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Agent skill

Brand Voice Extractor

by gooseworks-ai in gooseworks-ai/goose-skills

Analyze a company's published content to extract their brand voice, writing style, and tone guidelines.

MITAuto-check passedWriting & Content

Install Brand Voice Extractor

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill brand-voice-extractor -a claude-code

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

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills brand-voice-extractor --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/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/brand/capabilities/brand-voice-extractor .claude/skills/brand-voice-extractor && 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
brand-voice-extractor
GitHub stars
1.2k
Used in
1 other repo
Token cost
~1.9k tokens
SKILL.md length
589 words
Files
2
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

Analyze a company's published content to extract their brand voice, writing style, and tone guidelines.

  • Works in 4 steps: Select Content to Analyze → Fetch and Extract Text → Analyze Voice Dimensions → …
  • Tasks that involve Brand voice and tone
  • SKILL.md covers Quick Start, Inputs, Process and Tips, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Brand Voice Extractor is an agent skill from gooseworks-ai/goose-skills. Analyze a company's published content to extract their brand voice, writing style, and tone guidelines. Reads 10-20 of their best content pieces and produces a brand voice profile covering tone, vocabulary level, sentence structure, formatting patterns, CTAs, and target persona. Useful before writing outreach, content, or campaigns that should match a client's existing voice.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill.meta.json`).

It sits in Writing & Content, covering Brand voice and tone. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.

When your agent uses it

  • Tasks that involve Brand voice and tone

Example prompts

  • “/brand-voice-extractor”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Select Content to Analyze
  2. Fetch and Extract Text
  3. Analyze Voice Dimensions
  4. Generate Brand Voice Profile

What it can do on your machine

Read from SKILL.md and the folder at commit c650c6d. 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 markdown).

    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

Brand Voice Extractor loads about 1.9k tokens when it runs. Until then it costs about 100 tokens; SKILL.md has 589 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~100
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

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 gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 589 words, ~1,920 tokens.

Download SKILL.mdSave it as .claude/skills/brand-voice-extractor/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
brand-voice-extractor
description
Analyze a company's published content to extract their brand voice, writing style, and tone guidelines. Reads 10-20 of their best content pieces and produces a brand voice profile covering tone, vocabulary level, sentence structure, formatting patterns, CTAs, and target persona. Useful before writing outreach, content, or campaigns that should match a client's existing voice.
tags
brand

Brand Voice Extractor

Analyze a company's published content to extract their brand voice and writing style. Reads their top content pieces and produces actionable guidelines for matching their voice in future content, outreach, or campaigns.

Quick Start

Extract brand voice for [company]. Use their blog at [url].

Or with content already cataloged:

Extract brand voice for [client]. Use the content inventory at clients/[client]/research/content-inventory.json.

Inputs

InputRequiredSource
Content URLsYesUser provides, or pulled from site-content-catalog output
Company nameYesFor context in the analysis
Number of pagesNoDefault: 15. How many pages to analyze.

Process

Phase 1: Select Content to Analyze

If content URLs are provided directly, use those. Otherwise:

  1. Read the content inventory from site-content-catalog output
  2. Select a diverse sample of 10-20 pages, prioritizing:
    • Blog posts (primary voice indicator)
    • Landing pages (marketing voice)
    • Case studies (storytelling voice)
    • Mix of recent and older content (to detect voice evolution)
    • Mix of topics (to see consistency across subjects)

Selection heuristic:

  • 8-10 blog posts (mix of how-to, opinion, product updates)
  • 2-3 landing pages (homepage, product page, solutions page)
  • 2-3 case studies or customer stories (if available)
  • 1-2 comparison/vs pages (if available)
Phase 2: Fetch and Extract Text

For each selected URL:

  1. WebFetch the page
  2. Extract the main content body (strip nav, footer, sidebar)
  3. Store: title, URL, raw text, word count
Phase 3: Analyze Voice Dimensions

Analyze across these dimensions:

A) Tone
  • Formality spectrum: Casual ↔ Professional ↔ Academic
  • Emotional register: Excited ↔ Measured ↔ Dry
  • Authority stance: Peer/friend ↔ Expert/teacher ↔ Institution
  • Humor usage: Frequent ↔ Occasional ↔ None
  • Directness: Direct/bold ↔ Hedged/diplomatic
B) Vocabulary & Language
  • Reading level: Approximate grade level (simple vs. complex)
  • Jargon usage: Heavy industry jargon ↔ Plain language
  • Technical depth: Assumes expertise ↔ Explains everything
  • Power words: Common persuasion/action words they favor
  • Banned patterns: Words or phrases they conspicuously avoid
  • Unique vocabulary: Distinctive terms or phrases they use repeatedly
C) Sentence Structure
  • Average sentence length: Short/punchy ↔ Long/complex
  • Paragraph length: 1-2 sentences ↔ 3-4 ↔ 5+
  • Opening patterns: How they start articles (question, stat, story, bold claim)
  • Transition style: How they connect ideas
  • Use of fragments: Do they use incomplete sentences for emphasis?
D) Formatting Patterns
  • Headers: Frequency, style (question-based, how-to, numbered)
  • Lists: Bullets vs. numbered, frequency
  • Bold/italic: How they use emphasis
  • Images/media: Frequency, types (screenshots, illustrations, photos)
  • CTAs: Placement, style, frequency, language used
  • Pull quotes/callouts: Do they use them?
Show full SKILL.md (226 more words)Show less
E) Content Structure
  • Typical article length: Short (<800), Medium (800-1500), Long (1500+)
  • Introduction style: Hook type, length
  • Conclusion style: Summary, CTA, open question
  • Use of data/stats: Frequent ↔ Rare
  • Use of examples: Frequent ↔ Rare
  • Storytelling: Narrative-driven ↔ Information-driven
F) Persona & Audience
  • Who they write for: Inferred target reader (role, seniority, industry)
  • Assumed knowledge level: Beginner ↔ Intermediate ↔ Expert
  • Point of view: First person singular (I) ↔ First person plural (we) ↔ Second person (you) ↔ Third person
  • Reader relationship: Peer ↔ Teacher ↔ Service provider
Phase 4: Generate Brand Voice Profile

Produce a Markdown document with this structure:

markdown
# Brand Voice Profile: [Company Name]
**Analyzed:** [Date] | **Content pieces analyzed:** [N]
**Sources:** [list of URLs analyzed]

---

## Voice Summary (2-3 sentences)

[Company] writes in a [tone] voice that [description]. Their content targets
[audience] and assumes [knowledge level]. The overall feel is [adjectives].

---

## Tone Profile

| Dimension | Position | Evidence |
|-----------|----------|----------|
| Formality | [e.g., Professional-casual] | [Example quote] |
| Emotional Register | [e.g., Measured, occasionally excited] | [Example] |
| Authority | [e.g., Expert/teacher] | [Example] |
| Humor | [e.g., Rare, dry when used] | [Example] |
| Directness | [e.g., Very direct, bold claims] | [Example] |

---

## Language & Vocabulary

### Reading Level
[Grade level estimate and what that means]

### Signature Phrases
- "[phrase 1]" — used frequently to [purpose]
- "[phrase 2]" — recurring pattern in [context]

### Jargon & Technical Depth
[How much industry jargon they use, how they handle technical concepts]

### Words They Love
[List of frequently used power words, adjectives, verbs]

### Words They Avoid
[Notable absences or patterns they steer away from]

---

## Structure & Formatting

### Typical Article Structure
[Outline of how their articles are typically organized]

### Sentence & Paragraph Style
- Average sentence length: [X words]
- Typical paragraph: [X sentences]
- Notable patterns: [fragments, rhetorical questions, etc.]

### Formatting Habits
- Headers: [style]
- Lists: [frequency and style]
- Emphasis: [bold/italic patterns]
- CTAs: [where, how often, what language]

---

## Audience & Persona

### Target Reader
[Role, seniority, industry, pain points they address]

### Knowledge Assumptions
[What they assume the reader already knows]

### Point of View
[I/we/you usage and what it signals]

---

## Writing Guidelines (Actionable)

Use these guidelines when writing content, outreach, or campaigns for [Company]:

### Do
- [Guideline 1 with example]
- [Guideline 2 with example]
- [Guideline 3 with example]

### Don't
- [Anti-pattern 1]
- [Anti-pattern 2]
- [Anti-pattern 3]

### Voice Samples

**Their style:**
> [2-3 representative quotes from their content that exemplify the voice]

**How to match it:**
> [2-3 example sentences written in their voice about a neutral topic]

Tips

  • 15 pages is the sweet spot. Fewer than 10 won't capture enough variation. More than 25 adds cost without much signal.
  • Blog posts are the best voice signal. Landing pages are more formulaic. Blog posts show the authentic voice.
  • Look for consistency AND inconsistency. If their tone shifts dramatically between content types, note it — they may have multiple voice modes.
  • Check for ghost-written content. If some posts feel dramatically different, they may use external writers. Flag this in the analysis.
  • This skill has no code script. It's an agent-executed skill — the AI agent reads the content via WebFetch and performs the analysis directly. The structured output template above guides the analysis.

Dependencies

  • Web fetch capability (for reading content pages)
  • Optional: site-content-catalog output (for selecting which content to analyze)
  • No API keys or paid tools required

© gooseworks-ai, 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 1 other file in skills/brand/capabilities/brand-voice-extractor of gooseworks-ai/goose-skills.

  • SKILL.md
  • skill.meta.json

Open the folder on GitHubat commit c650c6d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Brand Voice Extractor 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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Categories

Questions about Brand Voice Extractor

What does Brand Voice Extractor do?

Analyze a company's published content to extract their brand voice, writing style, and tone guidelines. Brand Voice Extractor is an agent skill from gooseworks-ai/goose-skills. Analyze a company's published content to extract their brand voice, writing style, and tone guidelines.

When should I use Brand Voice Extractor?

Brand Voice Extractor fits situations like: tasks that involve Brand voice and tone.

How do I install Brand Voice Extractor in Claude Code?

Run `npx skills add gooseworks-ai/goose-skills --skill brand-voice-extractor -a claude-code`. Or copy the skill folder (skills/brand/capabilities/brand-voice-extractor in gooseworks-ai/goose-skills) into .claude/skills/brand-voice-extractor in your project. Claude Code loads it when a task matches its description.

How do I install Brand Voice Extractor in Codex?

Run `npx skills add gooseworks-ai/goose-skills --skill brand-voice-extractor -a codex`. Or copy the skill folder (skills/brand/capabilities/brand-voice-extractor in gooseworks-ai/goose-skills) into .agents/skills/brand-voice-extractor in your project. Codex loads it when a task matches its description.

Can I use Brand Voice Extractor 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 gooseworks-ai/goose-skills --skill brand-voice-extractor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/brand-voice-extractor, .gemini/skills/brand-voice-extractor, .github/skills/brand-voice-extractor and .opencode/skills/brand-voice-extractor in your project.

What does Brand Voice Extractor need to run?

SKILL.md names no scripts, command-line tools or credentials: Brand Voice Extractor is instructions for the agent only.

Does Brand Voice Extractor 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 Brand Voice Extractor 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 Brand Voice Extractor use?

Brand Voice Extractor 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 Brand Voice Extractor use?

About 1.9k tokens (SKILL.md is roughly 7.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Brand Voice Extractor?

Skills that share tags, products or a category with Brand Voice Extractor: Avoid AI Writing (conorbronsdon/avoid-ai-writing, 4.9k stars), Brand (Ohh-889/skyroc, 795 stars), Khazix WeChat Article Writer (KKKKhazix/khazix-skills, 21k stars) and Writing Guidelines (vercel-labs/agent-skills, 32k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Brand Voice Extractor?

gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.

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