# How the News-Pulse Skill Attributes Stock Price Movements to Specific Events in AI-Berkshire

> Discover how the news-pulse skill in AI-Berkshire links stock price shifts to specific events by analyzing corporate, regulatory, industry, and sentiment data for accurate attribution.

- Repository: [Xbt Lin/ai-berkshire](https://github.com/xbtlin/ai-berkshire)
- Tags: how-to-guide
- Published: 2026-07-28

---

**The `news-pulse` skill uses a deterministic multi-agent workflow that aggregates evidence from four orthogonal research dimensions—corporate events, regulatory changes, industry dynamics, and market sentiment—to produce a weighted, confidence-rated attribution of observed price movements.**

The `news-pulse` skill in the `xbtlin/ai-berkshire` repository implements a structured approach to financial forensics. By deploying specialized sub-agents that investigate distinct causal factors, the system transforms raw price volatility into actionable, evidence-based explanations grounded in source code logic defined in [`skills/news-pulse.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md).

## Workflow Architecture: From Price Drop to Attribution Report

The attribution process follows a strict three-stage pipeline that ensures comprehensive coverage while maintaining analytical rigor.

### Parameter Intake and Information Grading

The skill receives the company name, optional price-movement description, time window, and focus dimension upon invocation ([source L21-L28](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L21-L28)). Before launching investigations, the system computes an **information-availability grading** (A/B/C rating) that modulates search aggressiveness for each sub-agent ([source L34-L41](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L34-L41)).

This grading ensures that agents adjust their search depth based on data richness, preventing over-confidence in sparse information environments.

### Team Creation and Parallel Processing

A temporary team (`{公司名}-newspulse`) is instantiated to manage the investigation ([source L46-L53](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L46-L53)). Four parallel tasks are spawned simultaneously, with each agent operating under a strict **output schema** requiring core findings, timeline tables, attribution confidence scores, and data-gap statements ([source L35-L42](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L35-L42)).

## The Four Research Dimensions

Each sub-agent investigates a distinct causal layer, ensuring no single narrative dominates the attribution.

### Company-Level Corporate Events (company-event-scout)

The **company-event-scout** agent examines official announcements, earnings releases, M&A activity, and litigation events ([source L54-L66](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L54-L66)). Output includes dates, event descriptions, sources, and relevance ratings (high/medium/low).

### Regulatory and Policy Changes (regulatory-watcher)

The **regulatory-watcher** agent monitors industry-specific rules, cross-border policy shifts, tax modifications, and antitrust actions ([source L67-L78](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L67-L78)). It categorizes impacts as direct or indirect and assigns temporal relevance scores.

### Industry and Competitor Dynamics (industry-peer-analyst)

The **industry-peer-analyst** evaluates peer earnings, supply-chain disruptions, and beta-wave assessments across the competitive landscape ([source L80-L90](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L80-L90)). This agent captures sector-wide movements that might explain systematic price drift.

### Market Sentiment and Capital Flow (sentiment-tracker)

The **sentiment-tracker** aggregates analyst rating changes, 13F institutional holdings updates, short-interest spikes, and key opinion leader commentary (e.g., posts from influential investors like 段永ping) ([source L92-L104](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L92-L104)). It outputs sentiment polarity, source attribution, and confidence metrics.

All four agents execute in parallel background processes, typically completing within approximately ten minutes ([source L107-L113](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L107-L113)).

## Aggregation Logic: How Attribution Is Calculated

When sub-agents return their findings, the **team-lead** agent executes a deterministic merging process defined in [`skills/news-pulse.md`](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md) ([source L154-L176](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L154-L176)).

### Weighted Timeline Construction

The lead agent synthesizes a merged chronological table that includes a **weight column** (🔴 high, 🟡 medium, ⚪ low) indicating how strongly each event explains the price swing ([source L166-L174](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L166-L174)). These weights derive directly from the relevance annotations supplied by each sub-agent (e.g., “与股价异动相关性：高/中/低” in the company-event task ([source L64-L66](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L64-L66))).

### Evidence Matrix and Confidence Scoring

The final report contains an **evidence matrix** with rows for candidate explanations, supporting evidence, counter-evidence, confidence scores, and persistence estimates ([source L175-L181](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L175-L181)). This structure forces explicit consideration of alternative hypotheses.

### Nature Classification

The team-lead explicitly classifies the movement nature by selecting one of four categories: **价值事件** (value event), **情绪/技术波动** (sentiment/technical fluctuation), **真因不明** (cause unknown), or **混合** (mixed) ([source L184-L188](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L184-L188)). If no event reaches a high relevance threshold, the skill automatically flags the case as “真因不明” and recommends continued monitoring.

## Practical Usage and Output Format

### Invoking the Skill

Users trigger the attribution workflow via slash command or API call:

```bash

# Invoke the skill with specific parameters

/pulse-news 公司名="拼多多" 时间窗口=14 股价异动="跌 12%/3天"

```

The price-movement description is injected into each agent’s prompt to contextualize relevance scoring ([source L121-L122](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L121-L122)).

### Understanding the Attribution Report

Sub-agents return structured JSON-style outputs:

```json
{
  "timeline": [
    {"date":"2026-04-28","event":"公司发布 2025 Q4 财报，净利润下滑 8%","source":"https://example.com/notice","relevance":"高"},
    {"date":"2026-04-27","event":"监管发布新政策限制电子商务平台促销","source":"https://gov.cn/policy","relevance":"中"}
  ],
  "confidence":"0.85",
  "data_gap":"未找到关于供应链成本的公开信息"
}

```

The final aggregated attribution produces a weighted timeline:

```markdown
| 日期       | 维度       | 事件                                 | 来源                         | 异动归因权重 |
|------------|------------|--------------------------------------|------------------------------|--------------|
| 2026-04-28 | 公司       | 财报利润下滑 8%                      | https://example.com/notice   | 🔴 高        |
| 2026-04-27 | 监管       | 新政策限制促销活动                  | https://gov.cn/policy        | 🟡 中        |
| 2026-04-26 | 情绪       | 段永平在雪球发帖质疑业绩前景       | https://xueqiu.com/post/123  | ⚪ 低        |

```

### Persistence and Cleanup

Final reports are persisted under `reports/{公司名}/{公司名}-news-{YYYYMMDD}.md` ([source L177-L180](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L177-L180)). After delivery, the temporary team is deleted to free computational resources ([source L221-L223](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L221-L223)).

## Summary

- The `news-pulse` skill implements a **multi-agent workflow** that investigates four orthogonal dimensions: corporate events, regulatory changes, industry dynamics, and market sentiment.
- Each sub-agent assigns **relevance ratings** (high/medium/low) to discovered events, which the team-lead converts into weighted attribution scores (🔴🟡⚪).
- The system outputs a **merged timeline**, **evidence matrix**, and explicit **nature classification** (value event, sentiment/technical, unknown, or mixed).
- If evidence is insufficient, the skill explicitly flags **"真因不明"** (cause unknown) rather than forcing false attribution.
- All analysis is logged to timestamped markdown files in the `reports/` directory before the temporary agent team is cleaned up.

## Frequently Asked Questions

### How does the news-pulse skill determine which events matter most?

The skill weights events based on **relevance annotations** provided by each sub-agent during the parallel investigation phase. Each agent tags findings with high, medium, or low relevance relative to the specific price movement described. The team-lead agent tallies these tags across all four dimensions and assigns final weights (🔴 high, 🟡 medium, ⚪ low) in the merged timeline, ensuring cross-dimensional validation before prioritizing any single explanation.

### What happens when the skill cannot find a clear cause for a price movement?

If no event reaches the high relevance threshold after aggregating all four research dimensions, the team-lead automatically classifies the case as **"真因不明"** (cause unknown) according to the nature checklist logic ([source L186-L188](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L186-L188)). The final report will explicitly state this uncertainty and recommend continued monitoring rather than providing a speculative attribution.

### How long does the news-pulse analysis take to complete?

The four sub-agents execute in parallel background processes, with the complete investigation typically finishing within approximately **10 minutes** ([source L107-L113](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L107-L113)). This parallel architecture ensures comprehensive coverage across corporate, regulatory, industry, and sentiment dimensions without sequential delay.

### Where are the final attribution reports stored?

Completed reports are persisted as markdown files in the repository's file system under the path `reports/{company_name}/{company_name}-news-{YYYYMMDD}.md` ([source L177-L180](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L177-L180)). After storage, the temporary agent team created for that specific analysis is deleted to free resources ([source L221-L223](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md#L221-L223)).