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Agent skill

Revenue Geographic Segmentation

by OctagonAI in OctagonAI/skills

Retrieve detailed revenue breakdown by geographic segment for public companies.

MITAuto-check passed

Install Revenue Geographic Segmentation

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

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

GitHub CLI
$ gh skill install OctagonAI/skills revenue-geographic-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-geographic-segmentation .claude/skills/revenue-geographic-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-geographic-segmentation
GitHub stars
127
Token cost
~1k tokens
SKILL.md length
371 words
Files
5 (incl. references)
Skills in repo
53
Repo updated
First seen
Licence
MIT

At a glance

Retrieve detailed revenue breakdown by geographic segment for public companies.

  • Works in 5 steps: Regional concentration: Identify largest… → Growth trends: Track which regions are… → Currency exposure: Assess FX risk by… → …
  • Analyzing regional exposure
  • SKILL.md covers Prerequisites, Query Format, Output Format and Key Observations Pattern, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Revenue Geographic Segmentation is an agent skill from OctagonAI/skills. Retrieve detailed revenue breakdown by geographic segment for public companies. Use when analyzing regional exposure, geographic concentration, international expansion, or currency risk assessment.

Its SKILL.md is about 1k 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 regional exposure
  • Geographic concentration
  • International expansion
  • Currency risk assessment

Example prompts

  • “/revenue-geographic-segmentation”

Workflow steps

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

  1. Regional concentration: Identify largest revenue regions
  2. Growth trends: Track which regions are growing fastest
  3. Currency exposure: Assess FX risk by region
  4. Emerging markets: Monitor developing region growth
  5. Historical evolution: Track geographic mix changes over time

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 Geographic Segmentation loads about 1k tokens when it runs, and up to ~2.6k if it reads all its reference files. Until then it costs about 57 tokens; SKILL.md has 371 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~57
When it runs · the whole SKILL.md, loaded when a task matches
~1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.6k

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). 371 words, ~1,031 tokens.

Download SKILL.mdSave it as .claude/skills/revenue-geographic-segmentation/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
revenue-geographic-segmentation
description
Retrieve detailed revenue breakdown by geographic segment for public companies. Use when analyzing regional exposure, geographic concentration, international expansion, or currency risk assessment.

Revenue Geographic Segmentation

Retrieve detailed revenue breakdown by geographic 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 geographic 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 geographic segment for AAPL, for the annual period with a flat response structure"
  }
}

Output Format

The agent returns a table with revenue by geographic segment across years:

Fiscal YearAmericas SegmentEurope SegmentGreater China SegmentJapan SegmentRest of Asia Pacific Segment
2025$178,353.00M$111,032.00M$64,377.00M$28,703.00M$33,696.00M
2024$167,045.00M$101,328.00M$66,952.00M$25,052.00M$30,658.00M
2023$162,560.00M$94,294.00M$72,559.00M$24,257.00M$29,615.00M
2022$169,658.00M$95,118.00M$74,200.00M$25,977.00M$29,375.00M
2021$153,306.00M$89,307.00M$68,366.00M$28,482.00M$26,356.00M

Data Source: octagon-financials-agent

Key Observations Pattern

After receiving data, generate observations:

  1. Regional concentration: Identify largest revenue regions
  2. Growth trends: Track which regions are growing fastest
  3. Currency exposure: Assess FX risk by region
  4. Emerging markets: Monitor developing region growth
  5. Historical evolution: Track geographic mix changes over time

Analysis Tips

Regional Share Calculation
Region Share = Region Revenue / Total Revenue × 100

Calculate for each region to understand geographic mix.

Geographic Concentration
  • Americas >50% = US-centric
  • Single region >60% = high concentration
  • Well balanced = no region >40%
Growth Rate by Region
Region Growth = (Current Year - Prior Year) / Prior Year × 100

Identify fastest and slowest growing regions.

Currency Implications

Regional exposure implies currency risk:

  • Americas: USD (base currency typically)
  • Europe: EUR, GBP exposure
  • Greater China: CNY exposure
  • Japan: JPY exposure
  • Rest of Asia Pacific: Mixed currencies
Show full SKILL.md (153 more words)Show less
Geopolitical Risk

Consider regional risks:

  • Trade tensions (US-China)
  • Regulatory environment
  • Economic cycles
  • Political stability

Strategic Analysis

International Expansion

Track over time:

  • Is international share growing?
  • Which regions showing momentum?
  • New market entries?
Market Penetration

Compare to:

  • Regional GDP or population
  • Addressable market size
  • Competitor regional presence
Diversification Benefits

Balanced geographic mix provides:

  • Currency hedging (natural)
  • Economic cycle diversification
  • Regulatory risk distribution

Segment Evolution

Observe over 10+ years:

  • Americas: Typically stable, large base
  • Europe: Steady growth
  • Greater China: Rapid expansion then maturation
  • Emerging Asia: High growth potential
Inflection Points

Note significant changes:

  • New market entries
  • Trade policy impacts
  • Pandemic effects
  • Currency devaluations

Follow-up Queries

Based on results, suggest deeper analysis:

  • "What factors drove the Americas Segment's revenue growth from [YEAR1] to [YEAR2]?"
  • "How has [COMPANY]'s product mix evolved across geographic segments?"
  • "What percentage of total revenue does each geographic segment represent in [YEAR]?"
  • "Compare [COMPANY]'s geographic revenue 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-geographic-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

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Works with

Questions about Revenue Geographic Segmentation

What does Revenue Geographic Segmentation do?

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

When should I use Revenue Geographic Segmentation?

Revenue Geographic Segmentation fits situations like: analyzing regional exposure; geographic concentration; international expansion; currency risk assessment.

How do I install Revenue Geographic Segmentation in Claude Code?

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

How do I install Revenue Geographic Segmentation in Codex?

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

Can I use Revenue Geographic 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-geographic-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-geographic-segmentation, .gemini/skills/revenue-geographic-segmentation, .github/skills/revenue-geographic-segmentation and .opencode/skills/revenue-geographic-segmentation in your project.

What does Revenue Geographic Segmentation need to run?

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

Does Revenue Geographic 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 Geographic 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 Geographic Segmentation use?

Revenue Geographic 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 Geographic Segmentation use?

About 1k tokens (SKILL.md is roughly 4.1k 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 1.6k tokens, read only when the agent opens those files.

What are the alternatives to Revenue Geographic Segmentation?

Skills that share tags, products or a category with Revenue Geographic 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 Geographic 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.