Revenue Concentration logo

Revenue Concentration

Organization
openaccountant
revenue-concentration

Assess client concentration risk using revenue share and Herfindahl index.

Overview

Publisheropenaccountant
Repositoryskills
Skill namerevenue-concentration
Stars
106
Forks
18
Bundled files
Instructions only
LicenseMIT
Links
  • 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 openaccountant on GitHub. Read the source before you install it.

Installation

Install the Revenue Concentration 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/openaccountant/skills/tree/main/business/revenue-concentration
  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/openaccountant/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/business/revenue-concentration .claude/skills/revenue-concentration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Revenue Concentration 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 Revenue Concentration 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 Revenue Concentration 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.

Revenue Concentration Risk

Overview

Measure how dependent your business is on a small number of clients. Calculates each client's revenue share, computes the Herfindahl-Hirschman Index (HHI), and flags dangerous concentration levels.

Wilson Tools Used

  • transaction_search — find all revenue transactions and group by client or source to calculate per-client totals

Workflow

  1. Ask for the analysis period (recommend 12 months for accuracy).
  2. Use transaction_search to find all incoming payments (positive amounts).
  3. Group by vendor/payee name to get per-client revenue totals.
  4. Calculate each client's revenue share as a percentage of total.
  5. Compute the Herfindahl-Hirschman Index: HHI = sum of (each client's market share percentage squared).
  6. Generate the report:
REVENUE CONCENTRATION — [Period]
═══════════════════════════════════════════════════
Client              Revenue     Share    Cumulative
───────────────────────────────────────────────────
Acme Corp           $48,000     40.0%      40.0%
Beta LLC            $30,000     25.0%      65.0%
Gamma Inc           $18,000     15.0%      80.0%
Delta Co            $12,000     10.0%      90.0%
Other (3 clients)   $12,000     10.0%     100.0%
───────────────────────────────────────────────────
Total Revenue      $120,000    100.0%

Herfindahl Index (HHI):  2,550
Concentration Level:     HIGH
Top Client Dependency:   40.0%

Risk Assessment:
  - Losing Acme Corp would eliminate 40% of revenue
  - Top 2 clients = 65% of revenue
  - Top 3 clients = 80% of revenue
═══════════════════════════════════════════════════
  1. Interpret HHI:
    • Under 1,500: Low concentration (diversified)
    • 1,500-2,500: Moderate concentration
    • Over 2,500: High concentration (risky)
  2. Flag any single client above 25% as a key-person risk.
  3. Recommend target: no single client above 20%, top 3 clients below 50%.

Without Wilson

  1. Export bank transactions as CSV for the past 12 months.
  2. Filter to income only (positive amounts in most bank exports).
  3. Add a "Client" column and tag each deposit with the source client.
  4. Pivot table: Rows = Client, Values = Sum of Amount. Sort descending.
  5. Revenue Share: =ClientRevenue/TotalRevenue*100.
  6. HHI: In a new column, square each share: =Share^2. Then =SUM(SquaredShares).
  7. For cumulative share, use =SUM($B$2:B2)/TotalRevenue*100 (assuming column B is revenue, sorted descending).
  8. The U.S. DOJ uses HHI for antitrust analysis with the same thresholds: under 1,500 = unconcentrated, 1,500-2,500 = moderate, over 2,500 = highly concentrated. The same logic applies to your revenue risk.

Important Notes

  • A perfectly equal distribution across 4 clients gives an HHI of 2,500 (still moderate). You need 7+ roughly equal clients to reach "low concentration."
  • Revenue concentration is not inherently bad if the top clients are contractually committed (annual contracts, retainers). The risk is losing a major client with no notice.
  • Track this quarterly. If a single client's share is growing, actively invest in diversifying your revenue base.
  • Consider both revenue concentration and profit concentration. A client generating 40% of revenue but 60% of profit is an even bigger risk.

Frequently asked questions

What does the Revenue Concentration AI skill do?

Assess client concentration risk using revenue share and Herfindahl index.

Why use Revenue Concentration on TypingMind?

Because you install it once and use it with any model. Revenue Concentration 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 Revenue Concentration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openaccountant/skills/tree/main/business/revenue-concentration. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Revenue Concentration?

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 Revenue Concentration?

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

Is the Revenue Concentration AI skill free?

Yes. It is published on GitHub by openaccountant under the MIT 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇