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Agent skill

Company News Pulse

by xbtlin in xbtlin/ai-berkshire

Explains a sudden stock price move by scouting company events, regulation, industry rivals and market sentiment in parallel, then judges the main cause and whether to revisit the thesis.

MITAuto-check passedBusiness, Finance & HR

SKILL.md written in Chinese; this summary is our English description.

Install Company News Pulse

skills CLI
$ npx skills add xbtlin/ai-berkshire --skill news-pulse -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install xbtlin/ai-berkshire news-pulse --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/xbtlin/ai-berkshire.git skills-src && mkdir -p .claude/skills && cp -r skills-src/codex-skills/news-pulse .claude/skills/news-pulse && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
news-pulse
GitHub stars
17k
Token cost
~1.8k tokens
SKILL.md length
574 words
Files
1
Skills in repo
22
Repo updated
First seen
Licence
MIT

At a glance

Explains a sudden stock price move by scouting company events, regulation, industry rivals and market sentiment in parallel, then judges the main cause and whether to revisit the thesis.

  • Works in 8 steps: 一句话归因 → 完整事件时间线（合并 4 个维度） → 异动归因表 → …
  • A watched stock drops or jumps sharply and you want the cause
  • SKILL.md covers Codex adapter note, 适用场景, 执行流程 and 关键原则
  • Calls python3

What it does

This skill is a fast response for a company whose share price has jumped or fallen, not a full research report. It asks for the company, a look-back window (14 days by default, shorter in earnings season), the size and timing of the move, and which of four areas to emphasize, and it asks rather than assumes when something is missing. The target is an answer within ten minutes.

Four parallel agents scout company events, regulatory policy, industry competitors and market sentiment, after the company is graded A, B or C for how much information exists. The output is an event timeline, a judgment of the main driver behind the move and a call on whether the investment thesis needs review. For thinly covered stocks, finding no explanatory news counts as a result in itself, since the move may be technical or flow-driven.

When your agent uses it

  • A watched stock drops or jumps sharply and you want the cause
  • Making sense of a share price reaction after an earnings release
  • Checking whether a news headline is noise or a real signal
  • Deciding whether a move justifies re-reading your investment thesis

Example prompts

  • “Run a news pulse on PDD, looking back 14 days, with the price down 12% over 3 days.”
  • “Run a news pulse on Pop Mart for the last 7 days; the stock fell sharply this week.”
  • “Explain the drop after Tencent's earnings and say whether the thesis needs a review.”

Requirements

  • Web search access for the scouting agents

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. 一句话归因
  2. 完整事件时间线（合并 4 个维度）
  3. 异动归因表
  4. 性质判断（核心结论）
  5. 各维度侦察摘要
  6. 行动建议
  7. 接下来 7-30 天的跟踪清单
  8. 信息缺口声明

What it can do on your machine

Read from SKILL.md and the folder at commit a221a20. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Company News Pulse loads about 1.8k tokens when it runs. Until then it costs about 24 tokens; SKILL.md has 574 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~24
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from xbtlin/ai-berkshire at commit a221a20, republished under its MIT licence (© xbtlin). 574 words, ~1,797 tokens.

Download SKILL.mdSave it as .claude/skills/news-pulse/SKILL.md (or your agent's skills folder).
name
news-pulse
description
公司新闻脉搏：股价异动时快速归因。用 4 个并行 Agent 侦察公司事件/监管政策/行业对手/市场情绪，产出"事件时间线 + 异动主因判断 + 是否触发论文重审"。

Codex adapter note

This skill is generated from skills/news-pulse.md so Claude Code and Codex users share one canonical workflow.

  • Treat $ARGUMENTS as the user's request in the current Codex thread.
  • When the source mentions Claude-only surfaces such as Task, Agent, WebSearch, Bash, Read, or Write, use the closest Codex capability available in this session: subagents when available, web search when needed, shell commands for local tools, and normal file edits for workspace files.
  • Use shared project tools from tools/ in this repository. Prefer running commands from the repository root with paths like python3 tools/financial_rigor.py ...; if the current thread starts outside the repo, locate the actual checkout path first instead of assuming a fixed home-directory path.
  • Before starting research, run the date command to confirm today's date; treat it as the baseline for "latest" data and state the data cutoff date in the report header. Never assume the current date from training data.
  • Preserve the research quality rules from AGENTS.md: cross-check financial data, use exact arithmetic tools for valuation/math, and clearly label uncertainty and source gaps.

公司新闻脉搏：股价异动快速归因团队

对 $ARGUMENTS 进行最近新闻侦察与异动归因。这不是深度投研，是情报快速响应——目标是 10 分钟内回答："这家公司最近发生了什么？股价异动的真因是什么？要不要重审投资论文？"

适用场景

  • 持仓/关注股票股价大涨/大跌（一般触发线：单日 ±5%、一周 ±10%）
  • 财报后股价异动，想快速搞清市场在反应什么
  • 看到新闻标题，但不确定是噪音还是真信号
  • 不适用：完整投研（用 /investment-team）、财报深读（用 /earnings-review）、长期论文跟踪（用 /thesis-tracker）

执行流程

第一步：确认参数与场景

向用户澄清以下信息（如未在 $ARGUMENTS 中提供）：

参数说明默认
公司名中文/英文/股票代码均可必填
时间窗口侦察新闻的回溯天数默认 14 天，财报季可缩到 7 天
股价异动涨/跌幅度 + 时间，如"跌 12%/3 天"选填，有则用于聚焦归因
关注侧重公司事件 / 监管 / 行业 / 情绪默认四方平均

如果用户只说了公司名，先反问："最近多少天的新闻？有具体的股价异动要解释吗？"——不要默默假设。

第二步：信息可得性分级

参考 investment-team.md 的 A/B/C 评级，但维度不同：

等级特征侦察策略
A 级（信息充裕）大盘股、媒体覆盖广、财报季重点是降噪和归因——信息太多反而难找真因，每个 Agent 要有判断力，过滤掉"重复转述"的二手新闻
B 级（信息适中）中小盘、覆盖一般标准模式，每条关键事件附 1-2 个独立信源
C 级（信息稀缺）港股小票、新上市、冷门转入"扫盲模式"——可能找不到任何能解释异动的新闻，这个结论本身就有价值（可能是技术性/资金面而非基本面）

将评级告知每个 Agent，影响其侦察方式。

第三步：创建团队

使用 TeamCreate 创建团队：

  • team_name: {公司名}-newspulse（英文小写，如 pdd-newspulse）
  • agent_type: team-lead
第四步：创建 4 个侦察任务

使用 TaskCreate 创建以下 4 个任务：

任务 1：公司事件侦察（company-event-scout）
  • subject: 侦察 {公司名} 近 {N} 天的公司本体事件
  • description:
    1. 官方公告：港交所/SEC/巨潮 等监管披露平台最近披露
    2. 财报与业绩指引：最新季报/年报、业绩预告、业绩会要点
    3. 管理层动作：高管变动、增减持、回购、分红、股权激励
    4. 重大业务事件：新产品发布、并购重组、业务剥离、大客户/大订单
    5. 资本运作：再融资、可转债、ADR 转换、回 A/退市动议
    6. 诉讼与合规：被起诉、自行披露的合规事件
    7. 每条事件标注：日期 / 来源链接 / 一句话摘要 / 与股价异动可能相关性（高/中/低）
    8. 输出按时间倒序的时间线表格
任务 2：监管与政策（regulatory-watcher）
  • subject: 侦察 {行业/公司} 近 {N} 天的监管与政策变化
  • description:
    1. 行业监管：所在行业的新规、罚款、整改、牌照变化
    2. 跨境政策：中美关系（中概股）、关税、出口管制、数据安全
    3. 税收政策：增值税、企业所得税、个税相关变化
    4. 反垄断与竞争法：调查、罚款、并购否决
    5. 特殊行业政策：医药集采、教育双减、地产三道红线、互联网平台规制等
    6. 货币与外汇：影响该公司的汇率/利率/资本管制变化
    7. 每条政策标注：日期 / 来源 / 直接影响该公司的程度（直接/间接/无关）
    8. 重点判断：是否有"政策黑天鹅"刚刚落地
任务 3：行业与竞争对手（industry-peer-analyst）
  • subject: 侦察 {公司名} 行业格局与对手近 {N} 天的动态
  • description:
    1. 直接对手：列出 3-5 个核心竞争对手，逐个查最近事件（财报、产品、价格战、人事）
    2. 产业链上下游：上游原材料/供应商、下游客户/渠道，最近的价格、产能、订单变化
    3. 行业整体：行业景气度数据、出货量、需求侧信号（消费数据、招标数据等）
    4. 替代品威胁：新技术、新业态对该行业的冲击
    5. 行业指数表现：同行业股票最近表现，该公司是跑赢/跑输/同步
    6. 关键判断：这是公司个体事件，还是整个行业的 beta 波动？
    7. 每条事件标注来源和日期
Show full SKILL.md (223 more words)Show less
任务 4：市场情绪与卖方/大V（sentiment-tracker）
  • subject: 侦察 {公司名} 近 {N} 天的市场情绪与机构观点变化
  • description:
    1. 卖方评级变动：高盛、摩根、中金等最近的评级/目标价调整
    2. 机构持仓变化：13F 披露（美股）、港股通持仓、北上资金流向
    3. 做空数据：做空比例、新发布的做空报告（如有）
    4. 大 V 观点：可调用 python3 tools/xueqiu_scraper.py 抓段永平等大 V 最近相关发言
      • 段永平 user_id: 1247347556
      • 命令示例：python3 tools/xueqiu_scraper.py --user-id 1247347556 --keywords {公司名},{股票代码} --output /tmp/dyp-{公司名}.md
      • 仅在该公司是段永平/李录关注标的时调用，否则跳过节省时间
    5. 传言与小作文：媒体未证实的传言、社交媒体讨论热点（雪球/X/Reddit）
    6. 技术面信号：是否触及关键支撑/阻力、是否有大宗交易、融资融券异常
    7. 关键判断：是基本面驱动还是情绪/资金面驱动？
第五步：并行启动 4 个 Agent

必须在同一条消息中并行调用 4 次 Task 工具。每个 Agent 配置：

  • subagent_type: general-purpose
  • run_in_background: true
  • team_name: {公司名}-newspulse
  • name: 对应角色名（company-event-scout / regulatory-watcher / industry-peer-analyst / sentiment-tracker）

每个 Agent 的 prompt 模板：

你是 {公司名} 新闻脉搏团队中的"{角色中文名}"，负责侦察 {侦察维度} 维度的最近 {N} 天事件。

时间窗口：{起始日期} ~ {今日日期}
股价异动背景：{用户提供的异动信息，无则写"无特定异动，常规体检"}
信息可得性等级：{A/B/C}

请完成任务 #{任务编号}：{任务subject}

具体侦察要求：
{任务description的内容}

**侦察方法**：
- 优先使用 WebSearch 搜索时效性查询（关键词加日期或"最近"、"latest"、"2026"）
- 关键事件用 WebFetch 精读原始来源（公告原文、财报、监管文件）
- 对每个事件做"独立信源验证"——传言至少要 2 个独立来源
- **不要被标题党误导**：标题与正文不符的事件要标注"标题误导"

**输出格式（重要）**：
1. **核心发现**：3-5 条最关键的事件，每条 1-2 句话
2. **完整事件时间线表格**（按日期倒序）：
   | 日期 | 事件 | 来源 | 与股价异动相关性 | 持续性 |
3. **本维度归因结论**：基于侦察到的事件，回答"这个维度能否解释股价异动？置信度多少？"
4. **数据缺口声明**：哪些信息没找到、哪些有疑点、哪些需要等更多信息
5. 严格区分"事实"与"推测"，遵循 CLAUDE.md 客观性原则

**完成后**：
1. 使用 TaskUpdate 将任务标记为 completed
2. 通过 SendMessage 把完整侦察报告发送给 team-lead（type: "message", recipient: "team-lead"）
第六步：实时跟踪进度
  • 每收到一份侦察报告，向用户展示该维度的 3 条核心发现
  • 等待全部 4 份到齐
  • 全部到齐后，通过 SendMessage 向 4 个 Agent 发送 shutdown_request
第七步：team-lead 综合归因

汇总 4 份侦察报告，输出异动归因报告（不是研究报告，重点是"判断"）：

1. 一句话归因

用一段话（30-60 字）说明：这次股价异动的主因 + 次因 + 性质判断（价值事件/情绪波动/不明）

2. 完整事件时间线（合并 4 个维度）

按日期倒序，合并所有维度的事件：

日期维度事件来源异动归因权重
2026-04-30公司XX链接🔴 高
2026-04-29行业XX链接🟡 中
2026-04-28情绪XX链接⚪ 低

权重图例：🔴 高（足以单独解释异动）/ 🟡 中（贡献一部分）/ ⚪ 低（背景噪音）

3. 异动归因表
候选解释证据反证置信度持续性
例：财报 miss营收低于预期 5%、毛利率下滑一次性因素管理层有解释高短期 1-2 周
例：行业 beta同行同期跌 8%该股跌幅明显大于行业中与行业同步
4. 性质判断（核心结论）

打勾其一：

  • 价值事件：基本面发生了真实变化（业绩、护城河、管理层、终局），需要重审投资论文
  • 情绪/技术波动：基本面无变化，是资金面/情绪/Beta 驱动，可视为机会或噪音
  • 真因不明：找不到能匹配股价异动幅度的事件——这是最危险的结论，要么是市场提前知道了什么（内幕/抢跑），要么是我们漏掉了信息源
  • 混合：部分价值事件 + 部分情绪放大
5. 各维度侦察摘要

每维度 3-5 条最重要发现 + 该维度归因贡献度。

6. 行动建议
行动是否建议理由
触发投资论文重审（/thesis-tracker）
触发深度财报研读（/earnings-review）
触发管理层重审（/management-deep-dive）
调仓动作（加仓/减仓/不动）仅做提示，最终决策权在用户
仅观察
7. 接下来 7-30 天的跟踪清单
  • 待披露事件 1（如：5/15 业绩会）
  • 待跟踪指标 2
  • 关键观察信号 3
8. 信息缺口声明

诚实列出本次侦察没能解决的疑点、找不到的信息、需要等更多披露的事项。宁可标"不确定"，也不用推测填满。

第八步：保存报告

写入 reports/{公司名}/{公司名}-news-{YYYYMMDD}.md。如果 reports/{公司名}/ 目录不存在则创建（说明该公司还没建过任何研究报告）。

第九步：清理团队

使用 TeamDelete 清理团队资源。

关键原则

  1. 快胜过全——这个 skill 的核心价值是 10-15 分钟内给出归因判断，不要陷入深度分析（那是其他 skill 的职责）
  2. 归因优先于罗列——发现事件不难，难的是判断"哪个事件配得上这次股价异动"。要做减法，不要做加法
  3. 诚实面对"不明"——找不到主因时，明确写"真因不明"。这比硬凑因果链更有价值（市场可能在抢跑利空消息）
  4. 不预设立场——不要因为持仓就倾向于"这是情绪波动，没事"。证据指向哪边就写哪边
  5. 区分"催化剂"和"巧合"——同一时间发生的事件不一定是异动主因，要看影响量级是否匹配
  6. 尊重信息可得性——C 级公司可能就是查不到任何新闻，这个结论本身就要写出来
  7. 遵循 CLAUDE.md 客观性原则——所有判断附数据来源，区分事实与观点
  8. 不替用户做决策——给出归因和行动建议清单，但买卖决策由用户做

© xbtlin, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in codex-skills/news-pulse of xbtlin/ai-berkshire.

Open the folder on GitHubat commit a221a20

Compare with similar skills

Company News Pulse next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Company News Pulse compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Company News Pulse this skillxbtlin/ai-berkshire17k—~1.8kAutomated safety check: PassMIT
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Eastmoney Market DataHKUDS/Vibe-Trading35k—~1kAutomated safety check: PassMIT
Stock Deep Analysis Workflowwbh604/UZI-Skill7.1k—~9.1kAutomated safety check: NotesMIT
Zhengxi Fund Manager Views Librarylyra81604/zhengxi-views1.8k—~1.6kAutomated safety check: PassMIT
SEC EDGAR Filings FetcherHKUDS/Vibe-Trading35k—~1.4kAutomated safety check: PassMIT

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Categories

Questions about Company News Pulse

What does Company News Pulse do?

Explains a sudden stock price move by scouting company events, regulation, industry rivals and market sentiment in parallel, then judges the main cause and whether to revisit the thesis. This skill is a fast response for a company whose share price has jumped or fallen, not a full research report. It asks for the company, a look-back window (14 days by default, shorter in earnings season), the size and timing of the move, and which of four areas to emphasize, and it asks rather than assumes when something is missing.

When should I use Company News Pulse?

Company News Pulse fits situations like: A watched stock drops or jumps sharply and you want the cause; making sense of a share price reaction after an earnings release; checking whether a news headline is noise or a real signal; deciding whether a move justifies re-reading your investment thesis.

How do I install Company News Pulse in Claude Code?

Run `npx skills add xbtlin/ai-berkshire --skill news-pulse -a claude-code`. Or copy the skill folder (codex-skills/news-pulse in xbtlin/ai-berkshire) into .claude/skills/news-pulse in your project. Claude Code loads it when a task matches its description.

How do I install Company News Pulse in Codex?

Run `npx skills add xbtlin/ai-berkshire --skill news-pulse -a codex`. Or copy the skill folder (codex-skills/news-pulse in xbtlin/ai-berkshire) into .agents/skills/news-pulse in your project. Codex loads it when a task matches its description.

Can I use Company News Pulse in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add xbtlin/ai-berkshire --skill news-pulse -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/news-pulse, .gemini/skills/news-pulse, .github/skills/news-pulse and .opencode/skills/news-pulse in your project.

What does Company News Pulse need to run?

Going by SKILL.md and its folder, Company News Pulse needs the command-line tools its instructions call (python3). Our summary lists: Web search access for the scouting agents.

Does Company News Pulse access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Company News Pulse safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Company News Pulse use?

Company News Pulse is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Company News Pulse use?

About 1.8k tokens (SKILL.md is roughly 7.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Company News Pulse?

Skills that share tags, products or a category with Company News Pulse: AI-Trader Market Intel (HKUDS/AI-Trader, 23k stars), Eastmoney Market Data (HKUDS/Vibe-Trading, 35k stars), Stock Deep Analysis Workflow (wbh604/UZI-Skill, 7.1k stars) and Zhengxi Fund Manager Views Library (lyra81604/zhengxi-views, 1.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Company News Pulse?

xbtlin (a GitHub user) maintains it in xbtlin/ai-berkshire, which has 16,676 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 8, 2026.

Source: xbtlin/ai-berkshire on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.