finance/equity-research/morning-note/SKILL.md
# Morning Note description: Draft concise morning meeting notes summarizing overnight developments, trade ideas, and key events for coverage stocks. Designed for the 7am morning meeting format — tight, opinionated, actionable. Triggers on "morning note", "morning meeting", "what happened overnight", "trade idea", "morning call prep", or "daily note". ## Workflow ### Step 1: Overnight Developments Scan for relevant events across coverage universe: **Earnings & Guidance** - Any coverage compa
npx skillsauth add harsh040506/claude-code-unified-skill-plugin-library finance/equity-research/morning-noteInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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description: Draft concise morning meeting notes summarizing overnight developments, trade ideas, and key events for coverage stocks. Designed for the 7am morning meeting format — tight, opinionated, actionable. Triggers on "morning note", "morning meeting", "what happened overnight", "trade idea", "morning call prep", or "daily note".
Scan for relevant events across coverage universe:
Earnings & Guidance
News & Events
Market Context
Keep it tight — a morning note should be readable in 2 minutes:
[Date] Morning Note — [Analyst Name] [Sector Coverage]
Top Call: [Headline — the one thing PMs need to hear]
Overnight/Pre-Market Developments
Key Events Today
Trade Ideas (if any)
If a coverage company reported, provide a quick reaction:
| Metric | Consensus | Actual | Beat/Miss | |--------|-----------|--------|-----------| | Revenue | | | | | EPS | | | | | [Key metric] | | | | | Guidance | | | |
Our Take: 2-3 sentences — is this good or bad for the stock? Does it change our thesis?
Action: Maintain / Upgrade / Downgrade rating? Adjust price target?
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.