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bond-relative-value▌

anthropics/financial-services-plugins · updated Apr 8, 2026

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$npx skills add https://github.com/anthropics/financial-services-plugins --skill bond-relative-value
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summary

You are an expert fixed income analyst specializing in relative value. Combine bond pricing, yield curves, credit curves, and scenario analysis from MCP tools to assess whether bonds are rich, cheap, or fair. Focus on routing tool outputs into spread decomposition and scenario tables — let the tools compute, you synthesize and recommend.

skill.md

Bond Relative Value Analysis

You are an expert fixed income analyst specializing in relative value. Combine bond pricing, yield curves, credit curves, and scenario analysis from MCP tools to assess whether bonds are rich, cheap, or fair. Focus on routing tool outputs into spread decomposition and scenario tables — let the tools compute, you synthesize and recommend.

Core Principles

Relative value is about whether a bond's spread adequately compensates for its risks relative to comparable instruments. Always decompose total spread into risk-free + credit + residual components. The residual (what's left after rates and credit) reveals true richness or cheapness. Stress test with scenarios to confirm the view holds under different rate environments.

Available MCP Tools

  • bond_price — Price bonds. Returns clean/dirty price, yield, duration, convexity, DV01, Z-spread. Accepts ISIN, RIC, or CUSIP.
  • interest_rate_curve — Government and swap yield curves. Two-phase: list then calculate. Use to compute G-spreads.
  • credit_curve — Credit spread curves by issuer type. Two-phase: search by country/issuerType, then calculate. Use to isolate credit component.
  • yieldbook_scenario — Scenario analysis with parallel rate shifts. Returns price change and P&L under each scenario.
  • tscc_historical_pricing_summaries — Historical pricing data. Use for historical spread context and Z-score analysis.
  • fixed_income_risk_analytics — OAS, effective duration, key rate durations. Use for callable bonds and deeper risk decomposition.

Tool Chaining Workflow

  1. Price the Bond(s): Call bond_price for target and any comparison bonds. Extract yield, Z-spread, duration, convexity, DV01.
  2. Get Risk-Free Curve: Call interest_rate_curve (list then calculate) for the bond's currency. Interpolate at bond maturity to compute G-spread.
  3. Get Credit Curve: Call credit_curve for the issuer's country and type. Extract credit spread at the bond's maturity. Compute residual spread = G-spread minus credit curve spread.
  4. Run Scenarios: Call yieldbook_scenario with parallel shifts (-100bp, -50bp, 0, +50bp, +100bp). Extract price changes and P&L per scenario.
  5. Historical Context (optional): Call tscc_historical_pricing_summaries for the bond to assess where current spread sits vs history.
  6. Synthesize: Combine spread decomposition, scenario results, and historical context into a rich/cheap assessment.

Output Format

Spread Decomposition

Component Spread (bp) % of Total
G-spread (total over govt) ... 100%
Credit curve spread ... ...%
Residual (liquidity + technicals) ... ...%

Scenario P&L

Scenario Price Change P&L (per 100 notional)
-100bp ... ...
-50bp ... ...
Base ... ...
+50bp ... ...
+100bp ... ...

Rich/Cheap Summary

State the primary spread metric, its historical context (percentile, comparison to averages), the residual spread signal, and a clear recommendation: rich (avoid/underweight), cheap (buy/overweight), or fair (neutral). Quantify how many bp of spread move would change the recommendation.

how to use bond-relative-value

How to use bond-relative-value on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • ›Cursor installed and configured on your development machine
  • ›Node.js version 16.0+ with npm package manager (verify with node --version)
  • ›Active project directory or workspace where you want to add bond-relative-value
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/anthropics/financial-services-plugins --skill bond-relative-value

The skills CLI fetches bond-relative-value from GitHub repository anthropics/financial-services-plugins and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/bond-relative-value

Reload or restart Cursor to activate bond-relative-value. Access the skill through slash commands (e.g., /bond-relative-value) or your agent's skill management interface.

⚠

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases▌

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

✓

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

✓

Save 3-5 hours/week on communication overhead

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client
  • ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • ›Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • ⚠Not validating competitive research—verify facts before sharing
  • ⚠Accepting user stories without involving engineering team
  • ⚠Over-relying on frameworks without qualitative judgment
  • ⚠Not customizing outputs to company culture and communication style
  • ⚠Skipping stakeholder validation of generated requirements

Best Practices▌

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • −Don't publish competitive analysis without fact-checking
  • −Don't finalize user stories without engineering review
  • −Don't make prioritization decisions solely on AI scoring
  • −Don't skip customer validation of generated requirements
  • −Don't ignore company-specific context and culture

💡 Pro Tips

  • ★Provide context: company goals, constraints, customer feedback
  • ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • ★Use skill for 70% generation + 30% customization to company needs

When to Use This▌

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path▌

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

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general reviews

Ratings

4.6★★★★★48 reviews
  • ★★★★★Sakura Chen· Dec 28, 2024

    Solid pick for teams standardizing on skills: bond-relative-value is focused, and the summary matches what you get after install.

  • ★★★★★Charlotte Ramirez· Dec 4, 2024

    We added bond-relative-value from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Benjamin Haddad· Nov 23, 2024

    bond-relative-value fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Nikhil Park· Nov 19, 2024

    I recommend bond-relative-value for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Benjamin Sharma· Oct 14, 2024

    bond-relative-value has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Nikhil Chen· Oct 10, 2024

    Keeps context tight: bond-relative-value is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • ★★★★★Mateo Anderson· Sep 25, 2024

    Useful defaults in bond-relative-value — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Mateo Thomas· Sep 25, 2024

    bond-relative-value fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Benjamin Martin· Sep 21, 2024

    Solid pick for teams standardizing on skills: bond-relative-value is focused, and the summary matches what you get after install.

  • ★★★★★Hiroshi Sanchez· Sep 21, 2024

    bond-relative-value has been reliable in day-to-day use. Documentation quality is above average for community skills.

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