Agent skill

find-new-resources

Find Japanese NLP GitHub repositories and Hugging Face models/datasets that are NOT yet in awesome-japanese-nlp-resources. Suggests candidates to add for a given topic using WebSearch + WebFetch, then outputs contribution-ready markdown.

taishi-igithub.com/taishi-iGitHub ↗
claude-codecan modify filesCC0-1.0
Install
npx skills add taishi-i/awesome-japanese-nlp-resources --skill find-new-resources --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 18 KB
Bundled scripts: none
Allowed tools: BashWebSearchWebFetch
Path: plugins/awesome-japanese-nlp-resources/skills/find-new-resources/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 1,000

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

Find new Japanese NLP GitHub repositories and Hugging Face models/datasets for topic: "$ARGUMENTS" that are not already in the awesome-japanese-nlp-resources list. ## Instructions ### Preamble — Establish the current date Before doing anything else, run this once and remember the values — every step that mentions a year refers to them: ```bash echo "YEAR_NOW=$(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}` -

What's inside
Steps it walks through
  1. Instructions
  2. Preamble — Establish the current date
  3. Step 0 — Handle empty input
  4. Step 1 — Interpret the topic
  5. Step 2 — Locate the existing data file
  6. Step 3 — Build the existing-URL set
  7. Step 4 — Discover candidates via WebSearch
  8. Step 5 — Filter against the existing dataset
  9. Step 6 — Enrich top candidates via WebFetch
  10. Step 7 — Categorize
  11. Step 8 — Output contribution-ready markdown
  12. Step 9 — Sources
Commands it runs
echo "YEAR_NOW=$(date +%Y)"
echo "YEAR_PREV=$(($(date +%Y) - 1))"
echo "RESOURCES_PATH=$RESOURCES_PATH"
More from awesome-japanese-nlp-resources
All skills →
About this skill
What does the find-new-resources skill do?

Find Japanese NLP GitHub repositories and Hugging Face models/datasets that are NOT yet in awesome-japanese-nlp-resources. Suggests candidates to add for a given topic using WebSearch + WebFetch, then outputs contribution-ready markdown.

How do I install it?

Run `npx skills add taishi-i/awesome-japanese-nlp-resources --skill find-new-resources --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From taishi-i/awesome-japanese-nlp-resources, a repository with 1,000 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going