How the news-pulse Skill Performs Rapid News Attribution

The news-pulse skill achieves rapid news attribution by orchestrating four parallel scout agents that simultaneously scan company events, regulatory changes, industry peers, and market sentiment, synthesizing the findings into a prioritized attribution report within approximately ten minutes.

The news-pulse skill is a core component of the xbtlin/ai-berkshire repository designed to explain stock price movements through evidence-based investigation. Unlike traditional sequential research workflows, this skill employs a multi-agent architecture defined in [skills/news-pulse.md](https://github.com/xbtlin/ai-berkshire/blob/main/skills/news-pulse.md) that executes four orthogonal investigations concurrently, delivering a concise attribution analysis significantly faster than manual methods.

Parallel Agent Architecture

Dynamic Team Creation

The workflow begins with a TeamCreate operation (lines 44-48) that establishes a dedicated team named {ticker}-newspulse to own the entire attribution process. This temporary team structure ensures isolated resource management and clean termination via TeamDelete (lines 22-23) after report generation. According to the source code, this pattern allows the skill to spin up specialized computational resources on demand without leaving persistent infrastructure.

The Four Scout Agents

Once the team is established, the system launches four independent tasks using TaskCreate operations defined at lines 50-94. Each task runs in the background (run_in_background: true) and assigns a general-purpose agent to a specific domain:

• Company-event scout: Investigates corporate announcements, earnings reports, and management changes
• Regulatory-watcher: Monitors new regulations, fines, licensing changes, and government actions
• Industry-peer tracker: Analyzes competitor movements and sector-wide trends
• Sentiment-tracker: Assesses market narrative through social media and analyst opinions, optionally utilizing [tools/xueqiu_scraper.py](https://github.com/xbtlin/ai-berkshire/blob/main/tools/xueqiu_scraper.py) for Chinese "大 V" opinions

Agents communicate partial results back to the team lead through TaskUpdate and SendMessage operations (lines 44-46), enabling real-time aggregation without blocking the pipeline.

Rapid Attribution Mechanisms

Information-Availability Grading

The skill implements an information-availability grading system (lines 34-41) that assigns A, B, or C grades based on data richness for the target company. For C-grade companies with scarce data, the workflow switches to a "scan-only" mode at line 40, instantly concluding that no explanatory news is available rather than wasting resources on deep searches. This early-exit strategy ensures the system maintains its ~10-minute execution target even when facing data-poor scenarios.

Dual-Source Verification and Parallel Fetching

Each agent executes a dual-source verification rule requiring at least two independent sources to confirm rumors before triggering resource-intensive operations. Only events surviving this filter initiate a WebFetch of the original announcement, keeping I/O to a minimum. Agents prepend date windows and "latest" keywords to their WebSearch queries (line 30), biasing results toward fresh content and reducing noise from historical data.

Structured Synthesis and Persistence

The team lead merges the four dimensional reports using logic defined at lines 55-89, ranking events by relevance without requiring additional parsing. Agents return compact tables (date, event, source, relevance) that feed directly into the final synthesis. The completed report is written to reports/{company}/{company}-news-{YYYYMMDD}.md (lines 17-22) before the temporary team is dismantled.

Implementation Details and Code Examples

CLI Invocation

The skill is invoked through a CLI dispatcher that parses the company name, time window, and price movement context:

/news-pulse 腾讯 14 天 "跌 12%/3 天"

This command triggers the workflow defined in skills/news-pulse.md, with an alternative interface available at [codex-skills/news-pulse/SKILL.md](https://github.com/xbtlin/ai-berkshire/blob/main/codex-skills/news-pulse/SKILL.md).

Task Creation Configuration

The core parallel execution is established through YAML configuration:

- TaskCreate:
    team_name: "tencent-newspulse"
    subagent_type: "general-purpose"
    run_in_background: true
    name: "company-event-scout"
    subject: "侦察 腾讯 近 14 天的公司本体事件"
    description: |
      1. 官方公告 …
      8. 输出按时间倒序的时间线表格

Agent Prompt Template

Each agent receives a domain-specific prompt that includes the time window, stock movement context, and verification rules:

你是 腾讯 新闻脉搏团队中的"监管与政策"，
负责侦察 监管 与政策 维度的最近 14 天事件。

时间窗口：2026‑04‑01 ~ 2026‑04‑15
股价异动背景：跌 12%/3 天
信息可得性等级：A

请完成任务 #2：regulatory-watcher

具体侦察要求：
1. 行业监管：所在行业的新规、罚款、整改、牌照变化
…
- 使用 WebSearch 进行时效性查询
- 对关键事件使用 WebFetch 精读原始来源
- 传言至少要 2 个独立来源

Final Attribution Output

The synthesis produces a prioritized attribution table:

#### 一句话归因

> 腾讯股价大幅下跌，主要因监管部门对其游戏业务的反垄断处罚（高）+ 市场情绪负面加剧（中）。

#### 完整事件时间线

| 日期       | 维度   | 事件                              | 来源 | 异动归因权重 |
|------------|--------|-----------------------------------|------|--------------|
| 2026‑04‑14 | 公司   | 腾讯发布 Q1 财报，营收低于预期   | …    | ⚪ 低 |
| 2026‑04‑13 | 监管   | 国家市场监管局发出《反垄断行政处罚决定书》 | …    | 🔴 高 |
| 2026‑04‑12 | 情绪   | 大V 在雪球上发布“腾讯将被处罚”预测 | …    | 🟡 中 |

Summary

• The news-pulse skill creates a temporary team of four parallel agents via TeamCreate to investigate stock price movements from multiple angles simultaneously.
• Information-availability grading (A/B/C) and early-exit logic for C-grade companies ensure rapid completion even when data is scarce.
• Dual-source verification and selective WebFetch minimize I/O overhead while maintaining accuracy standards.
• Final attribution reports are persisted to reports/{company}/{company}-news-{YYYYMMDD}.md before the team is cleaned up via TeamDelete.

Frequently Asked Questions

How does the news-pulse skill verify the accuracy of news sources?

The skill implements a dual-source verification requirement where agents must confirm rumors through at least two independent sources before initiating a WebFetch operation. This rule, embedded in the agent prompt template at lines 17-33 of skills/news-pulse.md, ensures that only validated information proceeds to the attribution synthesis stage, preventing false positives from single-source speculation.

What happens when the news-pulse skill cannot find relevant news for a company?

When the information-availability grading system assigns a C-grade to a company (indicating scarce data availability), the workflow switches to a "scan-only" mode at line 40 of skills/news-pulse.md. In this mode, the agent instantly concludes that no explanatory news is available rather than executing deep searches, allowing the system to complete within the target timeframe while acknowledging data limitations.

Where does the news-pulse skill store its final attribution reports?

Upon completion, the team lead writes the final markdown report to the path reports/{company}/{company}-news-{YYYYMMDD}.md as specified at lines 17-22 of the skill definition. This standardized naming convention ensures reports are organized by company and date for easy retrieval, after which TeamDelete (lines 22-23) removes the temporary team to free resources.

Which agents are involved in the news-pulse skill's parallel workflow?

The workflow creates four general-purpose agents through TaskCreate operations (lines 50-94): the company-event scout, regulatory-watcher, industry-peer tracker, and sentiment-tracker. Each runs as an independent background task, with the sentiment agent optionally leveraging tools/xueqiu_scraper.py for Chinese social media analysis.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}