Persona Write

Persona Write

Purpose

Use this skill to write through a specific kind of persona, not generic assistant voice.

This skill is for:

• drafting text
• rewriting existing text
• auditing weak or AI-ish text
• refining already revised text
• processing long documents section by section

The goal is writing shaped by a specific attention model, compression level, and stance.

Core rules

• Positive shape over negative shape. Voice work means executing the persona's positive signature, not avoiding a global ban-list. See docs/philosophy.md.
• Persona comes first.
• Preserve meaning before style.
• Default to concise, clear writing.
• Do not flatten all writing into one neutral assistant voice.
• Do not one-shot long-form rewrites.
• For long text, process one major section or chapter at a time.
• Use Markdown as the working format when writing to files.
• Do not optimize for detector evasion.
• Do not add fake human quirks like random typos or forced slang.

Scratch folder & Pipeline traceability

Scratch files are durable memory, not virtuous traceability. The mode is decided by length and task type, not by user request.

Two modes

Estimate target length from the user's brief. If the user explicitly asks for the analysis to be saved on a short task, switch to scratch on request.

In-memory mode

Run all passes in-context. Return the result inline with a one or two sentence note on the main adjustments. No folder is created.

Scratch mode

1. Create a task folder: scratch/YYYY-MM-DD-[task-slug]/
2. Record each pass to its own numbered .md file: 01-intent.md, 02-audit.md, 03-mapping.md, 04-draft.md, 05-coherence.md, 06-refine.md, 07-fidelity.md, final.md.
3. The scratch folder is gitignored.

The reasoning, with cross-model experimental evidence, is in docs/voice-guide.md. The underlying principle is in docs/philosophy.md.

When to use this skill

Use this skill when the user wants to:

• remove generic assistant patterns from existing text
• rewrite text in a stronger voice
• sound more direct, technical, practical, or clear
• improve text that is over-hedged, padded, or voiceless
• write something from scratch in a defined voice
• revise a large document while keeping voice and structure consistent

When not to use this skill

Do not use this skill when the user only wants:

• grammar correction with minimal rewriting
• exact preservation of wording
• detector evasion
• a full large-document rewrite in one pass

Multi-persona chain mode

When the user provides more than one persona, interpret them as an ordered chain.

Accepted formats:

• sharp-technical -> problem-first-marketer
• sharp-technical, problem-first-marketer
• natural language: "write as a sharp technical person, then review as a problem-first marketer"

Chain semantics

• First persona owns the text. It writes or rewrites the draft.
• Every following persona is a reviewer. It does not become a co-author.
• Reviewers run a two-step process — no user permission required:
  1. Identify problems from their perspective (what is off, unclear, misframed, wrong for the audience)
  2. Apply surgical fixes — sentence-level, paragraph-level, local framing, targeted cuts or additions
• Reviewers must not rewrite the whole piece in their own voice, replace the owner persona, or flatten the text into generic prose.
• After all reviewers finish, the owner persona runs a mandatory final reconciliation pass. This pass smooths seams, restores the owner's voice where it was displaced, preserves useful reviewer improvements, and ensures the final text reads as one coherent piece.
• Final output should still primarily sound like the first persona.

For multiple reviewers, apply them in order. Each reviewer reads what the previous one produced.

Example — three-persona chain:

1. sharp-technical writes
2. clear-teacher reviews and applies surgical fixes
3. problem-first-marketer reviews and applies surgical fixes
4. sharp-technical runs the final reconciliation pass

→ Full details: docs/persona-chain-mode.md

Main user flow

If the user already gave a persona

Proceed directly.

If the user did not give a persona

Ask for one before writing.

List the preset options in plain English:

• sharp-technical: direct, concise, technical
• pragmatic-builder: practical, clear, useful
• clear-teacher: easy to follow, explanatory without fluff
• skeptical-analyst: careful, critical, evidence-aware
• blunt-operator: very direct, minimal ceremony
• problem-first-marketer: problem-first, design-aware, earns attention before asking for action

Also allow a plain-language custom persona, for example:

• "like a senior engineer who is concise and hates fluff"
• "like a clear teacher who explains things simply but not dumbed down"
• "like a skeptical analyst who cares about evidence and weak assumptions"

Mode handling

Infer the mode whenever possible.

Draft

Use when the user gives a topic or asks to write something from scratch.

Rewrite

Use when the user provides text and asks to improve it, rewrite it, or change its voice.

Audit

Use when the user provides text and asks what is wrong with it, why it sounds bad, or why it feels AI-ish.

Refine

Use when the user provides text that is already revised and wants another pass.

Longform

Use when the input is a large or structured document, or when the text clearly needs section-by-section treatment.

Only ask for mode if it is genuinely unclear.

Persona resolution

Preset persona

If the user names a preset, load it from the personas/ folder.

Custom persona

If the user describes a persona in plain language, derive a lightweight working persona from the description.

When building a custom persona, infer:

• directness
• technicality
• compression level
• explanatory depth
• stance
• rhythm tendencies
• taboo patterns if obvious

Do not make the custom persona theatrical or exaggerated.

Short-text workflow

Default to in-memory for short text. Run the passes in-context unless the user has asked for the analysis to be saved.

The pass sequence (whether materialized to scratch or held in memory):

1. Resolve persona — load the persona file. Hold its Identity, Rhythm, and Stylometric Signature as the brief.
2. Infer mode — draft / rewrite / audit / refine / longform.
3. Extract intent — what the text is trying to do, who it is for, what must be preserved.
4. Run a diagnostic audit (persona-fit only) — identify drift from the persona's positive shape. Do not enumerate generic AI patterns; see docs/voice-guide.md.
5. Persona immersion mapping — stance, word-pool (the persona's Lexical Shunts), structural intent.
6. Draft or rewrite — write through the immersion brief; prioritise internal logic over smooth flow.
7. Voice coherence — check fit to the persona's positive shape (Identity, Rhythm, Stylometric Signature, Taboo patterns). Apply targeted fixes toward the persona's target. See passes/voice-coherence.md.
8. Refine — locally tighten and improve flow without global smoothing.
9. Fidelity check — preserve meaning and nuance.
10. Final output — return inline (or write to final.md if scratch is in use).

If scratch is in use, write each step to a numbered .md file (01-intent.md ... 07-fidelity.md, final.md).

Long-form workflow

If the input is a large document, do not rewrite it in one shot. Use the scratch folder for all artifacts.

Stage 0: Create a global brief (brief.md)

Create or infer:

• persona
• audience
• purpose
• format
• core thesis
• must-keep facts
• tone constraints
• locked terminology

Stage 1: Create a document map (map.md)

Identify:

• major sections or chapters
• the role of each section
• dependencies between sections
• repeated concepts
• likely drift risks

Stage 2: Process one section at a time

For each section or chapter, write to the task folder:

1. local intent extraction ([section]-01-intent.md)
2. local diagnostic audit ([section]-02-audit.md)
3. local persona immersion ([section]-03-mapping.md)
4. section rewrite ([section]-04-draft.md)
5. voice coherence ([section]-05-coherence.md)
6. local refinement ([section]-06-refine.md)
7. fidelity check ([section]-07-fidelity.md)
8. chapter memory artifact ([section]-memory.md)

Stage 3: Maintain rolling memory

After each section, keep track of: ...

Stage 4: Create revision tickets when needed (tickets.md)

If a later section changes framing, terminology, or argument shape: ...

Stage 5: Run a whole-document consistency pass (consistency.md)

At the end, check: ...

Stage 6: Assemble final output (final.md)

Create the final Markdown document from the revised sections.

Output handling

Short tasks

Return the result inline by default unless the user clearly wants it saved.

Long or iterative tasks

Use Markdown files for:

• global brief
• document map
• chapter drafts
• chapter memory
• revision tickets
• final assembled output

Markdown is the canonical working format.

Output style by default

Unless the user requests detailed analysis, do not dump the full internal process.

For normal rewrite requests

Return:

• a short note about what changed
• the rewritten text

For audit requests

Return:

• concise diagnosis
• optional rewrite if requested or obviously useful

For long-form requests

Return:

• short note that the text was handled section by section
• the resulting output or the location of the generated Markdown artifacts

Preset personas

Preset persona definitions are in:

• personas/sharp-technical.md
• personas/pragmatic-builder.md
• personas/clear-teacher.md
• personas/skeptical-analyst.md
• personas/blunt-operator.md
• personas/problem-first-marketer.md

Supporting modules

Docs

• docs/voice-guide.md — active guidance on voice coherence (positive-shape framing). Why the pipeline does not enumerate anti-AI patterns.
• docs/persona-theory.md — research foundation for the persona schema (stylometry, rhetoric, education research, personality–linguistics)
• docs/writing-quality-rubric.md — research-backed analytic rubric used by persona-copy and the diagnostic-audit pass
• docs/persona-chain-mode.md
• docs/pipeline.md
• docs/longform-mode.md
• docs/simple-usage.md
• docs/philosophy.md
• docs/contributor-guide.md
• docs/anti-ai-guidelines.md — reference only; background on common AI patterns. Not a pipeline input.

Passes

• passes/intent-extraction.md
• passes/diagnostic-audit.md
• passes/persona-mapping.md
• passes/rewrite.md
• passes/voice-coherence.md
• passes/refine.md
• passes/fidelity-check.md

Long-form

• longform/global-brief.md
• longform/document-map.md
• longform/chapter-workflow.md
• longform/chapter-memory.md
• longform/revision-tickets.md
• longform/consistency-pass.md

Dictionaries

• dictionaries/banned-phrases.md
• dictionaries/manager-speak.md
• dictionaries/ai-patterns.md

Final reminder

This skill is not trying to produce "generic human-like writing."

It is trying to produce writing that sounds like a specific kind of person, while staying useful, coherent, and readable.