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.
npx gooseworks install --all # then, in Claude Code, Cursor, or Codex: /gooseworks use the brand-voice-extractor skill
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.
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.| Input | Required | Source |
|---|---|---|
| Content URLs | Yes | User provides, or pulled from site-content-catalog output |
| Company name | Yes | For context in the analysis |
| Number of pages | No | Default: 15. How many pages to analyze. |
If content URLs are provided directly, use those. Otherwise:
site-content-catalog outputSelection heuristic:
For each selected URL:
Analyze across these dimensions:
Produce a Markdown document with this structure:
# 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]site-content-catalog output (for selecting which content to analyze)Full video production sequence with script review, actual ingredient choices, controlled generation, editing, evidence-based quality review, polish, captions and delivery. A host binding supplies project storage, authentic approvals, provider access and billing.
Build a vox-pop street interview video ad. An interviewer with a handheld mic asks passers-by one question about the brand's product, they give blunt wrong guesses, one gives the real answer, and the cut lands on a branded end card. Generates the takes through the GooseWorks fal proxy (Seedance 2.0 with native voice), then grades, re-cuts, captions and gates them locally. Use for the street-interview format.
Write the words of a short-form video ad (voiceover, dialogue, chat bubbles, on-screen lines) the way performance creative teams do instead of from a blank page. Builds the script from the buyers' own words, the beat sheet of an ad that already works and three deliberately different angles, filters them with a rule check and a second non-Claude model, and takes the strongest into the review with the other two as one-line swaps. Use it in every video ad run before any paid step, and whenever the user asks to write, rewrite or improve a video ad script or says a script sounds generic or AI-written.