# Trend Deep Dive

> Deep-dive analysis of a single topic — heat curve, lifecycle stage, viral anomaly check, near-future prediction, related events, and sentiment evolution via the trendradar MCP. Use when the user asks 深挖XX / 这个话题怎么演变的 / XX 的趋势 / 生命周期 / XX 会不会火 / 异常热度 / 舆情演化 / 把 XX 说清楚 / predict X 的走势 / deep dive on X.

- **Type:** Skill
- **Install:** `agentstack add skill-asoiso-trend-radar-trend-deep-dive`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [asoiso](https://agentstack.voostack.com/s/asoiso)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [asoiso](https://github.com/asoiso)
- **Source:** https://github.com/asoiso/trend-radar/tree/main/trend-deep-dive
- **Website:** https://agentskills.io

## Install

```sh
agentstack add skill-asoiso-trend-radar-trend-deep-dive
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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.

2. **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.

3. **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.

4. **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.

5. **(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=
   )
   ```

6. **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.

```markdown
## 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](https://github.com/asoiso)
- **Source:** [asoiso/trend-radar](https://github.com/asoiso/trend-radar)
- **License:** Apache-2.0
- **Homepage:** https://agentskills.io

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

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-asoiso-trend-radar-trend-deep-dive
- Seller: https://agentstack.voostack.com/s/asoiso
- Browse the marketplace: https://agentstack.voostack.com/browse

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