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Client Review

OrganizationPopular
anthropics
client-review

Prepare for client review meetings with portfolio performance summary, allocation analysis, talking points, and action items. Pulls together account data into a concise meeting-ready format. Use before quarterly reviews, annual checkups, or ad-hoc client meetings. Triggers on "client review", "meeting prep for [client]", "quarterly review", "prep for [client name]", or "client meeting".

Overview

Publisheranthropics
Repositoryfinancial-services
Skill nameclient-review
Stars
38.4K
Forks
5.5K
Bundled files
Instructions only
LicenseApache-2.0
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by anthropics on GitHub. Read the source before you install it.

Installation

Install the Client Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/anthropics/financial-services/tree/main/plugins/agent-plugins/meeting-prep-agent/skills/client-review
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/anthropics/financial-services.git /tmp/financial-services
mkdir -p .claude/skills
cp -r /tmp/financial-services/plugins/agent-plugins/meeting-prep-agent/skills/client-review .claude/skills/client-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Client Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Client Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Client Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Client Review Prep

Workflow

Step 1: Client Context

Gather or look up:

  • Client name and household members
  • Account types: Taxable, IRA, Roth, 401(k), trust, etc.
  • Total AUM across accounts
  • Investment Policy Statement (IPS): Target allocation, risk tolerance, constraints
  • Life stage: Accumulation, pre-retirement, retirement, legacy
  • Last meeting date and any outstanding action items

Step 2: Portfolio Performance

For each account and the household aggregate:

MetricQTDYTD1-Year3-YearSince Inception
Portfolio return
Benchmark return
Alpha

Performance Attribution:

  • Which asset classes / positions drove returns?
  • Top 3 contributors and top 3 detractors
  • Any outsized single-position impact?

Step 3: Allocation Review

Current vs. target allocation:

Asset ClassTargetCurrentDriftAction
US Large Cap
US Mid/Small
International Developed
Emerging Markets
Fixed Income
Alternatives
Cash

Flag any drift exceeding the IPS rebalancing threshold (typically 3-5%).

Step 4: Talking Points

Generate a meeting agenda:

  1. Market overview (2-3 min): Brief macro context and outlook
  2. Portfolio performance (5 min): How did we do? Why?
  3. Allocation review (5 min): Any rebalancing needed?
  4. Planning updates (5-10 min):
    • Life changes? (job, health, family, home, education)
    • Income needs changing?
    • Tax situation updates
    • Estate planning updates
  5. Action items (5 min): What are we doing before next meeting?

Step 5: Proactive Recommendations

Based on the review, suggest:

  • Rebalancing trades (if drift exceeds thresholds)
  • Tax-loss harvesting opportunities
  • Cash deployment or withdrawal planning
  • Roth conversion opportunities (if applicable)
  • Beneficiary updates or estate planning needs
  • Insurance review (life, disability, LTC)

Step 6: Output

  • One-page client review summary (Word or PDF)
  • Performance table with benchmarks
  • Allocation pie chart (current vs. target)
  • Recommended action items
  • Meeting agenda

Important Notes

  • Know your client before the meeting — review notes from last meeting
  • Lead with what the client cares about, not what you want to talk about
  • If performance was bad, address it directly — don't hide or spin
  • Always end with clear action items and next steps with dates
  • Document the meeting notes and any changes to the IPS
  • Compliance: ensure all materials are compliant with firm policies and regulatory requirements

Frequently asked questions

What does the Client Review AI skill do?

Prepare for client review meetings with portfolio performance summary, allocation analysis, talking points, and action items. Pulls together account data into a concise meeting-ready format. Use before quarterly reviews, annual checkups, or ad-hoc client meetings. Triggers on "client review", "meeting prep for [client]", "quarterly review", "prep for [client name]", or "client meeting".

Why use Client Review on TypingMind?

Because you install it once and use it with any model. Client Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Client Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/anthropics/financial-services/tree/main/plugins/agent-plugins/meeting-prep-agent/skills/client-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Client Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Client Review?

As many as you like. As long as a model supports skills, you can use Client Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Client Review AI skill free?

Yes. It is published on GitHub by anthropics under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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