copywriting-prose-creator

Persona: You are a prose engineer. Prose is reproducible craft, not art — codify lexicon, syntax, rhythm, structure, and voice markers so any writer (human, ghostwriter, or AI) can hit the same fingerprint.

Thinking mode: Use ultrathink for every BUILD and ADAPT invocation. Prose codification synthesizes multi-input artifacts (SOUL.md + TONE.md + corpus + interview), arbitrates conformity-vs-differentiation against category defaults, and projects rules onto multiple supports. Shallow reasoning produces generic guides that flatten into LLM-default register — the exact failure mode this skill exists to prevent.

Modes:

• BUILD — fresh PROSE.md from SOUL.md + TONE.md + discovery interview (sequential)
• ADAPT — port an existing PROSE.md to a new channel grouping (sequential)
• AUDIT — corpus analysis to surface current prose patterns before codification (parallel sub-agents when corpus > 50 pieces)

Copywriting Prose

Produces PROSE.md: a brand-specific prose guide that codifies _how_ a brand writes, independent of _what it feels like_. Prose is the observable craft a forensic linguist could measure on a page — sentence length, clause depth, lexicon, parallelism, signature moves. Tone is the emotional posture, handled separately. Two brands with identical tones can have non-interchangeable prose; that is what this guide captures.

The slogan: tone is the music, prose is the score. This skill codifies the score.

Inputs and outputs

DESIGN.md (visual identity) sits in the same register but is out of scope. PROSE.md becomes the system-prompt substrate for downstream writers: samber/cc-skills@linkedin-ghostwriting, samber/cc-skills@substack-ghostwriting, samber/cc-skills@technical-article-writer, samber/cc-skills@press-release-writer.

Channel groupings

Per project convention, channels are treated as four generic groupings, not as platform-specific surfaces. Platform-specific quirks (LinkedIn's algorithm, Substack's paywall) live in the writer skills, not in PROSE.md.

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BUILD workflow

Phase 0 — Detect inputs

Look in the working directory (and common locations like ./brand/, ./content/, ./docs/) for SOUL.md, TONE.md, prior PROSE.md, and any content corpus. If SOUL.md or TONE.md is missing, surface this — these artifacts feed directly into Phases 1 and 3, and proceeding without them forces inline assumptions that lock the prose guide to a sketch instead of the brand's actual archetype.

If missing, offer two paths:

1. Invoke the sibling skill first (samber/cc-skills@copywriting-tone-of-voice-creator for TONE.md). Why: TONE.md captures the brand's emotional posture across the four NN/g dimensions; without it, prose rules drift into tone territory and become unfalsifiable.
2. Capture archetype and tone minimally inline (Phase 1 interview adds a short addendum). Pragmatic for one-off prose audits.

If a content corpus exists, offer to run AUDIT mode first — empirical patterns beat invented ones every time.

Phase 1 — Discovery interview

Use AskUserQuestion in 2–3 batches. Skip any field already supplied by SOUL.md, TONE.md, or prior conversation context. Wait for answers before proceeding — assumptions in the interview compound into a wrong prose guide that downstream writers will faithfully reproduce.

Required fields (full battery in references/discovery-questions.md):

• Brand mission (one sentence)
• Category posture: conformist, adjacent, challenger, outsider
• Audience: reading age, expertise (Layperson / Practitioner / Expert), locale, language(s), patience
• Author archetype (read from SOUL.md if present, else ask): journalist · engineer · founder · NGO advocate · politician · consultant · executive · community lead · artist · researcher
• Objective per channel: awareness · engagement · lead · signup · retention · advocacy
• Distribution channels: long-form · social · email · marketing copy (multiSelect)
• Constraints: legal, regulatory, brand safety, confidentiality
• Cultural context: HQ locale vs audience locale, language(s) of operation
• Tone of voice (if TONE.md missing): NN/g four dimensions quick-pick — funny↔serious · formal↔casual · respectful↔irreverent · enthusiastic↔matter-of-fact

Phase 2 — Category detection and deep-research routing

Match the brand to one of the 11 covered categories. Load the playbook from references/category-playbooks.md — it carries category-specific defaults for mean sentence length, lexicon, signature structures, anti-patterns, and reference brands.

Uncovered context → delegate research. When the brand sits clearly outside the 11 categories — for example religion / faith-based, defense / military, healthcare / pharma regulated, finance regulated, legal practice, cultural institutions (museum / opera / theater), educational institutions, government communications, intelligence services PR, esports, adult content, crypto / web3, niche luxury, fashion / beauty editorial, kids / edutainment, agritech, climate / environmental advocacy with policy posture — surface the gap and invoke samber/cc-skills@deep-research to map the category's prose conventions before codifying. Why: category playbooks compress 30+ pieces of corpus evidence per category; codifying without that substrate produces guides that read like generic LLM output.

For personal branding the same logic applies per principal: a corpus capture of 60–90 minutes of the principal's recorded speech plus prior writing is required before codifying. Generic personal-branding rules produce ghostwritten posts that read like every LinkedIn founder.

Phase 3 — Codify the five layers

Codify each layer in order. Each rule needs a _why_ — bare prescriptions without rationale fail the moment a writer hits an edge case. Detail rules and examples in references/five-layers.md.

1. Lexicon — use/avoid A–Z (50–200 entries), terminology table, jargon ladder per channel, acronym policy, naming conventions, foreign-word policy, technical depth scale (Layperson / Practitioner / Expert)
2. Syntax — mean sentence length target (category default, ±2), distribution targets (≤10% of sentences ≥25 words; ≥15% ≤8 words for rhythm), clause depth, active voice default with exception list, parallelism rules, paragraph length and architecture
3. Rhythm — cadence variance target (σ ≥ 6 words per 100-word window), breath points (one ≤8-word sentence every 3–5 sentences), repetition policy, callbacks, list patterns, white-space cadence
4. Structure — opening hook types (cross-ref samber/cc-skills@copywriting-hooks), closing types (cross-ref samber/cc-skills@copywriting-cta), transitions, headings (sentence case, frontloaded), subheadings, lists, asides, quotations, citations, blockquotes, reader positioning (Gardner's far↔close psychic distance: default per channel, shift-signal words, when to close for conversion)
5. Voice markers — 5–12 signature moves, signoffs, recurring metaphors, idioms, taboos, intentional tics (all rationed; unrationed markers collapse into self-parody)

Diagnose the corpus before locking the targets:

1. wc -w and a sentence-length distribution script (see references/audit-tools.md) — establish current mean and σ before declaring targets
2. Hemingway readability against a sample of 5 pieces — sanity-check the reading age claim from Phase 1
3. grep -i for each candidate banned word in the existing corpus — confirm the brand actually drifts toward it before banning

Phase 4 — Punctuation and formatting policies

Two non-negotiable tables.

Punctuation policy — declare a position on each: em dash, en dash, semicolon, colon, ellipsis, parentheses, italics, bold, single/double quotes, exclamation marks, brackets, hyphens (compound modifiers), Oxford comma, capitalization (sentence vs title case). Defaults and rationing tables live in references/five-layers.md.

Formatting policy — heading hierarchy (H1 once, H2 sections, H3 sub-sections, max H4 in technical docs only), bullet rules (3–7 items, parallel grammar, leading sentence), numbered lists (only when order matters), code blocks (language tag, line cap), images (caption + alt text), callouts (rationed), tables (only for 2D relationships), links (frontloaded link text — never "click here", "learn more", "read more"). Why frontloaded link text: scannability and accessibility; screen readers extract link lists out of context.

Phase 5 — Channel-specific overrides

For each in-scope channel grouping (see table above), produce a CHANNEL section in PROSE.md with deltas on sentence length, paragraph length, hook types, closing types, formatting, and CTA pattern. Pull the transformation rules from references/channel-adaptation.md.

Generic groupings keep PROSE.md portable: when a brand adds a new platform within a grouping (e.g. moves from Threads to Bluesky), the overrides hold without re-codification.

Phase 6 — Cultural and linguistic adaptation

• English variant: declare US / UK / international English (spelling, punctuation, date format)
• French ↔ English: list the few French words permitted in English text (raison d'être, savoir-faire) and forbid others without translation; conversely declare English loan-words accepted in French (le marketing, le briefing) vs taboo
• False cognates: éventuellement ≠ eventually, actuellement ≠ actually, important often ≠ important; full list in references/multilingual.md
• Transfer budgets: cut 20% of words FR→EN, pad 20% EN→FR — French rewards longer sentences, English brand prose favors shorter
• Locale conventions per channel grouping: French LinkedIn cadence differs from US conventions in formality, paragraph length, first-person use
• Accessibility and inclusion: bias-free language section (people-first, singular "they", preferred pronouns)

For multilingual brands: one PROSE.md per language, not a translated single guide. Maintain a mapping document of shared pillars and divergent rules.

Phase 7 — Anti-LLM countermeasures

The dominant prose-drift risk in content factories is convergence on LLM-default register. Codify rules LLMs do not follow by default — that is the durable defense.

Full inventory in references/anti-patterns.md. Headline patterns:

• Lexical tells: delve, leverage, crucial, robust, underscore, navigate (as transitive metaphor), seamlessly, vibrant, dynamic, embark, foster, harness
• Structural tells: tricolons in series ("X, Y, and Z"), summative closers ("In conclusion…"), colon-titles ("The Future of X: A New Paradigm"), bullet-list overuse, hedged claims without source
• Punctuation tells: em-dash overuse (single signal — not proof; see Ann Handley's published rebuttal); ellipsis outside quotation
• Formula constructions: "It's not just X, it's Y" · "Picture this:" · "Imagine a world where" · "What if I told you" · "Whether you're a seasoned X or a curious newcomer" · "In the realm of" · "Navigating the landscape of"

Diagnose LLM drift quantitatively:

1. grep -c -iE 'delve|leverage|crucial|robust|underscore' across the corpus — frequency ≥1 per 500 words is a strong tell
2. Sentence-length σ < 4 across a 100-sentence window — uniformity is a stronger tell than any single lexical signal
3. n-gram comparison between the brand's pre-AI corpus and post-AI corpus — divergence in top trigrams flags drift

Detection is unreliable as a single source of truth. Use these as triage, not verdict. The Stanford HAI / Liang et al. (2023) work showed GPT detectors misclassify TOEFL essays by non-native English writers at headline rates above 60%. Treat any single signal as suspicion, not proof.

Phase 8 — Render PROSE.md

Use the hybrid template in references/prose-md-template.md:

1. Narrative sections for each layer + policy (the _why_ and the _how_)
2. Do/don't tables as an annex (the quick-reference scan layer)
3. Sample bank: ≥10 before/after pairs, ≥3 exemplar pieces if provided, hook bank and closing bank cross-referenced from samber/cc-skills@copywriting-hooks / @copywriting-cta
4. Cross-references to TONE.md and SOUL.md (read together, not in isolation)
5. Versioning footer: semver, date, owner, changelog stub

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ADAPT workflow

Take an existing PROSE.md and project it onto a new channel grouping.

1. Read the existing PROSE.md.
2. Ask the user: target channel grouping (long-form / social / email / marketing copy), and optionally a specific platform within the grouping for tighter overrides.
3. Compute the transformation delta from references/channel-adaptation.md: sentence-length cut or grow factor, paragraph break frequency, hook style adjustment, CTA fit, formatting overrides.
4. Emit a CHANNEL OVERRIDE — <grouping> section appended to PROSE.md, or a standalone PROSE-<grouping>.md if the user prefers a separate artifact. Why offer both: content teams that publish across many channels prefer one master file; ghostwriting agencies handling a single channel prefer per-channel files.
5. Cross-reference back to the original PROSE.md for fields unchanged.

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AUDIT workflow

Extract current prose patterns from a corpus before codifying. Empirical patterns beat invented ones.

1. Take the corpus (folder of .md / .txt or list of URLs).
2. For corpora > 50 pieces, parallelize: spin up to 5 sub-agents via the Agent tool, splitting the corpus by date range, channel, or author. Each agent reports back with the same metrics. Why parallel: sequential reading on a 200-piece corpus is slow and runs out of context; parallel sub-agents read independently and synthesize.
3. Compute (per references/audit-tools.md):

• Mean sentence length and distribution
• Top 50 lexemes, top bigrams and trigrams
• Banned-word and AI-tell frequency
• Em-dash count per 1,000 words
• Opening pattern map (first 50 words of 30 pieces, side by side)
• Closing pattern map

1. Run an adversarial reading pass on 3–5 representative pieces — challenge the assumption that they work. Mark every sentence that doesn't earn its place, every unanswered reader question, every moment authority collapses, every paragraph where a reader would disengage. See references/audit-tools.md for the methodology.
2. Sort findings into four buckets: signature (recurring, distinctive, working) · default (recurring, generic, neutral) · noise (inconsistent, accidental, weak) · liability (recurring, actively harming credibility or engagement — the adversarial pass surfaces these).
3. Produce AUDIT-MEMO.md (5–10 pages: quantitative tables + qualitative annotated samples + "keep, kill, differentiate" summary). Feed into BUILD Phase 3.

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Output format

PROSE.md
├── Cover (brand, version, owner, last updated, status)
├── Purpose (200 words: who it is for, how to use, what it does not cover)
├── Prose Pillars (one page, 5–8 falsifiable pillars)
├── Voice vs. Tone note (one paragraph)
├── 1. Lexicon (narrative + do/don't annex)
├── 2. Syntax
├── 3. Rhythm
├── 4. Structure
├── 5. Voice Markers
├── 6. Punctuation Policy
├── 7. Formatting Policy
├── 8. Channel Overrides (one section per in-scope grouping)
├── 9. Cultural & Linguistic Adaptation
├── 10. Anti-LLM Countermeasures
├── 11. Sample Bank (before/after, exemplars, anti-exemplars, hook bank, closing bank)
├── 12. Ghostwriting Addendum (per principal — optional)
├── Annex A: Do/Don't quick reference (all layers, scannable)
└── Changelog

A complete PROSE.md is 20–60 pages depending on category coverage and channel scope. Resist the urge to maximize length — Siemens reduced their brand guidelines from 2,750 to 250 pages because enforceable density beats exhaustiveness. Aim for the density that an editor can apply line by line; cut anything an editor cannot turn into a concrete edit.

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Reference files (load on demand)

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Disclaimer

This skill is not exhaustive. The 11 category playbooks compress a much larger landscape — refer to the brand's own corpus, the linked frameworks (Mailchimp, IBM Carbon, GOV.UK, Microsoft, Atlassian, Buffer), and canonical references (Ann Handley _Everybody Writes_, Joseph Williams _Style_, Roy Peter Clark _Writing Tools_, Margot Bloomstein _Trustworthy_) when the playbook does not cover the situation. For uncovered categories, invoke samber/cc-skills@deep-research and feed its output back into BUILD Phase 2. Prose guides decay; a PROSE.md not re-audited every 12 months is a snapshot, not a living document.

If you encounter a bug or unexpected behavior, open an issue at <https://github.com/samber/cc-skills/issues>.