mcpbeat Sign in

Bond Relative Value Skill for Claude

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.

910 tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
33987
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/anthropics/financial-services --skill bond-relative-value

The instruction itself

8 sections, as written by the author

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

  • Price the Bond(s): Call bond_price for target and any comparison bonds. Extract yield, Z-spread, duration, convexity, DV01.
  • Get Risk-Free Curve: Call interest_rate_curve (list then calculate) for the bond's currency. Interpolate at bond maturity to compute G-spread.
  • 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.
  • Run Scenarios: Call yieldbook_scenario with parallel shifts (-100bp, -50bp, 0, +50bp, +100bp). Extract price changes and P&L per scenario.
  • Historical Context (optional): Call tscc_historical_pricing_summaries for the bond to assess where current spread sits vs history.
  • 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.

Other skills for the same job

different authors, same section of the catalogue
Viral Instagram Reels
by vyralcontent
×1

Plan, write, and diagnose Instagram Reels that earn cold-audience reach. Use whenever someone wants a reels script or reels hook for a specific Reel, is debugging why a Reel flopped, wants to know if a draft is worth testing with Trial Reels before going public, or needs a reels caption tuned for the post-hashtag instagram algorithm. Built around what Mosseri has publicly named as the signal hierarchy (watch time, sends per reach, likes per reach), the Trial Reels test-then-publish loop, the Original Content Guidelines and 30-day recovery window, the Edits app, and Reels Insights metrics (skip rate, share rate, followers from this post). Covers a Reels-specific reels strategy: send-driving CTAs, originality without watermarks, audio licensing by account type, captions as the primary SEO signal, and the anti-patterns that quietly cap distribution. Pattern-based guidance, not a virality promise.

18k tokens
Brainstorm Experiments New
by phuryn

Design lean startup experiments (pretotypes) for a new product. Creates XYZ hypotheses and suggests low-effort validation methods like landing pages, explainer videos, and pre-orders. Use when validating a new product idea, creating pretotypes, or testing market demand.

634 tokens
Amazon Alexa QA
by browser-act

Amazon Alexa for Shopping Q&A automation: submits questions to Amazon's Alexa/Rufus AI shopping assistant and collects response text; supports optional keyword search context (navigate to search results page before asking for category-specific answers). Use when user mentions Amazon Alexa, Rufus, Amazon shopping assistant, Amazon AI chat, ask Amazon, Amazon Q&A, automate Alexa questions, Rufus chatbot, Amazon assistant automation, collect Alexa responses, bulk question submission to Amazon, keyword search context, category research. Also applies to extracting Amazon product recommendations from conversational AI, automating repeated queries to Amazon's AI shopping feature, collecting Alexa shopping responses at scale, or market research within a specific product category.

4k tokens scripts
AI Ugc Ads
by tech-leads-club

When the user wants to create UGC ad campaigns, recruit UGC creators, generate AI UGC content, or scale with user-generated content. Also use when the user mentions 'UGC,' 'user-generated content,' 'creator ads,' 'Spark Ads,' 'whitelisting,' 'AI UGC,' 'Arcads,' 'Creatify,' 'creator brief,' or 'UGC testing.' This skill covers the UGC growth framework from creator recruitment through AI-powered scaling. Do NOT use for technical implementation, code review, or software architecture.

5k tokens
Input File Skill
by NVIDIA

Parse, modify, validate, and patch simulator input files. Use when working with reservoir simulation input files, testing scenarios, or validating simulation configurations. This implementation supports reference format (.DATA); other simulators use different extensions (e.g., .afi, .DAT). Supports natural language modifications, keyword patching, and syntax validation.

11k tokens scripts
Recon Scope Triage
by elementalsouls

Triage ASM/recon output for ownership before testing — separate the target's real assets from namespace-collision noise. Automated recon keyword-matches on the brand name, so for any target whose name is a common/dictionary word, the output is dominated by assets belonging to UNRELATED same-named companies (repos, cloud buckets, mobile apps, breach corpora, typosquats). Built from an authorized engagement where an ASM report's "Criticals" were overwhelmingly false positives and the combo/repos/mobile/bucket lists were polluted with unrelated same-named orgs. Use at the START of any engagement, immediately on receiving any ASM/recon/OSINT dataset, BEFORE testing anything.

2k tokens
Attribution Reconciler
by aaron-he-zhu

Use when platform-reported conversions disagree with GA4/ecommerce, when you suspect Meta and Google are double-counting the same sales, or for a standing (monthly) reconciliation workbook that de-dups stacked credit against an order-ID truth set, normalizes attribution windows and currency, compares attribution models, and reads incrementality from a geo/holdout test. Not for the point-in-time R2 veto or RQS gate — use ad-account-auditor; not for the ROI/ROAS ratio math itself — use roi-calculator; not for organic dark-social share attribution or GA4 direct-traffic decomposition — use dark-social-attributor. 付费广告归因对账/去重/增量

3k tokens
Conversion Signal QA
by aaron-he-zhu

Use when the user asks to "QA my conversion tracking before launch", "check my UTMs / pixel / event firing", "set up a tracking pre-flight", or "set the dedup rule so Meta and Google stop double-counting"; builds and fixes the measurement plumbing — conversion-event firing, UTM hygiene, cross-platform dedup rules, attribution-window alignment, and offline/iOS-ATT modeled-gap flags — as a pre-flight checklist plus a UTM/event-spec builder. Not for scoring R1/R2 — that is a scored veto in ad-account-auditor; not for account structure — use campaign-architect. 付费广告转化追踪QA/UTM规范/跨平台去重

4k tokens

How to use it

Copy the folder

Take anthropics/bond-relative-value from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.