Trend Deep Dive

trend-deep-dive

Single-topic, multi-angle investigation skill. Given one topic (a person, event, product, hashtag, or phrase), produce a structured report covering heat curve, lifecycle stage, viral anomaly assessment, near-future prediction, related satellite events, and sentiment evolution. Backed by the trendradar MCP.

When to activate

Activate when the user's intent is to understand one topic in depth (not to scan many topics, not to compose a campaign). Trigger phrases include:

• "深挖 XX" / "把 XX 说清楚" / "XX 是怎么演变的"
• "XX 的趋势" / "XX 的热度曲线" / "XX 这几天怎么样"
• "XX 处于什么阶段" / "XX 的生命周期" / "XX 还能火多久"
• "XX 会不会火" / "XX 接下来怎么走" / "predict X 的走势"
• "XX 是不是异常热点" / "有没有人为推动" / "是不是被刷的"
• "XX 的舆情" / "舆情演化" / "网友怎么看 XX"
• "deep dive on X" / "go deep on X"

If the user asks about many topics at once, redirect to trend-monitor. If they want to make a piece of content off the topic, redirect to viral-forge. If they want a report deliverable, hand off to trend-report after finishing the analysis.

Decision tree for analysis_type

mcp__trendradar__analyze_topic_trend accepts an analysis_type argument. Pick exactly one based on the user's primary goal:

| User goal (paraphrased) | analysis_type | Notable args | | ---------------------------------------------------------- | --------------- | ---------------------------------------------------------- | | "热度曲线 / 看变化 / 这几天怎么样 / 趋势是啥" | trend | granularity="day" (default) or "hour" if window ) `` Use the returned date_range` for every downstream call. If the user gave no time hint, default to the last 7 days and state that assumption in the final report.

1. Run the primary analysis. Pick analysis_type via the decision tree above, then:

`` mcp__trendradar__analyze_topic_trend( topic=, analysis_type=, date_range=, granularity="day" # use "hour" if window ≤ 72h or user wants intraday detail ) `` Capture the returned data points, peak timestamps, and any lifecycle/anomaly/prediction fields.

1. Surface satellite events. Run:

`` mcp__trendradar__find_related_news( reference_title=, date_range=, threshold=0.5, limit=30 ) `` These are the related events orbiting the topic — sub-stories, reactions, parallel angles. Sort by rank/heat.

1. Analyze sentiment. Over the related-news pool from step 3 (or, if it returned ,

date_range=, # pass article ids/urls from step 3 if the tool accepts them; otherwise let it re-fetch ) ``` Extract: positive %, neutral %, negative %, and one representative quote per bucket.

1. (Optional) Period comparison. If the user said "vs 上周", "比上个月怎么样", "compared to last month", call:

`` mcp__trendradar__compare_periods( topic=, current_range=, previous_range= ) ``

1. Read the substantive top items. Take the top 3–5 highest-ranked items from step 3 and pull their bodies:

`` mcp__trendradar__read_articles_batch(article_ids=[...]) `` Use these to ground key-node descriptions and sentiment quotes — never paraphrase from titles alone.

Output schema

Render the final answer as Markdown with all of the following sections, in this order. Omit a section only if explicitly N/A and say so.

## 一句话结论

## 热度曲线
| 日期/小时 | 热度 | 备注 |
|---|---|---|
| ... | ... | ... |

**趋势判定**: 上升 / 平台 / 衰退 (+ 短理由)

## 生命周期阶段
**当前阶段**: 萌芽 / 爆发 / 平台 / 衰退
**判定依据**: 

## 关键节点
- YYYY-MM-DD · 平台 · 
- ...

## 情感分布
- 正面 X% — 代表观点: "..."
- 中性 Y% — 代表观点: "..."
- 负面 Z% — 代表观点: "..."

## 相关事件 / 卫星话题
1.  (相似度 0.xx) — 一句话定位
2. ...

## 预测 (仅在调用了 predict 时)
- 未来 N 小时方向: 上行 / 持平 / 下行
- 置信度: 0.xx
- 触发再涨/熄火的关键变量: ...

## 数据来源与置信度
- MCP 调用: analyze_topic_trend(), find_related_news, analyze_sentiment, [compare_periods], read_articles_batch
- 数据点数量: N 篇文章 / M 个时间桶
- 置信度评估: 高 / 中 / 低 — 

Edge cases

• Topic too rare / no data. If analyze_topic_trend returns an empty curve, do not fabricate. Tell the user: "过去 内 trendradar 索引中未发现 ``" and offer two follow-ups: (a) widen the date range, (b) try a related broader keyword.
• English entity name. Run find_related_news with both the English term and the most likely 中文译名 (e.g. "OpenAI" + "OpenAI 公司" / "DeepSeek" + "深度求索"). Merge the two pools, dedupe by article id.
• Ambiguous / polysemous topic (e.g. "苹果" = company OR fruit, "Mercury" = planet OR element OR celebrity). First run find_related_news with limit=10, glance at returned titles. If you see ≥2 distinct senses, stop and ask the user which sense they mean before continuing — do not silently pick.
• Sentiment-only ask. If the user explicitly only wants 舆情 / 情感, you may skip lifecycle and predict but still run analyze_topic_trend(analysis_type="trend") to provide the curve context — sentiment without volume context is misleading.
• Window ≤ 24h. Use granularity="hour". Lifecycle judgement gets noisy on intraday windows; flag confidence as 中 or 低.

Don'ts

• Don't speculate beyond the data. If only analyze_topic_trend(trend) was called, do not issue a prediction — say "未运行 predict, 不做未来走势判断".
• Don't over-claim from one data point. A single article does not establish a curve, a stage, or a sentiment split.
• Don't skip sentiment when the user asks about "舆情 / 网友怎么看 / 情绪". Sentiment is the deliverable in that case, not a nice-to-have.
• Don't merge unrelated topics into one report. If the user listed three topics, run this skill three times, or hand off to trend-monitor.
• Don't read article bodies for all related items — only top 3–5. Body reads are expensive and rarely change the report shape past the first few.
• Don't output the report before step 4 (sentiment) finishes unless the user explicitly said "skip sentiment".

See also

• Shared MCP tool reference: [../docs/mcp-tools.md](../docs/mcp-tools.md)
• Decision tree worked examples: [references/decision-tree.md](references/decision-tree.md)
• Multi-topic scanning: trend-monitor skill
• Content generation off the analysis: viral-forge skill
• Packaging the analysis as a deliverable: trend-report skill

Source & license

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

• Author: asoiso
• Source: asoiso/trend-radar
• License: Apache-2.0
• Homepage: https://agentskills.io

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