Deal Screening Skill for Claude

Quickly screen inbound deal flow — CIMs, teasers, and broker materials — against the fund's investment criteria. Extracts key deal metrics, runs a pass/fail framework, and outputs a one-page screening memo. Use when reviewing new deal flow, triaging inbound materials, or deciding whether to take a first call. Triggers on "screen this deal", "review this CIM", "should we look at this", "triage this teaser", or "deal screening".

553 tokens

context cost

the whole folder, loaded on every use

files

instructions only

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 deal-screening

The instruction itself

7 sections, as written by the author

Deal Screening

Workflow

Step 1: Extract Deal Facts

From the provided CIM, teaser, or description, extract:

Company: Name, location, sector/subsector
Description: What they do (1-2 sentences)
Financials: Revenue, EBITDA, margins, growth rate
Deal type: Platform, add-on, recap, minority, carve-out
Asking price / valuation: Multiple, enterprise value if stated
Seller motivation: Why selling now
Management: Rolling or exiting
Key customers: Concentration risk
Key risks: Obvious red flags

Step 2: Screen Against Criteria

Apply the fund's investment criteria (ask user if not known):

|-----------|--------|--------|-----------|

Step 3: Quick Assessment

Provide a 3-part assessment:

Verdict: Pass / Further Diligence / Hard Pass
Bull case (2-3 bullets): Why this could be a good deal
Bear case (2-3 bullets): Key risks and concerns
Key questions: What you'd need to answer on a first call

Step 4: Output

One-page screening memo suitable for sharing with partners or an IC quick screen.

Important Notes

Speed matters — screening should take minutes, not hours
Be direct about red flags. Don't bury concerns
If financials seem inconsistent or incomplete, flag it explicitly
Ask for the fund's criteria upfront if this is the first screening
Save screening criteria in memory for future deals once confirmed

Other skills for the same job

different authors, same section of the catalogue

XLSX

by w95

×7

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.

Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.

MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.

Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.

Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.

16k tokens

How to use it

Copy the folder

Take anthropics/deal-screening 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.