Agent skill · writing content · rookie-ricardo
ak-rss-digest
Curate a Chinese reading digest from a fixed bundle of RSS and Atom feeds, with a strong preference for AI agent thinking, frontier AI commentary, deep interviews, and non-boring high-signal essays. Use when Codex needs to pull the latest week's posts by default, or a specific day's posts when explicitly requested, summarize them, score each article on a 10-point scale, and output only the posts scoring above 7 in a concise Chinese daily-brief style.
Why this skill is useful
Adds a specific workflow for curating and scoring AI-related articles from RSS feeds, which the AI wouldn't generate on its own.
What it needs
Requires python3 installed locally. About 3k tokens when loaded. Last updated 2026-07-25. 929 stars on the source repository.
What this skill does
AK RSS Digest Overview 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. Workflow 1. Run python3 scripts/fetchtodayfeeditems.py --format json to collect entries from the configured feeds. This defaults to the most recent 7 days. 2. Treat feed-level network failures as non-fatal. Continue with the feeds that succeeded and mention major failures only when they materially reduce coverage. 3. 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. 4. 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. 5. Score every serious candidate on the rubric below. Output only items with a score strictly greater than 7.0. 6. If nothing clears the threshold, say so directly instead of padding the output with mediocre picks. Selection Heuristics 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills rookie-ricardo/ak-rss-digest