Family Office AI Skills

Family Office AI Skills

Reusable AI workflows for lean family offices, RIAs, and wealth operators.

An open-source library of structured AI skills, prompts, and workflow templates for single-family offices, multi-family offices, registered investment advisers (RIAs), and the operators who run them: chiefs of staff, investment analysts, executive assistants, and operations teams. Each skill is a repeatable workflow with defined inputs, output structure, and guardrails, not a one-off prompt. Everything is read-only and judgment-supporting: nothing here decides, transacts, or sends.

Important. These materials support human judgment; they do not replace it. They are not investment, legal, tax, accounting, compliance, or cybersecurity advice, and their use creates no professional, advisory, or fiduciary relationship. Every output is an unverified draft for human review. See the Disclaimer before use.

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Contents

• Try this first
• For advanced operators and builders
• Why this exists
• What's inside
• The skills
• Who it's for
• How to use these skills
• Examples in practice
• Playbooks
• Case studies
• Documentation
• Trust and safety posture
• Privacy, security, and governance
• Disclaimer
• Built from operator experience
• Contributing
• License

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Try this first

New here? The Quickstart walks you through running one skill in under five minutes.

• Start with Principal Weekly Brief: the simplest, highest-frequency workflow to see how a skill behaves.
• Use fictional or approved, non-confidential data, never paste confidential information into public or unapproved AI tools.
• Treat the output as a draft for human review: verify every fact against source before relying on it.

For advanced operators and builders

This repository treats AI adoption as an operating-system problem, not a prompt-engineering problem. If you are designing an AI-native family office operating layer (as an RIA COO, chief of staff, or wealthtech builder) start here:

• Action authority model: where assistance ends and action begins, and the controls required before an AI-assisted workflow writes to any system of record.
• Standing context: the conventions, structure, and decisions an office has to write down before any workflow can resolve anything. Skills defer to it constantly; where it is absent they return (missing) rather than guessing.
• Reference architecture: the six-layer model: context, connector governance, skills, review gates, outputs, measurement.
• AI Workforce operating model: what an AI Workforce is, what stays human, and the vocabulary (prompt → skill → workflow → system).
• Skill maturity matrix: how usage matures from a one-off prompt to an operating layer (more structure and review, not more autonomy).
• System blueprints: composed, end-to-end designs (briefing, opportunity review, meeting-to-execution, vendor management).
• Evaluation examples: non-executable red-team scenarios for testing conservative behavior.
• Connector / context-access guide: how approved context enters workflows, as a governed data-access risk.

Why this exists

A lean family office runs on a small team carrying a wide surface area: investments, operations, governance, communications, reporting, vendors, entities, travel, and the principal's time. Headcount rarely scales with complexity. The constraint is almost always attention, not ambition.

An AI-native family office treats AI as an operating layer that absorbs the repeatable, structure-heavy work (drafting briefs, screening deals, summarizing diligence, preparing meetings, extracting action items, digesting documents) so that scarce human judgment is spent where it matters. The goal is operating leverage: a small team preparing more thoroughly and following through more reliably, with a person accountable for every judgment. AI does not reduce the office's risk; used carelessly it adds risk, which is why governance and human review are central to everything here.

These are workflows, not prompts. A prompt is a sentence; a workflow is a contract. Each skill specifies the inputs it expects, the output structure it produces, the behavioral rules it must follow, the quality-control checks it runs against itself, and the things it must never do. That structure makes output more consistent and easier to review: it does not make the output correct, so verification by a competent person remains required. It is also what separates this from generic "prompt engineering."

This repository publishes generalized, sanitized versions of workflows that map to that operating model. They contain no real names, entities, deals, vendors, principals, or data, and are meant to be read, adapted, and run inside your own governed environment.

What's inside

family-office-ai-skills/
├── skills/        Fifteen structured AI workflows (SKILL.md + README + examples)
├── blueprints/    Composed, end-to-end system designs
├── playbooks/     How recurring operational responsibilities are executed
├── case-studies/  Fictional, worked end-to-end examples
├── templates/     Reusable markdown templates for the artifacts they produce
├── evals/         Non-executable red-team scenarios
└── docs/          Governance, privacy, implementation, and operating-model guidance

• skills/: Fifteen workflows. Each contains a SKILL.md (the workflow contract), a README.md (plain-English overview), and an examples/ folder with a fictional sample input and the resulting output. Where a skill's job has a checkable answer, it also ships a test-kit/: fictional documents and an answer key for checking the skill before you rely on it.
• blueprints/: Composed, end-to-end system designs that show how several skills, context sources, and review gates fit together.
• playbooks/: How a lean family office runs its recurring operational responsibilities: the cadence, approvals, accountability, and review gates, and where AI reduces administrative burden without replacing judgment. See the playbooks overview.
• case-studies/: Fictional, sanitized examples that walk one workflow from operating problem to follow-through.
• templates/: Reusable markdown templates for weekly briefs, investment memos, manager diligence, meeting prep, action items, and accounting conventions.
• docs/: Practical guidance for running AI responsibly inside a family office or RIA: governance, privacy, implementation, and the operating model.

The skills

Skill	What it does	Example use case
Principal Weekly Brief	Turns the week's calendar, decisions, and updates into a concise principal briefing	A chief of staff drafts the weekly brief in a consistent format for review
Investment Memo Screener	Converts a deal summary, deck, CIM excerpt, or founder email into a first-pass investment memo	An analyst triages inbound deal flow into a consistent screening format
Manager Diligence Summarizer	Summarizes fund and manager materials into a diligence-ready format	An RIA standardizes manager write-ups across an allocation pipeline
Meeting Prep Pack	Builds a structured meeting brief from prior notes, attendees, and agenda	An EA prepares the principal for a counterparty meeting
Post-Meeting Action Extractor	Converts raw meeting notes into decisions, owners, and deadlines	An operator turns a long meeting into a clean, owned action list
Vendor Review	Evaluates vendor proposals, renewals, and service issues	An ops lead reviews a custodian or technology renewal before approval
Document Digest	Summarizes legal, insurance, trust, or operating documents into plain English	An operator gets a plain-English read of a subscription doc before it goes to counsel
Insurance Renewal Comparison	Compares a renewal against the expiring policy line by line and checks the broker's cover letter against what changed	An operator preparing for a renewal call finds changes on the declarations pages that the cover letter does not mention
Capital Call Notice Check	Checks a capital call notice against the previous notice and the office's record of the fund's terms: arithmetic, dates, fee basis, and every field that changed	An operator finds, before a notice reaches approval, that every payment instruction field changed and the total does not equal its own lines
Contract Renewal Check	Reads a self-renewing service agreement, with every amendment, against the provider's renewal letter and recent invoices: term end, notice deadline, notice method, price, and charges	An office manager finds that an amendment moved the notice deadline a month earlier than the renewal letter says
Entity Compliance Calendar	Builds a twelve-month calendar of one entity's filings, reports, meetings and renewals from its own documents, with every date traced to its clause and its count shown	An operator rebuilding an entity's calendar finds the members' tax information is due months before the accountant plans to send it
Pre-Booking Gap Analysis	Establishes what a ledger already contains before entries are prepared, so the work covers only the genuine gap	A controller picking up a period someone else started finds what is genuinely unbooked
Transfer and Duplicate Review	Finds where one economic event has been represented more than once across accounts	A bookkeeper working feeds across several accounts finds a movement counted twice
Reconciliation Evidence Pack	Assembles reconciliation evidence as two separate assertions, completeness and validity	A controller sees that an account's zero net difference is produced by offsetting errors
Post-Write Verification	Tests whether an approved change actually landed, using evidence read back through a different path	An operator confirms a batch of approved entries reached the ledger instead of trusting the interface that reported success

Who it's for

This library is built for two audiences that rarely share a vocabulary. It is written so both can use it.

Family office and wealth management operators Single-family and multi-family office operators, RIAs, investment analysts, chiefs of staff, executive assistants, and wealth operations teams. If you prepare briefings, screen deals, run diligence, manage vendors, or turn meetings into follow-through, these are the workflows you already do by hand: made repeatable.

AI builders and agent developers Engineers and operators building AI assistants, agents, and internal tooling for wealth and finance. Each SKILL.md is a clean, model-agnostic specification (structured inputs, defined output schema, explicit guardrails) that you can adapt into a skill file, a system prompt, or a step in an agent workflow. The format is compatible with skill-based agent runtimes and works with any capable assistant that follows structured instructions.

How to use these skills

These skills are model-agnostic. They work with any capable AI assistant that can follow a structured instruction set: a general-purpose assistant or an agent runtime that supports skill files.

1. Choose a skill that matches the task in front of you.
2. Open its SKILL.md and read the Inputs, Output, and Do not sections.
3. Gather the inputs the skill asks for. Provide only sanitized material: see privacy guidance.
4. Invoke the skill by giving the AI the instruction set plus your inputs (see each skill's Example request).
5. Review the output as a draft. Every output is a starting point for a human, never a final decision. Verify all facts against source.
6. Adapt the templates in templates/ to match your house style and recordkeeping.

For a full rollout (pilot selection, training, and review loops) see the Implementation Guide.

Examples in practice

Each skill ships with a fictional, end-to-end example so you can see exactly what goes in and what comes out.

• Investment Memo Screener: In: a cold founder email raising a seed round. Out: a structured first-pass memo that separates the promoter's thesis from verified fact, lists specific diligence questions, and flags the missing financials and terms: making no recommendation to invest. → sample input · sample output

• Post-Meeting Action Extractor: In: raw, messy operations-review notes. Out: clean decisions, owners, and deadlines, with every missing owner or date explicitly flagged rather than guessed, plus an internal follow-up draft a person reviews and sends. → sample input · sample output

• Document Digest: In: a dense fund subscription excerpt. Out: a plain-English summary, the key dates and obligations, unusual terms flagged for review, and the precise questions to put to counsel, without giving legal advice. → sample input · sample output

• Insurance Renewal Comparison: In: last year's declarations page, this year's renewal, and the broker's cover letter. Out: every change with both years' terms, whether the letter mentions it, what the documents cannot show, and questions for the broker, without judging coverage. It ships with a test kit: the same fictional renewal with an answer key, so you can check the skill in your own AI tool before relying on it. → sample input · sample output · test kit

• Capital Call Notice Check: In: the office's record of a fund's terms, the previous capital call notice, the new notice, and its cover email. Out: the notice's arithmetic and dates checked with the working shown, each term checked against the record, every field that changed and whether the email mentions it, the payment instructions field by field, and questions for the fund, without verifying anything or deciding whether to pay. It ships with a test kit: the same fictional notice with an answer key. → sample input · sample output · test kit

• Contract Renewal Check: In: a managed IT services agreement, an amendment, the provider's renewal letter, and the latest invoice. Out: the renewal, notice and price terms as amended with each clause quoted, the term's end and the notice deadline with every count shown, each statement in the letter checked against the agreement, each invoice line checked against the fee schedule, and questions for the provider and counsel, without recommending a renewal decision or sending anything. It ships with a test kit: the same fictional renewal with an answer key. → sample input · sample output · test kit

• Entity Compliance Calendar: In: a holding LLC's certificate of formation, operating agreement, two states' notices, the registered agents' letters, the accountant's email and the office's register entry. Out: anything already passed or due soon, a dated twelve-month calendar with each date's source quoted and its count shown, the rules behind the dates, where documents disagree, what the documents do not cover, and questions for counsel and the accountant, without adding obligations from outside the documents or making any filing. It ships with a test kit: the same fictional entity with an answer key. → sample input · sample output · test kit

Browse any skill's examples/ folder for the rest.

Playbooks

The playbooks/ folder documents how a lean family office runs its recurring operational responsibilities (the operating cadence, information flow, approvals, accountability, and review gates) and where AI reduces administrative burden without replacing judgment. Where a skill describes a reusable AI workflow, a playbook describes the standing job that workflow plugs into.

Eleven playbooks cover the work an office repeats: the principal weekly review, capital call processing, investment opportunity intake, board and committee meeting preparation, executive travel coordination, insurance renewal management, vendor onboarding and review, month-end close, quarter-end close, the entity governance calendar, and document review for counsel. Each separates AI assistance from human responsibility, carries explicit review gates, and cross-links the relevant skills, blueprints, case studies, and governance documents.

→ Start with the playbooks overview.

Case studies

The case-studies/ folder shows how the skills, blueprints, review gates, and connector guidance fit together in realistic operating workflows: a weekly principal brief, a first-pass investment opportunity review, an insurance renewal review, capital call processing, and turning ambiguous meeting notes into an execution plan.

They are fictionalized, sanitized examples: invented scenarios with no real names, employers, principals, vendors, investments, or documents: written to show how the skills apply in practice, not to describe any actual client or employer workflow. Each keeps the same read-only, human-in-the-loop, non-advisory posture as the rest of the repository.

→ Start with the case studies overview.

Documentation

Guide	What it covers
Action Authority Model	Where assistance ends and action begins: the five states of an AI-assisted action, the records-versus-resources boundary, and how approval gates fail silently
Standing Context	The conventions, structure, and decisions an office has to write down before a workflow can resolve anything, and the ways that substrate fails
AI Governance for Family Offices	Approved and prohibited use cases, model selection, human review, recordkeeping, staff training, accountability
Privacy and Confidentiality	What must never be pasted into public tools, de-identification, failure modes, a pre-paste checklist
Implementation Guide	A step-by-step rollout for a small office or RIA: pilot selection, training, review loops, measurement
Operating Model	The eight-layer AI-native family office operating model, from information intake to executive decision support
Reference Architecture	The six-layer AI-native family office architecture: context, connector governance, skills, review gates, outputs, measurement
AI Workforce Operating Model	What an "AI Workforce" is and is not, the prompt→skill→system vocabulary, and what stays human
Skill Maturity Matrix	How AI usage matures from a one-off prompt to an operating layer: more structure and review, not more autonomy
Connectors and Context Access	How an optional context-access layer fits, its maturity levels, and an approval checklist: a data-access risk, not a safety control
Threat Model	Practical risks of public family office AI skills, misuse cases, and the human-review model
Evaluation and Red-Team Guide	How to test that a skill behaves conservatively, with example red-team prompts and expected safe responses
Measurement Framework	Honest, directional metrics for adoption and value, with explicit limits
AI Maturity Model	Five levels from no formal AI use to a governed, AI-native family office
Skill Design Principles	The rules every skill in this library follows
Operator Case Study	Sanitized example of measuring an AI-native family office workflow layer (illustrative figures)

System blueprints: composed, end-to-end designs in blueprints/: weekly principal briefing, investment opportunity review, meeting-to-execution, and vendor management.

Evaluation examples: non-executable red-team scenarios in evals/ for testing that a skill behaves conservatively under pressure.

For AI builders: the machine-readable skill catalog and its JSON schema document each skill's conservative metadata. See the Roadmap for direction and the Changelog for history.

Trust and safety posture

The short version of how this library is meant to be used:

• Skills are read-only by default. They draft, summarize, and structure. Where a workflow does write to a system of record, it does so only under the action authority model: a reviewable proposal, a named human approving that specific change, and independent verification that the change landed.
• No skill grants transaction authority. Nothing here approves, executes, allocates, or sends. Money, external communication, contractual commitment, allocation decisions, access and permission changes, and irreversible deletion are never performed by an AI-assisted workflow, with or without approval.
• An unverified change is a failed change. "No error was reported" is not evidence that something worked, and workflows that treat it that way fail quietly.
• Human review is required: every output is an unverified draft for a competent person to verify.
• Guardrails are instructions, not guarantees: a skill's rules are written instructions to the AI, not enforceable controls. Human review is what catches an unsafe output, and review is a control only where something requires it; design is what enforces.
• Connectors are a data-access risk, not a safety control: connectors may improve context, but they should be approved, scoped, and treated as a data-access risk, not a safety control.
• You are responsible for professional review and compliance: these materials are not investment, legal, tax, accounting, compliance, cybersecurity, or fiduciary advice, and create no professional relationship.

See the Threat Model and Evaluation Guide for how this posture is maintained in practice.

Privacy, security, and governance

AI tools introduce real confidentiality and governance risk if used carelessly. Before using anything here:

• Do not paste confidential family or client information into public or consumer AI tools. Use an enterprise environment with appropriate data-handling terms. See Privacy and Confidentiality.
• Keep a human in the loop. These skills support judgment; they do not replace it. Nothing here should approve, commit, transact, or send on its own.
• Govern your usage. Define approved and prohibited use cases, model selection, review requirements, and recordkeeping before you scale. See AI Governance for Family Offices.
• Verify every fact. AI output can be confidently wrong. Treat all figures, dates, names, and claims as unverified until a human confirms them against source documents.
• Guardrails are instructions, not guarantees. The rules inside each skill (its Do not list and quality-control checks) are written instructions to the AI, not technical controls. A model may not follow them perfectly. Human review, not the prompt, is what keeps an unsafe output from becoming an unsafe action. Review is itself a control only where something requires it, because a gate nobody is obliged to pass is one more instruction. What enforces is design: access never granted, outputs that are drafts by construction, and a step the workflow cannot complete without.

See SECURITY.md for how to report a concern.

Disclaimer

This repository is provided for general informational and operational purposes only. It is not, and must not be relied upon as, investment, legal, tax, accounting, compliance, or cybersecurity advice. The skills and templates do not constitute a recommendation to make, hold, or dispose of any investment, or to take any other action.

AI-generated output can be inaccurate, incomplete, or misleading. You are responsible for independently verifying all output and for obtaining advice from qualified professionals before acting. Use of anything in this repository is entirely at your own risk, and you are responsible for your own professional review, supervision, and regulatory compliance. Use of these materials creates no attorney-client, adviser, fiduciary, or other professional relationship, and nothing here is tailored to your specific circumstances. See LICENSE for the full disclaimer of warranties and limitation of liability.

Built from operator experience

Created by Greg Jones from generalized operator experience. The repository contains sanitized workflow patterns and fictional examples. It is an independent public project and is not affiliated with, endorsed by, or representative of any employer, client, principal, family office, investment adviser, or other third party.

The workflows describe general patterns for lean family office and wealth operations: briefings, screening, diligence, meeting prep, follow-through, and document digestion. They are shared so other operators can adapt the structure, not to prescribe any particular decision or to publish any private operating system.

The reasoning behind these patterns (why a control is placed where it is, and where the machine is made to stop) is written up in longer form at gregjones.io

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