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Agent skill

Revenue Product Segmentation

by OctagonAI in OctagonAI/skills

Retrieve detailed revenue breakdown by product segment for public companies.

MITAuto-check passed

Install Revenue Product Segmentation

skills CLI
$ npx skills add OctagonAI/skills --skill revenue-product-segmentation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install OctagonAI/skills revenue-product-segmentation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/OctagonAI/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/revenue-product-segmentation .claude/skills/revenue-product-segmentation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
revenue-product-segmentation
GitHub stars
127
Token cost
~848 tokens
SKILL.md length
318 words
Files
5 (incl. references)
Skills in repo
53
Repo updated
First seen
Licence
MIT

At a glance

Retrieve detailed revenue breakdown by product segment for public companies.

  • Works in 5 steps: Revenue concentration: Calculate each… → Core business identification: Identify… → Growth segments: Note high-growth or… → …
  • Analyzing product mix
  • SKILL.md covers Prerequisites, Query Format, Output Format and Key Insights Pattern, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Revenue Product Segmentation is an agent skill from OctagonAI/skills. Retrieve detailed revenue breakdown by product segment for public companies. Use when analyzing product mix, revenue concentration, segment contribution, or business line performance.

Its SKILL.md is about 850 tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `README.md`, `marketplace.json` and `references/interpreting-results.md`).

It works with Model Context Protocol. The repository describes itself as: A collection of Claude skills for agentic financial research by Octagon. The licence is MIT.

When your agent uses it

  • Analyzing product mix
  • Revenue concentration
  • Segment contribution
  • Business line performance

Example prompts

  • “/revenue-product-segmentation”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Revenue concentration: Calculate each segment as % of total
  2. Core business identification: Identify largest revenue driver
  3. Growth segments: Note high-growth or emerging segments
  4. Diversification assessment: Evaluate revenue balance across segments
  5. Strategic positioning: Understand product portfolio mix

What it can do on your machine

Read from SKILL.md and the folder at commit 51e938c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Revenue Product Segmentation loads about 848 tokens when it runs, and up to ~2.4k if it reads all its reference files. Until then it costs about 53 tokens; SKILL.md has 318 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~848
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from OctagonAI/skills at commit 51e938c, republished under its MIT licence (© OctagonAI). 318 words, ~848 tokens.

Download SKILL.mdSave it as .claude/skills/revenue-product-segmentation/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
revenue-product-segmentation
description
Retrieve detailed revenue breakdown by product segment for public companies. Use when analyzing product mix, revenue concentration, segment contribution, or business line performance.

Revenue Product Segmentation

Retrieve detailed revenue breakdown by product segment for public companies using Octagon MCP.

Prerequisites

Ensure Octagon MCP is configured in your AI agent (Cursor, Claude Desktop, Windsurf, etc.). See references/mcp-setup.md for installation instructions.

Query Format

Retrieve detailed revenue by product segment for <TICKER>, for the annual period with a flat response structure.

MCP Call:

json
{
  "server": "octagon-mcp",
  "toolName": "octagon-agent",
  "arguments": {
    "prompt": "Retrieve detailed revenue by product segment for AAPL, for the annual period with a flat response structure"
  }
}

Output Format

The agent returns a table with revenue by product segment:

Product SegmentRevenue (USD Billion)
iPhone$209.59
Services$109.16
Wearables, Home and Accessories$35.69
Mac$33.71
iPad$28.02
Total$416.17

Data Source: octagon-financials-agent

Key Insights Pattern

After receiving data, generate insights:

  1. Revenue concentration: Calculate each segment as % of total
  2. Core business identification: Identify largest revenue driver
  3. Growth segments: Note high-growth or emerging segments
  4. Diversification assessment: Evaluate revenue balance across segments
  5. Strategic positioning: Understand product portfolio mix

Analysis Tips

Revenue Concentration

Calculate segment share:

Segment Share = Segment Revenue / Total Revenue × 100

Example: $209.59B / $416.17B = 50.36%

Concentration Risk
  • Single segment >50% = high concentration
  • Top 2 segments >80% = moderate concentration
  • No segment >30% = well diversified
Segment Dynamics

Compare to prior periods:

  • Which segments are growing share?
  • Which are declining?
  • Any new segments emerging?
Margin Implications

Different segments often have different margins:

  • Services typically higher margin than hardware
  • Premium products vs commodity segments
  • Recurring vs one-time revenue
Strategic Questions

Based on segmentation:

  • Is the company transitioning its business model?
  • Are growth investments in high-margin segments?
  • How does mix compare to competitors?

Segment Analysis Framework

Hardware vs Services

For tech companies:

  • Hardware: One-time, capital intensive
  • Services: Recurring, higher margin, scalable
Geographic Implications

Segments may have geographic skew:

  • Some products stronger in certain regions
  • Currency exposure by segment
  • Regulatory considerations
Lifecycle Positioning

Segment maturity assessment:

  • Growth phase: High growth, lower margins
  • Mature: Stable, optimized margins
  • Decline: Shrinking, harvesting

Follow-up Queries

Based on results, suggest deeper analysis:

  • "What are the year-over-year growth rates for each product segment?"
  • "How do these revenue figures compare to [COMPANY]'s guidance?"
  • "What are the regional breakdowns for [SEGMENT1] and [SEGMENT2] revenue?"
  • "Compare [COMPANY]'s product segment mix to [PEER1] and [PEER2]"

© OctagonAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references) in skills/revenue-product-segmentation of OctagonAI/skills.

  • SKILL.md
  • README.md
  • marketplace.json
  • references/interpreting-results.md
  • references/mcp-setup.md

Open the folder on GitHubat commit 51e938c

Compare with similar skills

Revenue Product Segmentation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Works with

Questions about Revenue Product Segmentation

What does Revenue Product Segmentation do?

Retrieve detailed revenue breakdown by product segment for public companies. Revenue Product Segmentation is an agent skill from OctagonAI/skills. Retrieve detailed revenue breakdown by product segment for public companies.

When should I use Revenue Product Segmentation?

Revenue Product Segmentation fits situations like: analyzing product mix; revenue concentration; segment contribution; business line performance.

How do I install Revenue Product Segmentation in Claude Code?

Run `npx skills add OctagonAI/skills --skill revenue-product-segmentation -a claude-code`. Or copy the skill folder (skills/revenue-product-segmentation in OctagonAI/skills) into .claude/skills/revenue-product-segmentation in your project. Claude Code loads it when a task matches its description.

How do I install Revenue Product Segmentation in Codex?

Run `npx skills add OctagonAI/skills --skill revenue-product-segmentation -a codex`. Or copy the skill folder (skills/revenue-product-segmentation in OctagonAI/skills) into .agents/skills/revenue-product-segmentation in your project. Codex loads it when a task matches its description.

Can I use Revenue Product Segmentation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add OctagonAI/skills --skill revenue-product-segmentation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/revenue-product-segmentation, .gemini/skills/revenue-product-segmentation, .github/skills/revenue-product-segmentation and .opencode/skills/revenue-product-segmentation in your project.

What does Revenue Product Segmentation need to run?

SKILL.md names no scripts, command-line tools or credentials: Revenue Product Segmentation is instructions for the agent only.

Does Revenue Product Segmentation access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Revenue Product Segmentation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Revenue Product Segmentation use?

Revenue Product Segmentation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Revenue Product Segmentation use?

About 848 tokens (SKILL.md is roughly 3.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.5k tokens, read only when the agent opens those files.

What are the alternatives to Revenue Product Segmentation?

Skills that share tags, products or a category with Revenue Product Segmentation: MCP Server Builder (anthropics/skills, 180k stars), MCP Server Builder (shareAI-lab/learn-claude-code, 78k stars), MCP Integration for Plugins (anthropics/claude-plugins-official, 38k stars) and Figma use_figma Plugin API Rules (warpdotdev/warp, 65k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Revenue Product Segmentation?

OctagonAI (a GitHub organization) maintains it in OctagonAI/skills, which has 127 GitHub stars. The repository holds 53 skills in this directory. The repository was last updated on June 5, 2026.

Source: OctagonAI/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.