MCPInstall

risk-management

by alphaonedevBusiness & Finance

Manages financial risks through quantitative analysis, modeling, and mitigation strategies.

Installs
65
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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/alphaonedev/openclaw-graph --skill risk-management
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads risk-management automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

risk-management

Purpose

This skill enables quantitative analysis, modeling, and mitigation of financial risks. It processes data to calculate metrics like Value at Risk (VaR), stress testing, and suggests strategies to reduce exposure, such as hedging or diversification.

When to Use

Use this skill for scenarios involving financial uncertainty, like portfolio risk assessment, credit risk evaluation, or market volatility analysis. Apply it when you need data-driven insights to comply with regulations (e.g., Basel III) or optimize investment decisions.

Key Capabilities

  • Perform VaR calculations using historical or Monte Carlo simulations.
  • Build risk models for market, credit, or operational risks with inputs like asset prices or default probabilities.
  • Generate mitigation strategies, such as recommending stop-loss levels or portfolio rebalancing based on risk thresholds.
  • Integrate with data sources for real-time analysis, supporting formats like CSV, JSON, or API feeds.
  • Output results in structured formats, including reports or JSON for further processing.

Usage Patterns

Always initialize with authentication via $OPENCLAW_API_KEY. For CLI, pipe data inputs directly; for API, use asynchronous calls for large datasets. Start by loading configuration files (e.g., YAML for model parameters). Common pattern: Analyze risk -> Review outputs -> Apply mitigation. For code integration, import the SDK and wrap calls in try-except blocks. Example 1: Analyze a stock portfolio's market risk by providing historical prices. Example 2: Evaluate credit risk for a loan portfolio and generate mitigation recommendations.

Common Commands/API

Use the OpenClaw CLI for quick tasks or the REST API for programmatic access. Authentication requires setting $OPENCLAW_API_KEY in your environment.

  • CLI Command: openclaw risk analyze --type market --model var --input portfolio.csv --confidence 95
    This calculates 95% VaR for market risk; output is a JSON file with metrics.

  • API Endpoint: POST https://api.openclaw.ai/v1/risk/analyze
    Body: {"type": "credit", "data": {"loans": [{"amount": 100000, "rating": "A"}]}, "model": "default-prob"}
    Response: JSON object with risk score and strategies, e.g., {"var": 5000, "mitigation": ["increase collateral"]}.

  • Code Snippet (Python):

    import openclaw
openclaw.set_key(os.environ['OPENCLAW_API_KEY'])
result = openclaw.risk.analyze(type='operational', data={'events': [100, 200]}, model='monte-carlo')
print(result['mitigation'])

  • Config Format: YAML for custom models, e.g.,

    model:
  type: var
  parameters:
    window: 252  # trading days
    confidence: 0.95

Integration Notes

Integrate by setting $OPENCLAW_API_KEY and using the SDK in your application. For web apps, handle webhooks for asynchronous results (e.g., POST to your endpoint on completion). Connect to data providers like Bloomberg via custom adapters; specify in config: {"data_source": "bloomberg", "api_endpoint": "https://api.bloomberg.com/data"}. Ensure compatibility with other OpenClaw skills by chaining outputs, e.g., pipe risk analysis results into a financial-analysis skill.

Error Handling

Always validate inputs before commands (e.g., check for required fields like --input). For API calls, catch HTTP errors: if status >= 400, retry up to 3 times with exponential backoff. Common errors: 401 (unauthorized – check $OPENCLAW_API_KEY), 400 (bad request – verify JSON schema), or 500 (server error – log and notify). In code, use:

try:
    result = openclaw.risk.analyze(...)
except openclaw.APIError as e:
    if e.status == 401:
        print("Reauthenticate with $OPENCLAW_API_KEY")
    else:
        raise

Log all errors with timestamps and include debug flags, e.g., openclaw risk analyze --debug.

Graph Relationships

  • Related to: financial-analysis (shares finance tag for combined data processing), portfolio-management (uses risk outputs for optimization).
  • Connected via: quantitative-analysis (common modeling techniques), mitigation-strategies (links to compliance tools).
  • Dependencies: Requires financial cluster skills for data input; provides outputs for decision-making skills.

FAQ

What does the risk-management skill do?

Manages financial risks through quantitative analysis, modeling, and mitigation strategies.

How do I install risk-management?

Run npx skills add https://github.com/alphaonedev/openclaw-graph --skill risk-management in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/risk-management/ (personal) or .claude/skills/risk-management/ (project).

Which AI agents can use risk-management?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is risk-management safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains risk-management?

It is published by alphaonedev in alphaonedev/openclaw-graph. It has 65 recorded installs.

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