finance/private-equity/dd-checklist/SKILL.md
# Due Diligence Checklist description: Generate and track comprehensive due diligence checklists tailored to the target company's sector, deal type, and complexity. Covers all major workstreams with request lists, status tracking, and red flag escalation. Use when kicking off diligence, organizing a data room review, or tracking outstanding items. Triggers on "dd checklist", "due diligence tracker", "diligence request list", "what do we still need", or "data room review". ## Workflow ### Step
npx skillsauth add harsh040506/claude-code-unified-skill-plugin-library finance/private-equity/dd-checklistInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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description: Generate and track comprehensive due diligence checklists tailored to the target company's sector, deal type, and complexity. Covers all major workstreams with request lists, status tracking, and red flag escalation. Use when kicking off diligence, organizing a data room review, or tracking outstanding items. Triggers on "dd checklist", "due diligence tracker", "diligence request list", "what do we still need", or "data room review".
Ask the user for:
Generate a checklist across all major workstreams, tailored to the sector:
Financial Due Diligence
Commercial Due Diligence
Legal Due Diligence
Operational Due Diligence
HR / People Due Diligence
IT / Technology Due Diligence (for tech-enabled businesses)
Environmental / ESG (where applicable)
For each item, track:
| Item | Workstream | Priority | Status | Owner | Notes | |------|-----------|----------|--------|-------|-------| | QoE report | Financial | P0 | Pending | | | | Customer interviews | Commercial | P0 | In Progress | | 3 of 10 complete |
Status options: Not Started → Requested → Received → In Review → Complete → Red Flag
Maintain a running list of red flags discovered during diligence:
Automatically add relevant items based on sector:
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.