Hot Topics

Hot Topics

Use this skill to collect current topics across Chinese and global sources, then filter them by the user's configured preferences.

Use This For

• daily topic discovery
• source-aware trend collection
• pre-writing topic research
• feeding high-signal items into ViralKB

Do Not Use This For

• retrieving title formulas from ViralKB
• drafting articles directly
• generating images
• deep single-topic research (use last30days instead)

Inputs

• optional keyword override
• optional source or platform override
• optional result limit

Outputs

• ranked topic list grouped by source
• tagged categories for each topic
• explicit separation between discussion heat and strategic importance when needed
• optional ViralKB ingestion summary
• optional topic framing that can seed an article brief

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Workflow

Step 1: Topic Quality Pre-Flight — detect keyword traps BEFORE running searches

Mandatory. Check the topic for known failure classes. Running searches on a keyword-trap topic burns time and produces junk. Detecting the trap upfront costs one turn.

Known trap classes:

| Class | Pattern | Why it fails | Action | |-------|---------|--------------|--------| | Demographic shopping | gift for {age} year old {gender}, present for {demographic} | Real posts use relationship + hobbies, not literal age | Ask for hobbies/relationship/budget | | Numeric keyword trap | Topic contains a number that collides with unrelated content (42 = Jackie Robinson, 40 = 40th anniversary) | Number dominates retrieval, pulls unrelated content | Strip the number unless semantically load-bearing | | Overly-literal concept | how to use X, what is Y tutorial | Social posts use different vocabulary ("my Docker setup", not "how to use Docker") | Reframe to discussion phrasing | | Generic single-noun | bread, sneakers, coffee with no specific hook | Infinite corpus, signal is noise | Ask for specificity |

If topic matches a trap class: emit a visible one-liner and either ask a clarifying question OR reframe before proceeding.

If topic does NOT match any class: emit Pre-Flight: {topic type} - proceeding. Then proceed to Step 2.

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Step 2: Entity Pre-Resolution — resolve who matters BEFORE searching

Before running searches on a named entity topic (person, brand, product, company):

1. Resolve X/Twitter handles (if platform is available):

`` WebSearch("{TOPIC} X twitter handle site:x.com") WebSearch("{TOPIC} CEO/founder X twitter site:x.com") # for companies WebSearch("{TOPIC} creator founder X twitter site:x.com") # for products `` Extract handles and pass them to the search commands.

1. Resolve GitHub repos (if topic is a product/project):

`` WebSearch("{TOPIC} github repo site:github.com") ` Extract owner/repo` for project-mode data.

1. Resolve related subreddits / communities:

`` WebSearch("{TOPIC} subreddit site:reddit.com") `` Find active communities discussing this topic.

Why this matters: keyword search is shallow. "DeepSeek" as a bare keyword matches everything containing "DeepSeek." Resolving @deepseek-ai, deepseek-ai/deepseek, and r/LocalLLaMA first gives you precise targeting that bare search cannot achieve.

Skip this step if:

• Topic is a generic concept (no specific entity)
• User provided handles/repos directly
• Using --quick depth

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Step 3: Collect Data — parallel channels

Collect from two channels in parallel:

A. opencli sources (if available):

• 小红书 (xiaohongshu)
• 知乎 (zhihu)
• B站 (bilibili)
• Twitter/X (twitter)
• GitHub Trending

B. RSS / API sources:

• Hacker News (front page RSS)
• Reddit (subreddit feeds)
• BBC News (tech RSS)

Failover behavior:

• If opencli is unavailable for a source, mark it as unavailable and continue with remaining sources
• Do not block on a single failing source; run all channels in parallel and merge results

---

Step 4: Cross-Source Deduplication — merge same story from different platforms

When the same story appears in multiple places:

• Merge into one entry, not three separate items
• Track which platforms confirmed it
• Attribution: [Platform1] · [Platform2] · +N more

Do NOT count repeated reposts as independent confirmation. A story appearing in 5 B站 reposts is still one story, not five.

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Step 5: Rank with Engagement Scoring — not just discussion volume

Rank results using TWO dimensions:

Dimension 1 — Discussion Heat (what people are actively talking about):

• HN: points + comment count
• Reddit: upvotes + comment count
• Twitter/X: likes + retweets + views
• 小红书: likes +收藏 +评论
• 知乎: 热度值（显示在列表中）
• B站: 播放量 + 弹幕数

Dimension 2 — Strategic Importance (regardless of volume):

• New model releases or API capabilities
• New enterprise/platform integrations
• Pricing or availability changes
• Major partnerships or acquisitions
• Capability expansions that change what users can do

Key rule: For a general "today's hot topics" answer, allow strategically important product updates to outrank noisier but less consequential discussion threads.

Tag each entry with:

• Primary category
• Engagement signals (e.g., "🔥 89⬆ · 11💬" for HN, "❤️ 4.1k" for 小红书)
• Source platform(s)

---

Step 6: Research Sufficiency Check

Before surfacing a topic as especially strong, confirm at least one of:

• Clear original source exists
• Multiple independent signals point to the same event
• Enough concrete detail to support a writing angle

If a topic is thin, tag it with a confidence caveat.

---

Step 7: Optional ViralKB Ingestion

For topics that qualify for downstream writing:

• Tag with recommended article angle
• Note likely audience
• Note HKR viability (Happy / Knowledge / Resonance)

---

Ranking Rules Summary

| Situation | Action | |-----------|--------| | HN 100⬆ but a model release got 20⬆ | Model release outranks if strategically important | | Same story on 3 platforms | Merge, tag multi-platform | | Single-source topic with weak discussion | Add confidence caveat | | Demographic trap query | Ask clarifying question first |

---

Article Brief Seeding

When a topic is strong enough for downstream writing, return enough structure to seed an article brief:

• topic
• possible angle
• likely audience
• HKR viability notes
• candidate hook
• source set summary
• risk notes if the topic is still thin

---

Output Format

Structure the ranked list as:

## 讨论热度优先

| 话题 | 来源 | 信号 |
|-----|------|-----|
| ... | ... | ... |

## 战略重要性优先

| 话题 | 来源 | 信号 |
|-----|------|-----|
| ... | ... | ... |

Tag each item with:

• [AI工具] / [模型更新] / [AI应用] / [算法突破] / [AI出海] category badge
• Engagement signal (🔥 ⬆ + 💬 count)
• Multi-platform indicator if applicable

---

Categories

• AI工具 / AI Tools
• 模型更新 / Model Updates
• AI应用 / AI Applications
• 算法突破 / Algorithm Breakthroughs
• AI出海 / AI Global Expansion

---

Fallback Guidance

• If opencli is unavailable, continue with RSS and make the reduced coverage explicit.
• If coverage is reduced, say so and frame output as partial rather than fully representative.
• If user preferences are missing, use sane defaults and suggest running setup later.
• If ViralKB ingestion fails, still return the ranked topic list.
• If the topic is a named entity and opencli is unavailable, use WebSearch to resolve handles before running structured searches.

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Relationship with last30days

Use hot-topics for: daily discovery — "what's happening today across AI?"

Use last30days for: deep research — "what is the complete picture of this specific topic over the last 30 days?"

The two skills are complementary. hot-topics feeds the discovery layer; last30days handles the investigation layer.

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References

• references/article-brief-seeding.md
• references/ranking-examples.md
• references/source-priority.md
• references/research-sufficiency.md
• references/topic-scoring.md

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: GiantClam
• Source: GiantClam/auto-viral-article-writer
• License: MIT

Install and usage instructions live in the source repository linked above.