ak-rss-digest

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

AK RSS Digest

AK RSS 阅读摘要

Overview

概述

Use this skill to build a current reading list from the feed bundle in

references/feeds.opml

. Default to the most recent 7 days ending on the current date in

Asia/Shanghai

, and narrow to a single day only when the user explicitly asks for it.

使用该技能从

references/feeds.opml

中的聚合源集合构建当前阅读列表。默认范围为截至

Asia/Shanghai

时区当前日期的最近7天内容，仅当用户明确要求时才缩小至单日内容。

Workflow

工作流程

Run
```
python3 scripts/fetch_today_feed_items.py --format json
```
to collect entries from the configured feeds. This defaults to the most recent 7 days.
Treat feed-level network failures as non-fatal. Continue with the feeds that succeeded and mention major failures only when they materially reduce coverage.
Read the structured output and discard obvious mismatches before opening article pages. Reject items that are clearly raw research papers, release notes, changelogs, benchmark dumps, or narrowly technical implementation logs without broader implications.
Open the remaining candidate links when the feed summary is too thin to judge the article well. Skim for thesis, novelty, readability, and whether the piece offers strong perspective rather than just information.
Score every serious candidate on the rubric below. Output only items with a score strictly greater than
```
7.0
```
.
If nothing clears the threshold, say so directly instead of padding the output with mediocre picks.

运行
```
python3 scripts/fetch_today_feed_items.py --format json
```
从配置的聚合源中收集条目。默认拉取最近7天的内容。
将源级网络故障视为非致命问题。继续处理成功拉取的源，仅当故障严重影响内容覆盖范围时才提及主要故障。
读取结构化输出，在打开文章页面之前剔除明显不匹配的内容。拒绝明显是原始研究论文、发布说明、更新日志、基准测试数据或仅涉及狭窄技术实现且无广泛影响的日志类内容。
当聚合源提供的摘要不足以判断文章质量时，打开剩余候选链接。快速浏览以判断文章的核心论点、新颖性、可读性，以及是否提供了有深度的观点而非仅传递信息。
根据下方评分标准为所有符合要求的候选文章打分。仅输出得分严格高于
```
7.0
```
的内容。
如果没有内容达到评分阈值，直接说明情况，而非用平庸的内容填充输出。

Selection Heuristics

选择准则

Prefer articles with at least one of these traits:

Fresh thinking about AI agents, agent tooling, agent UX, multi-agent workflows, evaluation, deployment, or failure modes.
Strong interviews or conversations with operators, founders, researchers, or engineers who reveal how frontier work is actually being done.
Essays that synthesize a new direction, new constraint, or strategic implication in AI, software, or adjacent technology.
Pieces that are readable and idea-dense for a general technical audience, not just specialists in one subfield.

Penalize heavily or reject:

Pure technical papers and paper summaries with little interpretive value.
Vendor marketing, launch fluff, SEO writing, or obvious news rewrites.
Narrow implementation diaries that do not connect to broader product, research, or ecosystem questions.
Dry reference material that is correct but not worth a strong recommendation.

优先选择具备以下至少一项特征的文章：

关于AI Agent、Agent工具、Agent用户体验、多Agent工作流、评估、部署或故障模式的新颖思考。
与从业者、创始人、研究人员或工程师的深度访谈或对话，揭示前沿工作的实际开展方式。
整合了AI、软件或相邻技术领域新方向、新约束或战略影响的文章。
面向普通技术读者（而非仅某细分领域专家）的可读性强、观点密集的内容。

严重扣分或直接拒绝的内容：

仅含技术论文或论文摘要，缺乏解读价值的内容。
厂商营销文、发布通稿、SEO软文或明显的新闻改写内容。
狭窄的实现日志，未关联更广泛的产品、研究或生态问题的内容。
内容正确但枯燥的参考资料，不值得强烈推荐的内容。

Scoring Rubric

评分标准

```
9-10
```
: Exceptional fit. Strong signal, strong writing, original insight, and clearly valuable for someone tracking AI agents or adjacent frontier shifts.
```
8-8.9
```
: Good recommendation. Worth reading, clear point of view, and relevant enough to the target taste profile.
```
7-7.9
```
: Borderline. Useful but not compelling enough for the final digest. Do not output it.
```
5-6.9
```
: Competent but dry, derivative, too narrow, or not aligned with the target taste profile.
```
<5
```
: Irrelevant, low-signal, or actively unsuitable.

When scoring, weigh these dimensions:

Relevance to AI agents, frontier AI, deep operator insight, or adjacent strategic technology discussion.
Originality of the article's argument or reporting.
Readability and ability to hold attention.
Practical usefulness for someone trying to keep up with meaningful new directions.

```
9-10
```
：完全符合需求。信息密度高、写作优秀、观点原创，对追踪AI Agent或相邻前沿动态的人群有明确价值。
```
8-8.9
```
：值得推荐。内容值得阅读，观点明确，与目标受众的偏好高度相关。
```
7-7.9
```
：临界值。有用但不足以纳入最终摘要，不输出此类内容。
```
5-6.9
```
：合格但枯燥、缺乏新意、范围过窄，或与目标受众偏好不符。
```
<5
```
：无关、信息密度低或完全不适合的内容。

评分时需考量以下维度：

与AI Agent、前沿AI、从业者深度见解或相邻战略技术讨论的相关性。
文章论点或报道的原创性。
可读性与吸引力。
对追踪有意义的技术新方向人群的实际价值。

Output Format

输出格式

Write the final answer in Simplified Chinese. For each article that scores above

, include exactly these elements with Chinese labels:

```
标题
```
: original article title.
```
评分
```
:
```
x/10
```
, use one decimal place when helpful.
```
推荐语
```
: one or two sentences explaining why this is worth reading.
```
摘要
```
: exactly two sentences summarizing the article.
```
链接
```
: canonical article URL.

Use a concise tone that reads like a curated daily brief, not a formal report:

Prefer short, direct sentences over explanatory padding.
Lead with why the article is worth the user's time.
Keep each item compact and scannable.
Avoid English field names such as
```
Title
```
,
```
Score
```
, or
```
Recommendation
```
.

Use this structure for the final answer:

markdown

本期从最近一周的 RSS 里筛出几篇值得看的文章，重点偏 AI agent、前沿判断和不太枯燥的深度内容。

- 标题：文章标题
  评分：8.7/10
  推荐语：1-2 句话，先说为什么值得看。
  摘要：严格两句话，讲清核心观点和价值。
  链接：文章链接

If nothing qualifies, say so directly in Chinese, for example:

markdown

这周没有筛到真正值得推荐的文章。现有更新要么偏技术细节，要么信息密度不够，没有过 7 分线。

最终答案使用简体中文撰写。对于每篇得分高于

的文章，需包含以下带中文标签的元素：

```
标题
```
: 文章原标题。
```
评分
```
:
```
x/10
```
，必要时保留一位小数。
```
推荐语
```
: 1-2句话，说明文章值得阅读的原因。
```
摘要
```
: 严格用两句话总结文章核心内容。
```
链接
```
: 文章标准URL。

采用简洁的语气，类似精心整理的日报风格，而非正式报告：

优先使用简短直接的句子，避免冗余解释。
先说明文章值得读者投入时间的原因。
每个条目保持简洁、易于快速浏览。
避免使用英文字段名如
```
Title
```
、
```
Score
```
或
```
Recommendation
```
。

最终答案采用以下结构：

markdown

本期从最近一周的RSS源里筛出几篇值得看的文章，重点偏AI Agent、前沿判断和不太枯燥的深度内容。

- 标题：文章标题
  评分：8.7/10
  推荐语：1-2句话，先说为什么值得看。
  摘要：严格两句话，讲清核心观点和价值。
  链接：文章链接

如果没有符合要求的内容，直接用中文说明，例如：

markdown

这周没有筛到真正值得推荐的文章。现有更新要么偏技术细节，要么信息密度不够，没有过7分线。

Resources

资源

```
scripts/fetch_today_feed_items.py
```
Use this script to fetch the configured feeds and return recent entries as structured JSON or Markdown.
```
references/feeds.opml
```
Use this as the source of truth for the feed bundle. Keep the workflow anchored to this file unless the user explicitly asks to change the feed list.

```
scripts/fetch_today_feed_items.py
```
使用该脚本拉取配置的聚合源内容，并以结构化JSON或Markdown格式返回近期条目。
```
references/feeds.opml
```
作为聚合源集合的权威来源。除非用户明确要求修改源列表，否则工作流程需基于该文件执行。

Command Examples

命令示例

Fetch the latest week of entries in Shanghai time:

bash

python3 scripts/fetch_today_feed_items.py --format json

Fetch a single day explicitly:

bash

python3 scripts/fetch_today_feed_items.py --date 2026-03-17 --days 1 --timezone Asia/Shanghai --format json

Fetch the latest posts from the past week:

bash

python3 scripts/fetch_today_feed_items.py --days 7 --limit 30 --format json

Inspect a quick Markdown view instead of JSON:

bash

python3 scripts/fetch_today_feed_items.py --format markdown

拉取上海时区最近一周的条目：

bash

python3 scripts/fetch_today_feed_items.py --format json

拉取指定单日的内容：

bash

python3 scripts/fetch_today_feed_items.py --date 2026-03-17 --days 1 --timezone Asia/Shanghai --format json

拉取最近一周的最新内容：

bash

python3 scripts/fetch_today_feed_items.py --days 7 --limit 30 --format json

以Markdown格式快速查看（而非JSON）：

bash

python3 scripts/fetch_today_feed_items.py --format markdown