| name | ai-topic-scout |
| description | Daily topic scouting for AI creators and AI bloggers. Use when the user asks to scan AI information sources, get daily AI topic ideas, monitor AI news, turn AI papers/products/open-source trends into content topics, or generate选题/脚本方向 from AI sources. |
AI Topic Scout
Overview
Use this skill to scan a fixed set of essential AI information sources and turn current signals into creator-ready topic ideas. Prioritize freshness, source quality, and audience relevance over raw volume.
Workflow
- Load
references/sources.md for the current source list, source roles, and topic filters.
- Browse or otherwise verify current items from the sources before claiming anything is new, latest, released, ranked, or trending.
- Collect signals across at least 6 of the 10 sources when the user asks for a daily run. If browsing is blocked, state the limitation and use only verifiable local/user-provided context.
- Deduplicate overlapping news. Merge repeated stories into one stronger topic with multiple source angles.
- Score candidates with the topic rubric below.
- Return the best topics in a creator-facing format.
Daily Output Format
Default to 8-12 topic ideas unless the user requests another number. For each topic, include:
选题: A short punchy title in Chinese.为什么现在: The freshness signal and source basis.内容角度: The opinion, explainer, tutorial, comparison, or test angle.适合形式: Short video, long video, thread, newsletter, live demo, or carousel.素材来源: Source names and links.优先级: High / Medium / Low with a one-line reason.
End with a short 今日首选 section naming the top 1-3 topics and why.
Topic Rubric
Score each candidate from 1-5:
- Freshness: Is it new or newly resurging today/this week?
- Creator leverage: Can a creator add explanation, demo, opinion, or comparison beyond repeating news?
- Audience value: Does it help viewers understand what changed, what to use, what to avoid, or what to learn?
- Evidence strength: Is it backed by primary sources, papers, code, benchmarks, or reputable analysis?
- Distinctiveness: Is it less likely to be a generic repost everyone will make?
Prioritize topics with high creator leverage and evidence strength. Do not over-prioritize incremental product announcements unless they change user behavior or the market map.
Angle Patterns
Use these patterns to turn source signals into content:
- New model/product release -> "What changed, who should switch, and what still fails?"
- Paper breakthrough -> "Explain the core idea with one visual metaphor and one real use case."
- Open-source repo spike -> "Hands-on test: can normal users actually use it?"
- Leaderboard movement -> "Benchmark drama: what this ranking does and does not prove."
- Safety/policy update -> "What builders and creators need to change now."
- Big-company strategy -> "What this reveals about the next platform war."
- Toolchain update -> "Workflow before/after demo for creators or developers."
Quality Rules
- Use primary sources first: official blogs, papers, repos, changelogs, leaderboards, and docs.
- Treat X/Twitter, Reddit, and newsletters as discovery/context unless they link to stronger evidence.
- Include concrete dates when discussing "today", "yesterday", "latest", or "this week".
- Separate confirmed facts from inference.
- Avoid hallucinating product details, benchmark ranks, pricing, availability, or release dates.
- If sources disagree, mention the disagreement rather than smoothing it away.
References
references/sources.md: The 10 core AI information sources, what to pull from each, and search patterns.
