finance/equity-research/bond-relative-value/SKILL.md
Perform relative value analysis on bonds by combining pricing, yield curve context, credit spreads, and scenario stress testing. Use when analyzing bond richness/cheapness, computing spread decomposition, comparing bonds, assessing bond value vs curves, or running rate shock scenarios.
npx skillsauth add harsh040506/claude-code-unified-skill-plugin-library bond-relative-valueInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
testing
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tools
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
testing
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development
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