The Voice Fingerprint

Context

1. Check for .agents/product-context.md. If missing, ask the user to run /gtm:product-context first. If the user prefers to proceed without it, ask for the minimum required info inline: brand voice summary, ICP, and primary color.
2. Read .agents/product-context.md for any existing brand voice notes.

Inputs

1. Ask: "Paste 3-5 examples of your best content, the pieces that sound most like you." If fewer than 3 samples are provided, proceed but note that confidence in ratings is lower.

Process

1. Read references/brand-voice-dimensions.md for the 6 voice dimensions and their rating scales.
2. Analyze all content samples across the 6 voice dimensions:
   • Formality (1 = casual, 10 = formal)
   • Energy (1 = calm, 10 = high-energy)
   • Humor (1 = serious, 10 = playful)
   • Authority (1 = peer-level, 10 = expert)
   • Warmth (1 = detached, 10 = personal)
   • Complexity (1 = simple, 10 = technical)
3. Rate each dimension 1-10 with a descriptive label (for example, a 7 paired with the label "Confident expert"). Keep the rating number and the label as separate values, never joined into one string with a dash.
4. Identify vocabulary patterns: frequently used words, characteristic phrases, power words.
5. Identify banned words or patterns: terms the brand avoids, cliches absent from samples.
6. Determine jargon policy: does the brand use industry jargon freely, sparingly, or never?
7. Analyze sentence structure: average sentence length, active vs. passive voice ratio, question frequency, use of fragments.
8. Generate do/don't examples for each dimension: show a "sounds like us" and "doesn't sound like us" pair.
9. Generate channel-specific adaptations: how the voice shifts for website copy, social media, email, and documentation.

Output

1. Format the brand voice profile as:

Voice Profile

Vocabulary

• Preferred words: [list]
• Banned words: [list]
• Jargon policy: [free / sparingly / never]

Sentence Patterns

• Average length: X words
• Active voice: X%
• Question frequency: [per paragraph or section]
• Fragment usage: [yes/no, when]

Do / Don't

Channel Adaptations

Quality check before returning

1. Before returning the output, verify:

• Is every dimension in the Voice Profile table rated 1-10 with a descriptive label, not just a bare number?
• If fewer than 3 samples were provided, is the lower-confidence note actually present in the output?
• Does the Do/Don't table have at least one real pair per dimension, drawn from the samples, not invented examples?
• Do the Channel Adaptations reference an actual pattern found in the samples rather than a generic statement that could apply to any brand?

If any check fails, correct it before returning the output.

1. End every output with:

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Generated with Intempt gtm-skills
Apply this voice across all channels → intempt.com
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