Voice Builder

Use to build a reusable voice guide (voice.md) by analyzing a person's or brand's ACTUAL writing samples — so every other social skill can write in their exact voice instead of generic AI prose. Run this when the user says "build my voice," "sound like me," "capture my tone," "analyze my writing," "train on my posts," "ghostwrite in my voice," or when content keeps coming out off-voice or generic. Requires real writing samples; if there are none, use brand-profile's voice interview instead. Works for anyone: founders, creators, executives who are ghostwritten, and brands. Read brand-profile first; this skill produces the deeper voice.md that complements it. This skill DEFINES the voice; to apply it per piece — tone shifts, style edits, de-AI-ing a draft — use writing-style-and-tone.

This skill turns real writing into a reproducible voice — a voice.md that any downstream skill (or human) can write from and produce posts indistinguishable from the source.

The core principle: voice is extracted from evidence, not described in adjectives. "Punchy and authentic" is unwritable — it tells a writer nothing. "Opens with a one-line provocation, writes in short sentences with one long one for contrast, uses em-dashes, never uses exclamation marks, ends on a question" is reproducible. Your job is to convert someone's writing into rules that specific.

When to use this

• After brand-profile, to go deep on voice when the user has writing samples.
• When content keeps coming out generic, stiff, or "not me."
• For ghostwriting at scale — capture the voice once, write in it forever.

When NOT to use this: if there are no usable samples, don't invent a voice. Route to brand-profile's voice interview (dimensions + lexicon + the "we sound ___, never ___" line) and come back when samples exist. A fabricated voice is worse than an honest interview.

Step 0 — Read brand-profile first

Load brand-profile.md for identity, audience, and guardrails. voice-builder produces the voice.md that sits alongside it. If a voice.md already exists and is current, load it, summarize it back, and skip to whatever the user actually needs.

Step 1 — Gather the right samples

Quality beats quantity. The best samples are unmistakably the person — pieces they're proud of, in their natural register, that they actually wrote (not committee-edited, not ghostwritten by someone else, not AI-generated). Aim for 5–10 substantial samples; 3 is the floor. Spoken transcripts are gold — they capture natural rhythm before self-editing flattens it. See references/samples-guide.md for what to collect and what to exclude.

If samples are thin or inconsistent, say so plainly and proceed at lower confidence rather than overstating. Honesty about confidence is part of the deliverable.

Step 2 — Analyze across the six layers (with evidence)

Do not free-associate about "tone." Work the framework in references/analysis-framework.md, layer by layer:

1. Lexicon — word choice, signature phrases, contractions, register, banned-by-habit words.
2. Syntax — sentence length and variance, openers, fragments, active/passive, rhythm.
3. Rhetoric — questions, direct address, repetition, analogy, contrast, humor, the "turn."
4. Structure — how they open, build, and close; paragraphing; formatting.
5. Stance — distance from reader, authority posture, confidence, emotional register.
6. Negative space — what they conspicuously never do. Often the most defining layer.

For every pattern you name, cite the evidence — quote the sample. A voice guide built on assertions drifts; one built on quotes holds.

Step 3 — Distill the voice fingerprint

From the analysis, extract the 5–7 most distinctive, reproducible signatures — the few things that, done right, make the writing recognizable as theirs. This is the heart of voice.md. Pair it with the negative space (the hard "never"s). If you nailed only the fingerprint, a reader should already believe it's them.

Step 4 — Write voice.md

Use references/voice-template.md. Make it operational, not a personality essay: rules a writer can follow, a tone map for how the voice flexes by context, and 3–5 do/don't rewrites (a generic sentence → the same thing in this voice). Keep real sample snippets in as calibration anchors.

Step 5 — Validate with the voice test (do not skip)

A voice.md that hasn't been tested is a hypothesis. Run the loop in references/validation.md:

1. Generate a fresh post on a new topic using only voice.md.
2. Place it beside a real sample. Could the user tell which is which?
3. Score it against the fingerprint signatures — which did it hit, which did it miss?
4. Fix the misses, refine voice.md, and repeat once or twice.

Show the user the test post and the result. The voice isn't done until it passes.

What "great" looks like (self-check before finishing)

• A writer who has never met this person could write an on-voice post from voice.md alone.
• Every claim cites a sample — no unsupported adjectives.
• The fingerprint is specific enough to be falsifiable (you could point to a post and say "that's not them, because…").
• Negative space is captured, not just positive traits.
• It passed the voice test, and you showed the proof.

If any of these is missing, you're not done.

Edge cases — handle explicitly

• Thin samples (1–2 short pieces): capture what's observable, mark the guide low-confidence, name which layers you couldn't determine, and supplement with the brand-profile interview. Don't bluff certainty.
• Inconsistent samples (voice drift): don't average them into mush. Flag the inconsistency and ask which samples represent the target voice; anchor on those.
• Aspirational voice (they want to sound different than they currently do): separate current from target. Collect samples of who they want to sound like, label them aspirational, and blend transparently so the user knows what's being borrowed.
• Multiple voices (company vs founder; several creators): one voice.md per voice, named. Never blend voices — it produces a person who exists nowhere.
• Heavily edited / ghostwritten / AI-generated samples: exclude them; they aren't the person's voice. Say why.
• Spoken vs written: transcripts reveal natural rhythm but need cleanup rules (filler, false starts). Capture the rhythm, note the cleanup.
• Multilingual: analyze per language; voice rarely maps 1:1 across languages.

Related skills

• brand-profile — read first; provides identity, audience, guardrails.
• writing-style-and-tone — applies the voice.md per piece and per moment (tone map, edit passes, AI-tell sweep). This skill writes the constitution; that one governs by it.
• Every content skill (caption-writer, reels-script, thread-writer, linkedin-post-writer, …) reads the resulting voice.md.
• audience-research, content-pillars — complementary foundation skills.

References

• references/samples-guide.md — how to source and vet the right writing samples.
• references/analysis-framework.md — the six-layer framework for deconstructing voice.
• references/voice-template.md — the exact voice.md structure to produce.
• references/validation.md — the voice test: generate, compare, score, refine.
• references/examples.md — two contrasting worked examples, end to end.