ClawHub

ai-newsletter-chn

Other

Generate a daily AI news newsletter for a Chinese audience from fresh web sources, summarizing current AI/ML articles into Markdown and JSON with Simplified Chinese output.

Install

openclaw skills install ai-newsletter-chn

AI Newsletter Daily

Generate a concise daily AI newsletter for a Chinese audience from fresh web sources.

Use this skill only when the request is about current AI/ML news, releases, research, funding, product launches, model updates, regulation, benchmarks, or practitioner-relevant developments.

Do not use this skill for:

  • Evergreen explainers.
  • Non-AI topics.
  • Long-form research that is not intended to become a curated newsletter.

Inputs

Expected inputs, with defaults if missing:

  • target_news_count = 20
  • search_query = "latest AI news today"
  • search_time_window_days = 2
  • max_search_results = 60
  • min_articles_required = 10
  • include_domains = []
  • exclude_domains = ["youtube.com", "reddit.com", "facebook.com", "x.com", "twitter.com"]
  • summary_model = "host-default"
  • max_scrape_retries = 2

Rules:

  • Clamp target_news_count to 1..50.
  • Clamp search_time_window_days to 1..14.
  • Clamp max_search_results to 20..120.
  • Clamp min_articles_required to 1..50.
  • Clamp max_scrape_retries to 0..5.
  • If min_articles_required > target_news_count, set min_articles_required = target_news_count.

Batch policy

Use a two-stage batch limit:

  • Search batch: collect up to max_search_results candidates from search.
  • Scrape batch: keep the top target_news_count * 2 ranked candidates for fetch and summary attempts.
  • Final batch: return only the top target_news_count verified items.

Do not summarize every search result. Over-collect, filter, verify, then reduce to the final batch.

Required outputs

Return all of the following:

  1. newsletter_items as a list of objects.
  2. markdown_newsletter as a string.
  3. json_newsletter as an object.

Each newsletter item must include:

  • title
  • url
  • domain
  • published_at
  • summary
  • relevance_score
  • source_query

Use "unknown" for published_at when no date is available.

Deterministic workflow

  1. Resolve inputs.

    • Apply defaults and bounds.
    • Initialize warnings = [].
    • Initialize seen_canonical_urls = set().
    • Initialize processed_urls = set().

  2. Search.

    • Run web_search with search_query.
    • If there are no usable results, retry once with:
      • "{search_query} generative AI LLM model open source enterprise"
    • If there are still no usable results, fail with a clear message.

  3. Normalize and filter.

    • Keep only results with non-empty title and URL.
    • Canonicalize URLs by lowering the host, removing tracking parameters when possible, and normalizing safe trailing slashes.
    • Drop duplicates by canonical URL.
    • Apply include_domains and exclude_domains.
    • Prefer results likely within search_time_window_days.
    • Keep unknown dates, but score them lower.

  4. Rank.

    • Score each candidate from 0 to 100:
      • AI-topic relevance: 0..50.
      • Freshness: 0..30.
      • Title/snippet clarity: 0..20.
    • Sort by:
      • relevance_score descending
      • published_at descending, unknown last
      • url ascending
    • Keep the top target_news_count * 2 candidates.

  5. Verify and summarize.

    • Process candidates in ranked order until target_news_count verified items are collected.
    • Skip candidates whose canonical URL is already in processed_urls.
    • Attempt web_fetch up to max_scrape_retries + 1 times.
    • If fetch fails, add a warning with the URL and reason, then continue.
    • Cross-check search result vs fetched page using title similarity, domain consistency, topic alignment, and published date when available.
    • If the page appears materially inconsistent, skip it and warn.
    • Summarize each accepted article in one short plain-text paragraph, max about 80 words, focused on why it matters to AI practitioners.

  6. Minimum quality gate.

    • If collected items are fewer than min_articles_required, run one fallback search with:
      • "AI news today machine learning model release funding research"
    • Process only new candidates not already seen or processed.
    • Repeat filtering, ranking, verification, and summarization.

  7. Final integrity check.

    • Ensure every final item has non-empty title, url, domain, summary, source_query, and numeric relevance_score.
    • Ensure each URL appears once.
    • Ensure markdown_newsletter and json_newsletter match in item count.
    • Remove and warn on any invalid item.

  8. Finalize.

    • Sort by relevance_score descending, then published_at descending.
    • Truncate to target_news_count.
    • Render markdown_newsletter.
    • Assemble json_newsletter.
    • Apply the language output rule below.
    • Return all outputs.

Language output

  • Translate the final markdown_newsletter body and each article summary in newsletter_items into Simplified Chinese.
  • Keep title, url, domain, published_at, relevance_score, and source_query unchanged.
  • If a source title is already in Chinese, preserve it as-is.
  • Do not add extra commentary outside the newsletter content.

Verification

Accept items only if:

  • URL is valid and canonicalized.
  • Search result and fetched page broadly match.
  • Topic is actually AI/news relevant.
  • Published date is present or safely unknown.
  • Fetched content is not malformed or off-topic.

Record warnings for failed URLs, short reasons, and whether fallback search was used.

Output Format

markdown_newsletter:

  • H1 title with date.
  • One H2 per article.
  • One short summary paragraph per article.
  • One source link per article.

json_newsletter:

  • date
  • query
  • count
  • articles
  • warnings

Safety rules

  • Use only sanctioned tools.
  • Do not request API keys from the user.
  • Do not expose secrets.
  • Do not include copyrighted full article text.
  • Keep summaries neutral, concise, and factual.
  • Preserve deterministic behavior wherever tool outputs allow.