lgldlk/lgldlk-agent-skills · Archived

ai-news-digest

Fetch and summarize the latest AI news from reputable sources (via RSS/Atom feeds) and produce a dated digest with links.

First seen Jun 22, 2026

Installation

$ npx skills add lgldlk/lgldlk-agent-skills --skill ai-news-digest

Summary

  • Fetch and summarize the latest AI news from reputable sources (via RSS/Atom feeds) and produce a dated digest with links.
  • Use when the user asks for “latest AI news”, “AI 新闻汇总/简报”, “本周/今日 AI 动态”, or when you need to gather up-to-date AI announcements, research highlights, and industry coverage from known sites.

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 3
License LICENSE
Default branch main
Open issues 0
Status Archived

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 4,745 B
  • docs SUMMARY.md 370 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

AI News Digest

Workflow (required)

强制要求:在生成 AI 新闻汇总/简报前,必须先运行脚本获取最新数据。不要凭记忆或直接编写新闻摘要。若脚本失败,先重试一次;仍失败再使用 web.run 作为兜底,并在结果中标注“数据来源与时间窗口”。

  1. 必须抓取最新新闻

默认抓取过去 24 小时;如果用户指定“本周”“过去 72 小时”“今天”等范围,调整 --since。Use a task-local output path such as ./ai-news.json, ./ai-news.md, or a file under a temporary working directory. From the skill directory, run:

python3 scripts/fetch_ai_news.py --since 24h --limit 0 --max-per-source 0 --format json --output ./ai-news.json

Or generate Markdown directly (mandatory alternative):

python3 scripts/fetch_ai_news.py --since 24h --limit 0 --max-per-source 0 --format md --output ./ai-news.md

1b) 需要时补充 X + 非 RSS 站点

  • Use web.run.searchquery with references/xwatchlist.json and references/nonrsssources.json.
  • Keep only items within the same time window (e.g. past 72 hours), and prefer links to the canonical announcement/blog/paper.

1c) 需要时补充 GitHub “热门上榜项目” + 社区热度/反馈

Run:

python3 scripts/fetch_github_ai_trending.py --since daily --limit 0 --format md --output ./github-ai.md

Notes:

  • This uses GitHub Trending (HTML) for the “up榜热门” list, then enriches repos via GitHub API for heat/feedback signals.
  • Set GITHUBTOKEN (or GHTOKEN) to avoid low rate limits.
  • Feedback signals include: open PRs, open/closed issues in the past N days, “good first issue/help wanted”, and top discussed issues.
  • If you only need “hot list”, run faster with --no-feedback (and/or --no-profile).
  1. 生成中文汇总(必须)
  • Output in Chinese unless the user asked for English.
  • Always include specific dates (e.g. “2026-02-04”) for “today / latest / yesterday”.
  • For each item: 发生了什么 + 为什么重要 + link(必要时 2 links).
  • If items conflict across sources, mention uncertainty and cite multiple links.
  • End with a Chinese wrap-up section: “趋势与建议” (3–7 bullets). For 24h digests, title it “24小时趋势与建议”.
  1. If the user wants only certain outlets/topics
  • Edit references/sources.json (add/remove feeds, or toggle "enabled": true/false), then re-run the script.
  • Use filters when you only need a subset:

- Keywords: --keywords agent,benchmark,open-source - Source tags: --include-tags vendor,research / --exclude-tags community

Output templates

Short digest (default)

  • Title: AI News Digest — <DATE RANGE>
  • Sections (optional): Product/Company, Research, Policy/Safety, Market/Business
  • 8–15 bullets total, each with 1 link (2 links if needed)

- If there are many items within 24h, cluster by theme and summarize each cluster; do not drop items silently.

Long digest

  • Add 1–2 sentence summaries per item, grouped by section
  • Include a “Notable trends” section at the end (3–5 bullets)

Notes

  • Prefer RSS/Atom feeds over scraping HTML for reliability.
  • If a target site has no public feed, use web.run (search + open) as a fallback and still include dated links.
  • For non-RSS “known sites”, use references/nonrsssources.json query templates with web.run.search_query.
  • For X (Twitter) news, prefer web.run search (no scraping): use references/x_watchlist.json handles + templates, and set a clear time window (e.g. past 72 hours).
  • For GitHub trending, prefer scripts/fetchgithubai_trending.py (no scraping beyond the Trending listing page).
  • If a feed is down, retry once, then skip and report which sources failed.
  • Do not claim anything is “latest” without a date/time window (e.g. “past 72 hours”).

X (Twitter) quick recipe (web.run)

Use web.run.search_query with recency (days) and queries like:

  • site:x.com/OpenAI (announce OR released OR launch OR model OR paper)
  • site:x.com (OpenAI OR AnthropicAI OR GoogleDeepMind OR MistralAI OR xai) (released OR launch OR model)

Then: extract only posts that link to an official announcement/blog/paper, and include both the X link and the canonical source link when possible.