Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add leecyno1/boutique-skills --skill anthropic-fs-private-equity-ai-readinessgit clone --depth 1 https://github.com/leecyno1/boutique-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/leecyno1/boutique-skills/anthropic-fs-private-equity-ai-readiness)<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/anthropic-fs-private-equity-ai-readiness"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/anthropic-fs-private-equity-ai-readiness/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/anthropic-fs-private-equity-ai-readiness"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/anthropic-fs-private-equity-ai-readiness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00112 | $0.01380 |
| Opus 5 | $0.00056 | $0.00690 |
| Sonnet 5 | $0.00022 | $0.00276 |
| Haiku 4.5 | $0.00011 | $0.00138 |
Grade A, and why
ai-readiness scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to ai-readiness — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 100 lines · 112 tokens per session scan A b1bed0ae1234
ai-readiness is a skill published in the GitHub repository leecyno1/boutique-skills (5 stars, last pushed today), licensed MIT. It adds 112 tokens to every session and 1,380 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ai-readiness, differing in 0 lines, and is treated as a copy.
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