Agent skill · Business & Finance

longbridge-fundamental

Company fundamentals via Longbridge — latest financial report KPIs (revenue / net income / EPS / ROE / margins / cash flow), YoY trends, dividend history, forward EPS consensus, analyst ratings, corporate actions. Three depth tiers (snapshot / standard / full). Triggers: "基本面", "业绩", "财报", "财务健康", "盈利能力", "营收", "净利润", "ROE", "毛利率", "分红历史", "EPS 预期", "研报评级", "業績", "財報", "財務健康", "毛利率", "分紅歷史", "fundamentals", "financials", "earnings report", "EPS forecast", "analyst rating", "ROE", "gross margin", "free cash flow", "dividend history", "company report".

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

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

Facts
Files in the skill folder: 2
SKILL.md size: 7 KB
Bundled scripts: none
Version: 1.0.0
Declared author: longbridge
Path: skills/analysis/longbridge-fundamental/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-fundamental Prompt-only analysis skill. Orchestrates Longbridge CLI commands to deliver a five-dimension fundamentals snapshot: profitability, financial health, growth, shareholder return, market expectation. > **Response language**: match the user's input language — Simplified Chinese / Traditional Chinese / English. ## Three depth tiers LLM picks based on prompt verbosity: | Tier | Trigger phrases | Data needed | |---|---|---| | **snapshot** | *"X 怎么样"*, *"how is X"*, brief curiosity | Latest financial report KPIs + analyst estimates + consensus | | **standard** (default) | *"X 基本面 / 业绩 / 财报"*, *"X fundamentals"* | Snapshot + full income/balance/cash-flow statements + dividend history | | **full** | *"X 全面分析"*, *"detailed fundamentals"* | Standard + company profile + operating data + corporate actions + institution ratings (distribution, history, per-institution detail, industry rank) | Tiers are additive — don't pull all 8+ tools when the user asks a casual question. ## When to use - *"贵州茅台 基本面"*, *"NVDA fundamentals"* → standard - *"NVDA 业绩好不好"*, *"how is NVDA's earnings"* → standard, but **never reduce to good/bad** — give numbers - *"AAPL 毛利率"*, *"AAPL gross marg

What's inside
Steps it walks through
  1. Three depth tiers
  2. When to use
  3. CLI
  4. Workflow
  5. Output template (5 sections, mandatory)
  6. Field dictionary
  7. Output constraints
  8. Industry-specific reading
  9. Error handling
  10. MCP fallback
  11. Related skills
  12. File layout
Ships with 1 file
  • metadata.json
Commands it runs
longbridge <subcommand> NVDA.US --format json   # run --help for available flags and subcommand names
More from claude-skill-registry
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About this skill
What does the longbridge-fundamental skill do?

Company fundamentals via Longbridge — latest financial report KPIs (revenue / net income / EPS / ROE / margins / cash flow), YoY trends, dividend history, forward EPS consensus, analyst ratings, corporate actions. Three depth tiers (snapshot / standard / full). Triggers: "基本面", "业绩", "财报", "财务健康", "盈利能力", "营收", "净利润", "ROE", "毛利率", "分红历史", "EPS 预期", "研报评级", "業績", "財報", "財務健康", "毛利率", "分紅歷史", "fundamentals", "financials", "earnings report", "EPS forecast", "analyst rating", "ROE", "gross margin", "free cash flow", "dividend history", "company report".

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill longbridge-fundamental --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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