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.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionbond-relative-valueExecute the skills CLI command in your project's root directory to begin installation:
Fetches bond-relative-value from anthropics/financial-services-plugins and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate bond-relative-value. Access via /bond-relative-value in your agent's command palette.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
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.
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.bond_price for target and any comparison bonds. Extract yield, Z-spread, duration, convexity, DV01.interest_rate_curve (list then calculate) for the bond's currency. Interpolate at bond maturity to compute G-spread.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.yieldbook_scenario with parallel shifts (-100bp, -50bp, 0, +50bp, +100bp). Extract price changes and P&L per scenario.tscc_historical_pricing_summaries for the bond to assess where current spread sits vs history.| Component | Spread (bp) | % of Total |
|---|---|---|
| G-spread (total over govt) | ... | 100% |
| Credit curve spread | ... | ...% |
| Residual (liquidity + technicals) | ... | ...% |
| Scenario | Price Change | P&L (per 100 notional) |
|---|---|---|
| -100bp | ... | ... |
| -50bp | ... | ... |
| Base | ... | ... |
| +50bp | ... | ... |
| +100bp | ... | ... |
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.
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Solid pick for teams standardizing on skills: bond-relative-value is focused, and the summary matches what you get after install.
We added bond-relative-value from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
bond-relative-value fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend bond-relative-value for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
bond-relative-value has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: bond-relative-value is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in bond-relative-value — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
bond-relative-value fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: bond-relative-value is focused, and the summary matches what you get after install.
bond-relative-value has been reliable in day-to-day use. Documentation quality is above average for community skills.
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