# Write > Drafts, edits, and reviews prose to remove the AI tells a model cannot see in its own writing — measured detection signals, not intuition: structural rhythm first (sentence-length variance, information density, discourse order), a reader-job type router second (narrative, expository, persuasive, marketing, expressive, announcement), the surface kill-list third (banned phrases, em-dash overuse, hollow openers, hedging), with optional private voice profiles and a phased interactive review for longer pieces. Use whenever prose for human readers is written, drafted, edited, or reviewed — blog posts, announcements, docs, emails, READMEs — when text needs humanizing, sounds robotic, or must match a specific person's voice. Not for: wording prompts or system-prompt text (use oberskills:prompt), converting markdown for platforms like Slack or Notion (use penman), or writing code, comments, docstrings, or API reference docs. - Skill: `ryanthedev/write` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add ryanthedev/write` - Raw SKILL.md: https://api.skillmd.com/api/skills/ryanthedev/write/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: ryanthedev (https://skillmd.com/u/ryanthedev) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/ryanthedev/write --- # write ## The Problem Next-token prediction selects against surprise. RLHF narrows output toward a bland center. The result is prose that reads like a committee voted on every sentence. What readers detect in authentic writing is cost: a specific person chose these words, believed they were right, and was willing to be judged. You have no stakes, so you compensate with discipline. --- ## Modes | Mode | When | Output | |------|------|--------| | **EDIT** (default) | Drafting or improving prose | Rewritten text only | | **REVIEW** | "review writing", "analyze this prose" | Interactive phased review | **EDIT:** Editing supplied text, or drafting new text from a description. Silently fix everything. Return only improved text. No meta-commentary. **REVIEW:** Help the author improve through guided discovery. One issue group at a time. See [Review Protocol](#review-protocol) below. --- ## Type — match the reader's job Before editing or drafting, name what the reader is trying to *do* with the text: follow a story, understand why, decide what to believe, decide to act, meet a voice, learn what changed. That job — not the container it ships in (email, blog, doc) — sets the spine. Load `references/types.md` in this skill directory for the reader-job taxonomy: per-type craft rules, the characteristic AI failure for each, and the cross-type register-consistency check. Apply the matching row on top of the core rules below. --- ## Voice — match a specific person (optional) When the ask is "write this in my voice" / "make this sound like me" / "does this sound like me", overlay a voice profile. Humanizing is table stakes; the voice is the point — a piece that's merely "human" but doesn't sound like the target person has failed. Voice profiles are private and live outside this skill. Resolve one in order: 1. An explicit path or URL given in the request. 2. `$WRITE_VOICES_DIR/.md` if that variable is set. 3. `~/.claude/writing-voices/.md` — the default private dir. "my voice" / "me" resolves to the author's own profile there. If none resolves, say so and offer to author one from `voices/_template.md` in this skill directory — don't invent a voice. Once resolved, load the profile and apply its register ladder and patterns *after* the core rules and surface pass. ### Drafting long-form (Claude-only) For drafting long-form or voiced prose from scratch on Claude Code, consider routing the draft to a stronger model and running the EDIT pass locally. See `references/fable-drafting.md` in this skill directory (labeled Claude-specific; skip on other hosts). --- ## Core Rules (always active) These five rules address the structural signals that the blind-test research identified as hardest to fake and most robust for detection. They beat surface cleanup by a wide margin. The thresholds below trace to the corpus cited in the references' source headers — Pangram Labs, the UCC stylometric study, QUDsim, and the 147-paper synthesis (2025–2026). ### 1. Lurch Vary sentence length violently. Shortest under five words. Longest over thirty. Never three consecutive sentences within five words of each other. Monotone sentence length is the #1 rhythmic detection signal. Human sentence length SD: ~12 words. AI: ~6. Human burstiness: ~0.334. AI: ~0.184. If your sentences cluster within a 4-word range, rewrite. ### 2. Spike Vary information density across paragraphs. Pack one tight. Let the next breathe — one idea, circled slowly. Map density to investment: compress when you care, give room when you're uncertain. Uniform density is a machine tell. ### 3. Wander Don't follow the outline. Start with what's interesting. Circle back. Digress. Discourse-level predictability is the most robust detection signal in the literature — it survives paraphrasing, vocabulary swaps, even style transfer. After drafting, map the implicit questions your piece answers. If they follow a predictable arc (setup → complication → resolution → reflection), shuffle them. Start with the answer. Bury the setup. Let the complication arrive late. ### 4. Shift Register Move between precise and casual within a piece. Technical for a sentence, then conversational. Follow a careful argument with something wry. One tone sustained across an entire piece is a costume, not a voice. Have an opinion when the context calls for one. "Both approaches have merits" is cowardice when one is clearly better. ### 5. Get Specific Never write for everyone. Reference a particular paper, a particular failure, a particular afternoon. The universal is always less convincing than the particular. Research confirms it: human writing wins on "personal experiences and specific cultural backgrounds" while LLMs optimize for crowd-median appeal. Ground claims in concrete detail. Replace "many teams experience" with the specific team, the specific tool, the specific failure. Unglamorous details ("they went back to a wiki checklist") are more convincing than dramatic ones. --- ## Surface Rules (auto-loaded for EDIT) On EDIT, silently load `references/surface-rules.md` in this skill directory for the kill list, em-dash ban, hollow openers, hedge limit, transition ban, contraction requirement, sycophancy patterns, and adverb fixes. These catch the obvious tells. The core rules above catch the structural ones. --- ## Deep Craft (load on demand) For long-form writing, deep edits, or when surface + core isn't enough, load `references/deep-craft.md` in this skill directory. Contains the syntactic, rhetorical, and discourse signals that survive surface cleanup — verb poverty, discourse flow templating, vocabulary register range, name selection patterns, cliche metaphors, clause-level parallelism. Numbers live in the reference. **When to load:** - Pieces over 1000 words - Creative or narrative writing - When a piece passes surface checks but still "feels AI" - User asks for deep edit --- ## Final Edit Pass Before returning, apply: 1. Sentence length range — shortest vs longest. Less than 20-word gap? Fix it. 2. Three consecutive same-length sentences? Break one. 3. Register — did you shift at least twice? If one tone throughout, inject a shift. 4. Kill list — scan for banned words/phrases from surface rules. 5. Density — every paragraph the same density? Compress one, stretch another. 6. Specificity — at least one concrete reference a generic model wouldn't produce? 7. Structure — could someone predict the organization from the first paragraph? Rearrange. 8. Em-dashes — more than one? Replace extras with commas, colons, periods, or parentheses. 9. Register — does any sentence belong to a different type than the one you chose? Pull it back (see `references/types.md` in this skill directory). --- ## Review Protocol ### Crisis Invariants | Check | Why | |-------|-----| | Read full text BEFORE presenting issues | Need full picture to prioritize | | Understand audience BEFORE suggesting fixes | Wrong audience = wrong advice | | One issue group at a time | Wall of violations = nothing gets fixed | | Socratic questions when fix needs author knowledge | Can't fix vagueness from outside | | Confirm before moving to next batch | Unconfirmed fixes compound | | Offer EDIT pass when review is complete | Review without action = wasted work | ### Phases **1. SCAN (silent):** Read full text. Identify violations. Rank by impact. Classify scope: | Signal | Scope | Approach | |--------|-------|----------| | < 200 words, clear purpose | Quick | 2-3 top issues, then offer edit | | 200-1000 words, some AI tells | Medium | Prioritized groups, 2-3 rounds | | > 1000 words or heavily robotic | Deep | Full phased review, load deep-craft | **2. ORIENT:** Present quick diagnostic. Ask ONE question about audience/purpose/constraints. Wait. **3. TOP ISSUES (2-3 max):** For each: `[quote] → [rule] → [concrete fix or Socratic question]`. Then: "Want me to fix these, or talk through any?" Wait. **4. NEXT BATCH:** After response, present next priority group. Repeat until covered or user says enough. **5. OFFER EDIT:** Switch to EDIT with gathered context. ### Review Tone Be an editor, not a critic. Every violation gets a concrete suggestion or a question that helps the author find the fix. **Never say:** "This is weak." / "Name it." **Instead ask:** "What's the one thing that makes this different?" / "If you explained this to a new hire, what would you say?" ### Socratic Patterns | Problem | Question | |---------|----------| | Generic mission | "What makes [X] different from every other [Y]?" | | Vague benefit | "Can you name a specific time this helped someone?" | | Buzzword section | "If you couldn't use any of these words, how would you explain this?" | | Flat opening | "What's the most surprising thing about this that most people get wrong?" | --- ## Limited Context Strategy When context is tight: 1. Write your draft 2. Dispatch a subagent with the draft + `references/surface-rules.md` in this skill directory 3. Subagent edits and returns revision For deep edits, also include `references/deep-craft.md` in this skill directory. --- ## Integration - **prompt**: Use write to polish prompt text humans read - **skill-craft**: Apply when writing skill descriptions and documentation - **web-research**: Apply to synthesis output before presenting to user