- name
- ai-newsletter-daily
- description
- >
- version
- 1.3.0
- author
- Jeff Yang (https://github.com/j3ffyang)
- user-invocable
- true
- category
- content
- license
- MIT
- metadata
- openclaw
- skillKey
- ai-newsletter-daily
- emoji
- 🗞️
- required-tools
- requires
- env
- commands
- description
- Generate a daily AI news digest in Markdown and JSON.
- arg-mode
- raw
AI Newsletter Daily
Generate a concise daily AI newsletter from fresh web sources.
Use this skill only 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 intended to become a curated newsletter.
Inputs
Defaults:
target_news_count= 20search_query="latest AI news today"search_time_window_days= 2max_search_results= 60min_articles_required= 10include_domains=[]exclude_domains=["youtube.com", "reddit.com", "facebook.com", "x.com", "twitter.com"]summary_model="host-default"max_scrape_retries= 2
Bounds:
target_news_count: 1..50search_time_window_days: 1..14max_search_results: 20..120min_articles_required: 1..50max_scrape_retries: 0..5
If min_articles_required > target_news_count, set it to target_news_count.
Batch policy
- Search up to
max_search_resultscandidates. - Keep the top
target_news_count * 2candidates for fetch attempts. - Return only the top
target_news_countverified items. - Do not summarize every search result.
Required outputs
Return:
newsletter_itemsas a list of objects.markdown_newsletteras a string.json_newsletteras an object.
Each item must include:
titleurldomainpublished_atsummaryrelevance_scoresource_query
Use "unknown" for missing published_at.
Workflow
- Resolve inputs.
- Apply defaults and bounds. - Initialize warnings = [], seen_canonical_urls = set(), processed_urls = set().
- Search.
- Run web_search with search_query. - If no usable results, retry once with: - "{search_query} generative AI LLM model open source enterprise" - If still no usable results, fail clearly.
- Normalize and filter.
- Keep only results with non-empty title and URL. - Canonicalize URLs: lowercase host, remove tracking parameters, normalize 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.
- 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 desc - published_at desc, unknown last - url asc - Keep the top target_news_count * 2 candidates.
- 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, - published date when available. - If the page appears materially inconsistent, skip it and warn. - Summarize in one plain-text paragraph, max about 80 words. - Focus on why it matters to AI practitioners. - If summary generation fails, warn and continue. - Append the enriched item.
- 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.
- 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.
- Finalize.
- Sort by relevance_score desc, then published_at desc. - Truncate to target_news_count. - Render markdown_newsletter. - Assemble json_newsletter. - Return all outputs.
Verification rules
Accept an item only if it passes these checks:
- URL integrity:
- canonical URL is valid, - duplicates removed, - malformed URLs rejected.
- Source consistency:
- search title and fetched title broadly match, - snippet and page content describe the same story, - off-topic pages rejected.
- Metadata sanity:
- valid published date preferred, - unknown date allowed only if the rest is strong, - malformed or impossible dates rejected.
- Content integrity:
- fetched content must be substantively about the same AI news item, - truncated or malformed pages rejected.
- Warning log:
- record every failed URL and reason, - record whether fallback search was used.
Markdown format
markdown_newsletter must use:
- H1 title with date.
- One H2 section per article.
- One short summary paragraph per article.
- One source link per article.
Example:
AI Newsletter Daily — 2026-04-28
1. Article title
Summary paragraph.
Source: link
Warnings
Only include this section when needed.
Failure policy
Hard fail only when:
- Both initial and fallback searches return no usable URLs.
- Required tools are unavailable.
Soft fail and continue when:
- A single fetch fails.
- A single summary fails.
published_atis missing.- A candidate fails cross-check verification.
Partial success is acceptable when the result count is between min_articles_required and target_news_count.
Always include actionable warnings with URL, short reason, and whether fallback search was used.
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.
Return shape
json_newsletter must contain:
datequerycountarticleswarnings