finance/equity-research/macro-rates-monitor/SKILL.md
Build macroeconomic and rates dashboards combining macro indicators, yield curves, inflation breakevens, and swap rates. Use when monitoring macro conditions, analyzing yield curve shape, decomposing real vs nominal rates, assessing policy rate expectations, or evaluating financial conditions.
npx skillsauth add harsh040506/claude-code-unified-skill-plugin-library macro-rates-monitorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
You are an expert macro strategist and rates analyst. Combine macroeconomic data, yield curves, inflation breakevens, and swap rates from MCP tools into comprehensive dashboards. Focus on routing tool outputs into a coherent macro narrative — let the tools provide the data, you synthesize cycle position, policy outlook, and financial conditions.
Macro analysis synthesizes multiple indicators into a narrative. Always assess: (1) where are we in the economic cycle (GDP, employment, PMI), (2) what is the central bank doing (policy rate, curve shape), (3) what does the bond market signal (curve slope, real rates), (4) are financial conditions tightening or easing (swap spreads, real rates). Start broad, drill down.
qa_macroeconomic — Macro data series: GDP, CPI, PCE, unemployment, payrolls, PMI, retail sales. Multiple countries and frequencies. Search by mnemonic pattern or description.interest_rate_curve — Government yield curves and swap curves. Two-phase: list then calculate. Use for curve shape and slope analysis.inflation_curve — Inflation breakeven curves and real yields. Two-phase: search then calculate. Use for real rate decomposition.ir_swap — Swap rates by tenor and currency. Two-phase: list templates then price. Use to compute swap spreads.tscc_historical_pricing_summaries — Historical pricing data. Use for historical yield context and trend analysis.qa_macroeconomic for GDP, CPI/PCE, unemployment, and PMI for the target country. Retrieve latest values and recent series.interest_rate_curve (list then calculate) for the government curve. Extract yields at standard tenors. Compute 2s10s and 3M-10Y slopes. Classify curve shape.inflation_curve (search then calculate). Compute real rates = nominal minus breakeven at each tenor. Assess whether real rates are accommodative or restrictive.ir_swap (list then price) at 2Y, 5Y, 10Y. Compute swap spread = swap rate minus government yield at each tenor. Assess financial conditions.tscc_historical_pricing_summaries for the benchmark yield (e.g., 10Y). Assess where current yields sit vs recent history.When querying qa_macroeconomic, use wildcard patterns to discover mnemonics:
| Indicator | Current | Prior | Direction | Signal | |-----------|---------|-------|-----------|--------| | GDP Growth | ...% | ...% | ... | Expansion/Contraction | | Core Inflation (YoY) | ...% | ...% | ... | Above/At/Below target | | Unemployment | ...% | ...% | ... | Tight/Balanced/Slack | | PMI Manufacturing | ... | ... | ... | Expansion/Contraction |
Present yields at key tenors (3M, 2Y, 5Y, 10Y, 30Y). Highlight 2s10s and 3M-10Y slopes. Note curve shape: normal / flat / inverted / humped.
| Tenor | Nominal | Breakeven | Real Rate | Signal | |-------|---------|-----------|-----------|--------| | 5Y | ...% | ...% | ...% | Accommodative/Restrictive | | 10Y | ...% | ...% | ...% | Accommodative/Restrictive |
| Tenor | Swap Rate | Govt Yield | Swap Spread (bp) | Signal | |-------|-----------|------------|-------------------|--------| | 2Y | ... | ... | ... | Normal/Elevated/Stressed | | 5Y | ... | ... | ... | Normal/Elevated/Stressed | | 10Y | ... | ... | ... | Normal/Elevated/Stressed |
2-3 sentences on the macro-rates regime: cycle position, policy outlook, financial conditions, and key risks.
testing
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
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
This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".
development
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.