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ai-newsletter艾时事通讯

Agent Skill

ai-newsletter 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

3,408

周安装

142

GitHub Stars

公开资料未说明

下载量

1,136
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ai-newsletter(艾时事通讯)
来源仓库:https://github.com/j3ffyang/ai-newsletter
安装命令:
openclaw skills install ai-newsletter
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install ai-newsletter

简介

每日 AI 新闻通讯从全网资源自动抓取并生成精选摘要。ai-newsletter 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

  • 当用户询问当前 AI 摘要或新闻综述时自动触发此技能。
  • 输出 Markdown 与 JSON 格式便于后续编辑与系统集成。
  • 信源覆盖广泛但可能存在时效性差异,建议交叉验证重要信息。
  • 安装后可在 OpenClaw 中定时运行或按需提供定制化简报。

SKILL.md

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 = 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

Bounds:

  • 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.

Batch policy

  • Search up to max_search_results candidates.
  • Keep the top target_news_count * 2 candidates for fetch attempts.
  • Return only the top target_news_count verified items.
  • Do not summarize every search result.

Required outputs

Return:

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

Each item must include:

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

Use "unknown" for missing published_at.

Workflow

  1. Resolve inputs.

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

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. 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_at is 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:

  • date
  • query
  • count
  • articles
  • warnings

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

96.73%
按下载量换算1,099

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权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

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