Agent skill · Business & Finance

longbridge-stock-research

Comprehensive equity research snapshot — integrates analyst consensus estimates, company fundamentals (revenue / profit / valuation), 60-day price history, and recent major news to produce an investment research snapshot similar to a sell-side equity research brief. Triggers: "股票研究", "个股分析", "研究报告", "个股快照", "综合分析", "股票调研", "股票深度", "個股分析", "研究報告", "個股快照", "綜合分析", "股票研究", "stock research", "equity research", "stock analysis", "research snapshot", "investment brief", "stock deep dive", "comprehensive analysis", "NVDA research", "700.HK analysis".

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill longbridge-stock-research --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 4 KB
Bundled scripts: none
Version: 1.0.0
Declared author: longbridge
Path: skills/analysis/longbridge-stock-research/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# longbridge-stock-research Generates a concise equity research snapshot for a single stock by aggregating analyst consensus, key financials, valuation, 60-day price performance, and recent news — structured like a sell-side research brief. > **Response language**: match the user's input language — Simplified Chinese / Traditional Chinese / English. ## When to use Trigger when the user asks for a holistic view of a stock combining multiple data sources: - *"给我做一个 NVDA 的研究报告"* / *"幫我分析一下 700.HK"* / *"Do a research brief on TSLA"* - *"个股快照"*, *"综合分析 AAPL"*, *"stock deep dive into ARM"* For single-datatype queries (price only, valuation only, news only), prefer the dedicated skill instead. ## Workflow 1. Parse the symbol and normalise to `<CODE>.<MARKET>` (see symbol format below). 2. Run all five CLI commands (parallel is fine): - Analyst consensus estimates - Income statement (latest 4 quarters or annual) - Valuation snapshot - Recent news (latest 10 items) - 60-day daily candlestick 3. Synthesise into a structured research brief (see Output section). 4. Cite data source as **Longbridge Securities** / **数据来源:长桥证券** / **數據來源:長橋證券**. ## Symbol format `<CODE>.<MARKET>` — e.g. `NVDA.US`

What's inside
Steps it walks through
  1. When to use
  2. Workflow
  3. Symbol format
  4. CLI
  5. Output
  6. Error handling
  7. Related skills
  8. File layout
Ships with 1 file
  • metadata.json
Commands it runs
Analyst consensus (target price, EPS estimates, rating distribution)
longbridge consensus <SYMBOL> --format json
Income statement (revenue, net income, EPS — quarterly)
longbridge financial-report <SYMBOL> --kind IS --format json
Valuation snapshot (PE / PB / PS / EV-EBITDA)
longbridge valuation <SYMBOL> --format json
Recent news (latest 10 items)
longbridge news <SYMBOL> --format json
longbridge kline <SYMBOL> --period day --count 60 --format json
More from claude-skill-registry
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About this skill
What does the longbridge-stock-research skill do?

Comprehensive equity research snapshot — integrates analyst consensus estimates, company fundamentals (revenue / profit / valuation), 60-day price history, and recent major news to produce an investment research snapshot similar to a sell-side equity research brief. Triggers: "股票研究", "个股分析", "研究报告", "个股快照", "综合分析", "股票调研", "股票深度", "個股分析", "研究報告", "個股快照", "綜合分析", "股票研究", "stock research", "equity research", "stock analysis", "research snapshot", "investment brief", "stock deep dive", "comprehensive analysis", "NVDA research", "700.HK analysis".

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill longbridge-stock-research --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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