ai-newsletter-chn-for-hermes

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name: ai-newsletter-daily description: > Generate a daily AI news newsletter for a Chinese audience from fresh web sources. Return the newsletter body and article summaries in Simplified Chinese. version: 1.0.0 author: Jeff Yang (https://github.com/j3ffyang) license: MIT platforms: [linux, macos, windows] metadata: hermes: tags: [AI, News, Newsletter] requires_toolsets: [web] requires_tools: [web_search, web_fetch] required_environment_variables: - name: BRAVE_API_KEY prompt: Enter your BRAVE API key help: Required for web search required_for: Web search - name: FIRECRAWL_API_KEY prompt: Enter your Firecrawl API key help: Required for web fetching required_for: Web fetching

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AI Newsletter Daily

When to Use

Use for current AI/ML news, releases, research, funding, product launches, model updates, regulation, benchmarks, or practitioner-relevant developments.

Do not use for evergreen explainers, non-AI topics, or long-form research that is not meant to become a curated newsletter.

Procedure

1. Resolve inputs. - Defaults: 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. - Clamp: target_news_count 1..50, search_time_window_days 1..14, max_search_results 20..120, min_articles_required 1..50, max_scrape_retries 0..5. - If min_articles_required > target_news_count, set it to target_news_count.

2. Search and filter. - Run web_search with search_query. - If no usable results, retry once with "{search_query} generative AI LLM model open source enterprise". - Keep only results with non-empty title and URL. - Canonicalize URLs, drop duplicates, apply domain filters, and prefer fresh results.

3. Rank. - Score 0..100 from AI-topic relevance, freshness, and title/snippet quality. - Sort by score desc, published date desc, URL asc. - Keep top target_news_count * 2 candidates.

4. Fetch, verify, summarize. - Process candidates in order until target_news_count verified items are collected. - Skip already processed canonical URLs. - Fetch each candidate up to max_scrape_retries + 1 times with web_fetch. - Verify title, domain, topic, and date against the search result. - Skip inconsistent pages and record a warning. - Summarize each accepted article in one plain-text paragraph, max ~80 words, focused on why it matters to AI practitioners.

5. Fallback. - 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 and repeat the same filter/rank/fetch/verify/summarize flow.

6. Finalize. - Keep only valid items with non-empty title, url, domain, summary, source_query, and numeric relevance_score. - Remove duplicates by canonical URL. - Sort by score desc, then published date desc. - Truncate to target_news_count. - Return newsletter_items, markdown_newsletter, and json_newsletter.

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

Language Output

Return the newsletter body and all article summaries in Simplified Chinese. Preserve all source metadata unchanged (title, url, domain, published_at, relevance_score, source_query).