Agent skill

longbridge-risk-return

Risk-return optimisation for investment portfolios via Longbridge — builds risk-adjusted return-optimal portfolios based on fund size, risk preference (conservative / balanced / aggressive), and investment horizon. Asset allocation across equities / bonds / cash / commodities / alternatives. Evaluates current portfolio efficiency versus the efficient frontier. Triggers: "风险收益优化", "组合效率", "有效前沿", "风险偏好配置", "最优组合", "风险调整收益", "大类资产配置", "投资组合优化", "風險收益優化", "組合效率", "有效前沿", "風險偏好配置", "最優組合", "risk-return optimization", "portfolio efficiency", "efficient frontier", "risk preference", "optimal portfol

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

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

Facts
Files in the skill folder: 2
SKILL.md size: 5 KB
Bundled scripts: none
Version: 1.0.0
Declared author: longbridge
Path: skills/analysis/longbridge-risk-return/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-risk-return Risk-return optimisation — evaluate portfolio efficiency versus the efficient frontier and recommend optimal asset allocation. > **Response language**: match the user's input language — Simplified Chinese / Traditional Chinese / English. ## When to use Trigger on prompts asking for: - Portfolio optimisation — *"帮我优化投资组合"*, *"optimal portfolio"*, *"投资组合优化"* - Efficient frontier analysis — *"有效前沿"*, *"efficient frontier"*, *"组合效率"* - Risk preference-based allocation — *"稳健型配置"*, *"aggressive allocation"*, *"风险偏好配置"* - Risk-adjusted return improvement — *"提高夏普比率"*, *"risk-adjusted return"*, *"大类资产配置"* > Requires Longbridge login with Trade scope for account data. ## Workflow 1. Fetch current portfolio and positions. 2. Ask the user for: - Risk preference: Conservative (低风险) / Balanced (稳健) / Aggressive (进取) - Investment horizon: short (1–2y) / medium (3–5y) / long (5y+) - Any constraints: max single-stock weight, excluded asset classes 3. Fetch 1-year daily return history for each position. 4. Compute: - Current portfolio: expected return, volatility, Sharpe ratio, max drawdown - Correlation matrix of holdings - Efficient frontier points (using simplified mean

What's inside
Steps it walks through
  1. When to use
  2. Workflow
  3. CLI
  4. Output structure
  5. Error handling
  6. MCP fallback
  7. Related skills
  8. File layout
Ships with 1 file
  • metadata.json
Commands it runs
Account portfolio summary
longbridge portfolio --format json
Current positions
longbridge positions --format json
longbridge kline <SYMBOL> --period day --count 252 --format json
FX rates for currency normalisation
longbridge exchange-rate --format json
More from claude-skill-registry
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About this skill
What does the longbridge-risk-return skill do?

Risk-return optimisation for investment portfolios via Longbridge — builds risk-adjusted return-optimal portfolios based on fund size, risk preference (conservative / balanced / aggressive), and investment horizon. Asset allocation across equities / bonds / cash / commodities / alternatives. Evaluates current portfolio efficiency versus the efficient frontier. Triggers: "风险收益优化", "组合效率", "有效前沿", "风险偏好配置", "最优组合", "风险调整收益", "大类资产配置", "投资组合优化", "風險收益優化", "組合效率", "有效前沿", "風險偏好配置", "最優組合", "risk-return optimization", "portfolio efficiency", "efficient frontier", "risk preference", "optimal portfol

How do I install it?

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