Ai News Digest

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.search_query with references/x_watchlist.json and references/non_rss_sources.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 GITHUB_TOKEN (or GH_TOKEN) 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).

2) 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”.

3) 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 —
• 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/non_rss_sources.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/fetch_github_ai_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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: lgldlk
• Source: lgldlk/lgldlk-agent-skills
• License: MIT

Install and usage instructions live in the source repository linked above.