AI Readiness
AnthropicOfficialPublished by Anthropic
- Skill
- Research and analysis
- Business and productivity
- Apache-2.0
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a systematic approach for evaluating portfolio companies to determine where AI investments will yield the highest EBITDA impact. By ingesting financial and operational data, the agent performs a per-company scan to identify quick wins, ranks these opportunities based on financial impact and speed to value, and highlights cross-portfolio 'replays' to maximize efficiency. It is designed to help operating partners make data-driven decisions during quarterly reviews and annual planning cycles.
What it does
- Ingests and analyzes quarterly updates, financials, and board materials from portfolio companies.
- Evaluates potential AI projects against strict readiness gates including data quality, internal ownership, and pilot feasibility.
- Ranks AI opportunities across the entire portfolio based on projected EBITDA contribution and speed to value.
- Identifies repeatable playbooks that can be deployed across multiple companies to leverage shared learnings.
- Generates a concise summary report for operating partners, including a ranked action list and clear Go/Wait status for each company.
When to use it
- During quarterly portfolio reviews to determine where to focus operating-partner time.
- When conducting annual planning to set AI investment priorities.
- When deciding which portfolio companies are best positioned for immediate AI deployment.
- When looking for cross-company synergies to scale successful AI implementations.
Advantages
- Prioritizes high-impact financial outcomes over speculative technology trends.
- Prevents wasted effort by identifying blockers early through rigorous gate checks.
- Maximizes ROI by identifying repeatable 'replays' across similar portfolio companies.
- Provides a clear, objective rationale for why certain projects are prioritized or deferred.
Usage examples
- “AI readiness scan for the Q3 portfolio data.”
- The agent processes the uploaded board packs and financials, returning a ranked table of the top 5 AI opportunities across the portfolio and a list of recommended cross-company replays.
- “Which portcos are ready for AI?”
- The agent evaluates the current portfolio data against the three readiness gates and provides a list of companies that are 'Go' for immediate pilots versus those that need to 'Wait' due to data or ownership gaps.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/anthropics/ai-readiness/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip ai-readiness.zip -d .agents/skills
Documentation by Anthropic
Portfolio AI Readiness
Workflow
Step 1: Connect to Portfolio Data
First, ask the user where the portfolio materials live. Don't assume — offer the options:
MCP servers — data room, SharePoint, Google Drive, or a portfolio-ops database if one is connected
Local files — a folder path on disk with quarterly decks, financials, board packs
File uploads — drag PDFs, PowerPoint, or Excel directly into the conversation
Once connected, pull quarterly updates, board decks, and financials for the portfolio (or a subset). For each company, extract: sector, revenue, headcount by function, tech stack mentioned, and any AI/automation initiatives already in flight.
If the user provides a single company, still run the scan but skip the cross-portfolio ranking.
Ask up front if not obvious from materials:
Hold period remaining per company (AI payback matters less 12 months from exit)
Whether any portco has already deployed something that worked
Step 2: Per-Company Scan
For each company, answer three gate questions. All three yes → Go. Any no → Wait with a note on what unblocks it.
Is the data there? Can they produce a clean input for the use case — customer list, invoice feed, contract repository — without a 6-month data project first?
Is there an owner? Someone on the management team who will drive this, not a sponsor who will "support" it.
Can we pilot in 30 days? One team, one workflow, off-the-shelf tooling. If the answer starts with "first we'd need to...", it's not a quick win.
Then identify the top 2-3 leverage points. Look for these patterns in the cost structure and operations:
Back Office (usually fastest to pilot)
Invoice processing, AP/AR matching, expense categorization
Contract abstraction — vendor agreements, leases, customer MSAs
Month-end close: reconciliations, flux commentary, lender reporting first drafts
Revenue / Front Office
RFP and proposal first drafts — big lever if revenue is project-based
Sales call summaries and CRM hygiene
Customer support ticket triage and first-response drafting
Quoting for configured / complex products
Operations (sector-dependent)
SOP and quality documentation generation
Scheduling and dispatch (field services, logistics)
Code generation and review (software portcos)
For each leverage point, capture in one line: what it replaces, FTE-hours/week saved (assume 30-50%, not 100%), and whether it's buy-off-the-shelf or needs a light build.
Step 3: Rank Across the Portfolio
Stack every leverage point from every company into one list. Rank by:
Dollar impact — annualized EBITDA contribution (cost out + revenue lift, net of tool cost)
Speed to value — months to first measurable result
Probability — discount for data quality, change management risk, management team capability
Tiebreaker: favor opportunities with <18 months of hold period remaining — those need to move now or not at all.
Output the stack:
| Rank | Company | Opportunity | Est. EBITDA ($) | Months to Value | Gate | First Step |
|---|---|---|---|---|---|---|
| 1 | Go | |||||
| 2 | Go | |||||
| 3 | Wait — [blocker] |
Step 4: Find the Replays
The highest-leverage move in a portfolio is running one successful play at multiple companies. Scan for:
Same sector, same function — two healthcare services portcos with manual prior-auth? One implementation, two deployments.
Same tool, different company — if one portco already has a working invoice-processing setup, flag every other portco with >$Xm in AP volume as a fast follower.
Shared vendor leverage — three portcos buying the same tool is a pricing conversation.
List each replay with the lead company (who proves it) and follower companies (who copy it).
Step 5: Output
One page for the operating partner, structured for a portfolio review:
Top 5 across the portfolio — the ranked table from Step 3, with owner and 30-day first step
Replays — 2-3 playbooks that hit multiple companies at once
Go / Wait by company — one line each; for Waits, what unblocks them
What we're NOT doing — the opportunities that looked good on paper but failed a gate; saves the operating partner from relitigating them every quarter
Aggregate EBITDA contribution — total portfolio-wide AI opportunity, split Year 1 quick wins vs. Years 2-3 scale
Important Notes
Rank by dollars, not excitement. A boring AP automation that saves $400k at a $40m revenue company beats a flashy customer-facing chatbot every time.
The binding constraint is almost always data, not models. If a company can't produce a clean customer list, AI isn't the first project — a data cleanup is. Say so plainly.
Off-the-shelf first. Custom builds are slow, expensive, and fragile for companies without engineering depth. Favor tools they can buy and deploy.
Ownership is the real gate. A quick win with no internal owner dies in 90 days. If no one on the management team wants it, mark it Wait regardless of the dollar size.
Hold period drives urgency. A company 3 years from exit can afford a foundational data project. A company 12 months out needs something that shows up in the LTM EBITDA for the CIM — or skip it.
Failed pilots are signal. If management already tried something and it didn't stick, find out why before proposing the same thing again.
Files
- ATTRIBUTION.mdAdded by AutoGineer922 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md6.0 KiB
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Requirements and compatibility
Written for: Claude Code, Claude apps
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an MCP server
Security
No issues found by our static scan· No issues found
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Attribution and licence
- Published by
- Anthropic
- Publisher
- Anthropic (Official)
- Source
- anthropics/financial-services · plugins/vertical-plugins/private-equity/skills/ai-readiness · commit 6056506
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Anthropic or Anthropic.
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