Agent skill · Data & Analytics

longbridge-asset-allocation

Asset allocation and portfolio optimisation via Longbridge — efficient frontier (MPT), Black-Litterman model overview, risk parity / risk budgeting, all-weather strategy, and practical allocation recommendations based on the user's Longbridge account data. Triggers: "资产配置", "组合优化", "有效前沿", "Black-Litterman", "风险预算", "风险平价", "全天候策略", "大类资产", "資產配置", "組合優化", "有效前沿", "風險預算", "風險平價", "全天候策略", "大類資產", "asset allocation", "portfolio optimization", "efficient frontier", "Black-Litterman", "risk parity", "all-weather strategy", "mean-variance optimization", "strategic allocation".

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

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

Facts
Files in the skill folder: 2
SKILL.md size: 6 KB
Bundled scripts: none
Version: 1.0.0
Declared author: longbridge
Path: skills/analysis/longbridge-asset-allocation/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-asset-allocation Prompt-only analysis skill. Explains major asset-allocation frameworks (MPT efficient frontier, Black-Litterman, risk parity, all-weather) and, when the user is logged in, applies them to their actual Longbridge portfolio data. > **Response language**: match the user's input language — Simplified Chinese / Traditional Chinese / English. ## When to use - *"帮我做资产配置分析"* / *"資產配置分析"* / *"help me with asset allocation"* - *"什么是有效前沿"* / *"有效前沿"* / *"explain the efficient frontier"* - *"Black-Litterman 模型怎么用"* / *"Black-Litterman model"* - *"风险平价策略"* / *"風險平價策略"* / *"risk parity strategy"* - *"全天候策略怎么配置"* / *"全天候策略"* / *"all-weather portfolio allocation"* - *"帮我优化组合配置"* / *"optimize my portfolio allocation"* ## Workflow 1. **Framework selection**: identify which allocation approach the user wants (MPT / Black-Litterman / risk parity / all-weather / practical advice). 2. **Account data** (if logged in): fetch current positions and 252-day price history for each holding. 3. **Explain the framework** with the user's actual holdings as context. 4. **Generate suggested target weights** based on the chosen framework. 5. Present the allocation with rationale. ## CLI

What's inside
Steps it walks through
  1. When to use
  2. Workflow
  3. CLI
  4. Framework Reference
  5. MPT (Modern Portfolio Theory)
  6. Black-Litterman
  7. Risk Parity
  8. All-Weather (Bridgewater style)
  9. Output template
  10. Error handling
  11. MCP fallback
  12. Related skills
  13. File layout
Ships with 1 file
  • metadata.json
Commands it runs
Current holdings (if user is logged in)
longbridge portfolio --format json
longbridge positions --format json
longbridge kline <SYMBOL> --period day --count 252 --format json
longbridge calc-index <SYMBOL> --format json
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About this skill
What does the longbridge-asset-allocation skill do?

Asset allocation and portfolio optimisation via Longbridge — efficient frontier (MPT), Black-Litterman model overview, risk parity / risk budgeting, all-weather strategy, and practical allocation recommendations based on the user's Longbridge account data. Triggers: "资产配置", "组合优化", "有效前沿", "Black-Litterman", "风险预算", "风险平价", "全天候策略", "大类资产", "資產配置", "組合優化", "有效前沿", "風險預算", "風險平價", "全天候策略", "大類資產", "asset allocation", "portfolio optimization", "efficient frontier", "Black-Litterman", "risk parity", "all-weather strategy", "mean-variance optimization", "strategic allocation".

How do I install it?

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