights", "fine-tuned", links to HuggingFace |
| ChatGPT / LLM application | LLM app, RAG, agent |
| Dictionary and IME | dictionary, lexicon, IME |
| Corpus | corpus, dataset, annotation |
| Tutorial | tutorial, course, lecture, "学習", "入門" |
| Research summary | survey, paper list, "サーベイ" |
Step 8 — Output contribution-ready markdown
Language detection rule (apply before writing any output):
$ARGUMENTSis empty → English$ARGUMENTScontains Japanese characters (hiragana / katakana / kanji) → Japanese- Otherwise → English
Apply the detected language to all headings and prose. Resource descriptions in the bullet lines are always in English regardless of output language (the awesome list standard).
English output template (default):
## New candidates for "$ARGUMENTS"
Found **N** resources not yet in awesome-japanese-nlp-resources (**G** GitHub repositories, **H** Hugging Face models/datasets).
*(Search keywords: keyword1, keyword2, ...)*
### Python library
* [repo-name](https://github.com/owner/repo) - One-line English description. (⭐ 123, last updated: YYYY-MM)
* ...
### Corpus
* ...
### Hugging Face model
* [model-name](https://huggingface.co/owner/name) - 📥 {downloads} / ⭐ {likes} / One-line English description.
* ...
### Hugging Face dataset
* [dataset-name](https://huggingface.co/datasets/owner/name) - 📥 {downloads} / ⭐ {likes} / One-line English description.
* ...
---
### Notes
- The `<dominant category>` category is most active in the "$ARGUMENTS" space
- Suggested section in `README.md`: `<section-name>` (GitHub candidates) / `docs/huggingface.md`: `<pipeline-tag section>` (Hugging Face candidates)
- Highlight: <name> (⭐ N or 📥 N) — short reason why it stands out
- Follow-up suggestion: <alternative keywords to try next>
Japanese output template (when query is in Japanese):
## "$ARGUMENTS" に関する追加候補
awesome-japanese-nlp-resources に未収録のリソース **N 件** を発見しました（GitHub リポジトリ **G 件** / Hugging Face モデル・データセット **H 件**）。
*(検索キーワード: keyword1, keyword2, ...)*
### Python library
* [repo-name](https://github.com/owner/repo) - One-line English description. (⭐ 123, 最終更新: YYYY-MM)
* ...
### Corpus
* ...
### Hugging Face model
* [model-name](https://huggingface.co/owner/name) - 📥 {downloads} / ⭐ {likes} / One-line English description.
* ...
### Hugging Face dataset
* [dataset-name](https://huggingface.co/datasets/owner/name) - 📥 {downloads} / ⭐ {likes} / One-line English description.
* ...
---
### 追加の検討メモ
- "$ARGUMENTS" 領域では `<dominant category>` のリソースが特に活発
- 推奨追加先: `README.md` の `<section-name>` セクション（GitHub の場合）/ `docs/huggingface.md`（Hugging Face の場合）
- 注目株: <name> (⭐ N or 📥 N) — 短い推薦理由
- フォローアップ提案: <その他のキーワードで再検索すると良い場合の提案>
Rules for the bullet lines:
GitHub candidates: format
* [name](url) - description. (⭐ N, last updated: YYYY-MM)matches the existingREADME.mdcontribution style exactly — paste-ready for a PRHugging Face candidates: format
* [name](url) - 📥 {downloads} / ⭐ {likes} / description.matches the existingdocs/huggingface.mdcontribution style exactly — paste-ready for a PR. Abbreviate downloads withk/Msuffixes the same waydocs/huggingface.mddoes (e.g.📥 367k,📥 2M)Keep the description ≤ 100 characters and in English. If the repo/model's own README or card is in Japanese, translate it to English for the bullet line (the awesome list keeps resource descriptions in English)
For GitHub, English output appends
(⭐ N, last updated: YYYY-MM); Japanese output appends(⭐ N, 最終更新: YYYY-MM)If
last_updatedis null from WebFetch, writelast updated: unknown(English) or最終更新: 不明(Japanese)If no candidates remain after filtering, output:
English:
## New candidates for "$ARGUMENTS" No unlisted resources found (checked both GitHub and Hugging Face). Suggestions: - Retry with different keywords: `<suggested keywords>` - Check existing similar resources with `/awesome-japanese-nlp-resources:search "$ARGUMENTS"`Japanese:
## "$ARGUMENTS" に関する追加候補 該当する未収録リソースは見つかりませんでした（GitHub / Hugging Face とも確認済み）。 検討事項: - 別キーワードで再試行: `<suggested keywords>` - 既存リスト内で類似カテゴリを `/awesome-japanese-nlp-resources:search "$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: taishi-i/awesome-japanese-nlp-resources — distributed by TomeVault.