ai-newsletter-chn

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name: ai-newsletter-daily description: > 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. version: 1.2.1 author: Jeff Yang (https://github.com/j3ffyang) user-invocable: true category: content license: MIT metadata: openclaw: skillKey: ai-newsletter-daily emoji: "🗞️" required-tools: - web_search - web_fetch requires: env: - BRAVE_API_KEY - FIRECRAWL_API_KEY commands: - name: ai-newsletter description: Generate a daily AI news digest in Markdown and JSON. arg-mode: raw

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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.