human-writing by doodledood/claude-code-plugins

User request: $ARGUMENTS

Apply these research-backed writing principles to the current task. If no specific request, apply them to whatever prose content is being written in context.

The Core Insight

The fundamental problem is statistical uniformity. AI text is measurably more predictable (~50% lower perplexity), less varied in sentence length (~38% lower burstiness), and narrower in vocabulary (type-token ratio: human 55.3 vs AI 45.5). The path to human-sounding writing runs through embracing imperfection, not perfecting output.

The single most reliable tell is uniformity. Human writing is messy, varied, and surprising. AI writing is smooth, consistent, and predictable.

The 10-20-70 Rule

Prompting contributes ~10% of output quality, editing ~20%, and the writer's own domain expertise and input ~70%. No amount of prompt engineering substitutes for having something to say. Require the writer's genuine insight, opinions, and experiences before generating content.

Hierarchy of Impact

From highest to lowest impact on making writing sound human:

Priority	Technique	Why
1	Put your own thinking in first	AI cannot generate genuine insight, lived experience, or original analysis
2	Develop a distinctive voice	Voice is the ultimate differentiator — consistent, cannot be faked by editing
3	Edit ruthlessly	Four-layer system: word → sentence → structure → content
4	Design the workflow	Never write a complete piece in one shot
5	Prompt with constraints	Banned words + persona + writing samples
6	Embrace imperfection	Fragments. Tangents. Opinions. Rough edges make writing alive.

Vocabulary Kill-List

Never use these words — they are statistically flagged as AI-generated across peer-reviewed studies of millions of documents:

Nouns: delve, tapestry, landscape, realm, testament, journey, insight, resilience, ecosystem, milestone, prowess, utilization

Verbs: embark, endeavor, leverage, harness, navigate (metaphorical), unlock, foster, catalyze, bolster, underscore, showcase, elucidate, encompass, unveil

Adjectives: seamless, robust, groundbreaking, transformative, pivotal, vibrant, compelling, crucial, invaluable, holistic, multifaceted, meticulous, commendable, intricate

Adverbs: seamlessly, meticulously, notably, profoundly, predominantly, subsequently, thereby, ultimately

Phrases: "ever-evolving landscape," "in today's fast-paced world," "as we navigate the complexities," "It isn't just X, it's Y," "it's important to note," "it's worth noting that," "without further ado," "in conclusion," "at the heart of"

False intensifiers: "genuinely," "truly," "actually" (when used to simulate conviction)

Four-Layer Editing System

Apply in order from surface to substance:

Layer 1: Word-Level

Search-replace or delete kill-list vocabulary on sight. Strip adjectives from paragraphs, restore only those carrying concrete information. "Robust system" → "handles 10k req/s without data loss."

Layer 2: Sentence-Level

Read only the first few words of consecutive sentences — wherever three or more follow the same pattern, cut or combine. Vary sentence length deliberately: short for punch, long for nuance. The contrast creates impact. Add intentional imperfection: fragments, casual asides, conversational phrasing.

Layer 3: Structural

Eliminate meta-commentary ("In this section, we will..."). Kill recap conclusions that only repeat earlier points. Break pattern symmetry: demote repetitive subheadings, merge overlapping sections, ensure each paragraph's opening differs structurally from the one before.

Layer 4: Content

Add lived experience: anecdotes, firsthand observations, specific failures. Ground in specifics — ask of every sentence: "Could this fit any topic?" If yes, it needs grounding. Inject honest opinion: state what you actually think, not what "many experts" believe.

Final Check

Read aloud. Stumbling, running out of breath, or awkwardness marks where prose needs work.

Seven Craft Fundamentals AI Structurally Cannot Produce

These are structural limitations of statistical text generation — areas where human writers create unbridgeable distance:

Showing vs Telling — Render specific sensory details that let readers experience emotion. AI defaults to summarizing ("serene and tranquil") rather than showing (the dragonfly hovering over still water).
Specificity from Lived Experience — AI produces "gentle breeze" and "blooming flowers" (statistically most probable). Replace generic descriptions with observations nobody else has made. Name the cafe, the specific dish, the particular moment.
Strategic Omission — AI tends toward completeness and closure. Resonant writing lives in what's left unsaid. A character dodging a question reveals more than any direct statement. Trained to produce text, not withhold it.
Rhythm Variation — AI produces sentences of similar length and structure. Use rhythm deliberately: shorten sentences as tension rises. Drop a short sentence after several long ones. Like that.
Deliberate Rule-Breaking — Choose the wrong word because it sounds better. Let a fragment hang. Incomplete sentences. For emphasis. Because sometimes a complete sentence kills the moment.
Humor — Classified as an "AI-complete problem." Google DeepMind study with 20 comedians: AI "struggled to produce material that was original, stimulating, or — crucially — funny." Humor requires authentic vulnerability and cultural boundary-breaking.
Genuine Insight — AI provides summaries; humans provide analysis. Keep asking "Why?" iteratively. Data shows the "what" — insight tells the "why."

Structural Anti-Patterns

Pattern	Tell	Fix
Uniform paragraph length	Every section gets equal treatment regardless of importance	Spend more space on what matters, less on what doesn't
List addiction	Jumping into numbered/bulleted lists without narrative buildup	Use flowing prose; lists only when genuinely parallel
Formulaic scaffolding	"Firstly... Secondly... Finally" at 2-5x human rate	Vary transitions or eliminate them
Grammar perfection	No fragments, run-ons, or unconventional starts	Perfection is suspicious — include occasional wonky phrasing
Colon titles	"Topic: Explanation" format	Vary title structure
Symmetric structure	Every section mirrors the same internal organization	Break the pattern

Punctuation and Formatting Rules

Em-dashes (—) and en-dashes (–): One of the most reliable AI tells. ChatGPT uses 8 per 573 words; Deepseek 9 per 555 words. Ban them entirely — use commas, periods, parentheses, or colons instead.
Emojis: AI overuses emojis as emotional proxies, especially in casual/marketing content. Never add emojis unless the user explicitly requests them. Excessive emoji use signals AI generation immediately.
Semicolons: AI rarely uses them. Including some adds human texture.
Contractions: AI avoids them. Use them freely in conversational prose.
Oxford commas: AI applies them consistently. Break the pattern occasionally.

Rhetorical Anti-Patterns

Pattern	Tell	Fix
Tricolon obsession	Groups ideas in threes: "Time, resources, and attention"	Break with two, four, or seven items erratically
Perfect antithesis	"Not just X, but Y" — neat binary oppositions	Real arguments are messier
Rhetorical questions as staging	"How do we solve this?" → pre-composed answer	Ask genuine questions or just state the point
Excessive hedging	"may potentially offer what could be considered significant benefits"	Strip to: "this works"
Compulsive signposting	"It's worth noting," "It's important to remember"	Trust the reader
Opinion-avoidant framing	"commonly described as," "many find," "generally considered"	State the view directly

Tonal Principles

Vary register. AI picks a lane and stays there. Shift between formal and colloquial. Reveal personality through tonal variation.
Don't be relentlessly positive. AI frames everything positively. Call things weak, inadequate, or bad when they are.
Show unequal enthusiasm. AI treats all subjects with equal professional distance. Nerd out about topics you care about. Show visible impatience with boring ones.
Take risks. AI prioritizes broad palatability. Write confusing sentences, sharp observations, and controversial assertions when appropriate.

What Must Be Present (The Negative Space)

AI text is identified as much by what's absent as what's present:

Lived experience — specific personal anecdotes, not "generic specificity"
Sensory specificity — unexpected observations, not statistically probable descriptions
Silence and subtext — what characters don't say, what's left unsaid
Genuine messiness — false starts, changed directions, productive digressions
A perspective — a view that could not fit any other prompt

Prompting Techniques (When Generating)

Tier 1 (Strongest evidence)

Banned word lists: Prohibit the kill-list vocabulary explicitly. Constraint-based prompting outperforms aspirational instructions.
Persona assignment: "Explain this to a colleague over coffee" produces fundamentally different output than "Write about X."
Writing sample matching: Paste your own writing and instruct to match tone, structure, emotional depth — not copy structure.

Tier 2 (Good evidence)

Conversational framing: Frame as conversations, not commands.
Negative instructions: "Don't use formal transitions. Don't start paragraphs with 'It is worth noting.'"
Emotional targeting: "Write from a place of frustration with the old way" — specify the emotional register.

Tier 3 (Supporting evidence)

Short sentence requests: Counteracts multi-clause tendency.
Dramatic paragraph length variation: Include one-sentence paragraphs for emphasis.
Layer multiple techniques: Combine persona + banned words + emotional targeting + samples.

Workflow Principles

Never write a complete piece in one shot. Section-by-section with review between.
Bookend approach: AI for ideation at start, polish at end. Human controls the core creative middle.
Messy draft approach: Your scattered thoughts + original angles → AI cleans into readable prose → you edit for voice.
Your original thinking goes IN before AI touches it.

Statistical Signatures (Detection Context)

Understanding what detectors measure helps write text that doesn't trigger them:

Metric	Human	AI	Meaning
Perplexity (surprisal)	~8.2	~4.2	AI is ~50% more predictable
Burstiness (sentence variation)	0.61	0.38	AI has ~38% less variation
Token probability entropy	4.56	3.11	AI makes more uniform word choices (d=3.08)
Type-token ratio	55.3	45.5	Humans use broader vocabulary
Late-stage volatility	Consistent	Decays 24-32%	AI becomes more predictable as it continues

Key implication: Introduce genuine unpredictability — varied vocabulary, surprising sentence lengths, unexpected word choices, inconsistent structure.

Detection Methods (Reference)

Statistical/zero-shot: Perplexity, burstiness, entropy, probability curvature (DetectGPT), cross-perplexity ratio (Binoculars), directional memorization (BiScope)
Trained classifiers: GPTZero, Originality.ai, Turnitin, Copyleaks — achieve 65-96% on unedited AI text
Watermarking: Vocabulary partitioning, SynthID tournament sampling
Stylometric: Type-token ratio, stop word count, hapax legomenon rate
What defeats detection: Paraphrasing (reduces by 87-99%), simple modifications, human editing, nucleus sampling
Theoretical limit: Detection converges toward random chance as models improve (Sadasivan et al.)

For full detection science detail, see detection-science.md.

Model-Specific Signatures

Model	Key Tells
ChatGPT	Formal, clinical; heavy em-dashes (8/573 words); overuses "delve," "align," "noteworthy"; dry, robotic
Gemini	Conversational, explanatory; prefers simple language; no em-dash overuse
Claude	More natural and literary; minimal em-dashes (2/948 words); tonal flexibility; occasionally generates fiction unprompted
Deepseek	Heavy em-dashes (9/555 words); similar to ChatGPT structurally

Reference Files

Detailed research backing these principles:

File	Contents	Consult When
ai-tells-and-fingerprints.md	Vocabulary fingerprints, structural/rhetorical/tonal patterns, statistical signatures, model-specific signatures	Reviewing text for AI tells, understanding what detectors look for
humanizing-playbook.md	Four-layer editing system, voice development, 7 craft fundamentals, professional workflows, what editors look for	Editing AI-assisted text, developing writing voice, understanding editorial standards
prompting-and-workflow.md	Prompt engineering techniques (tiered), workflow designs, tool-specific strategies, style library building	Setting up writing prompts, designing hybrid workflows, building personal style libraries
detection-science.md	Detection methods, accuracy data, evasion methods, arms race, theoretical limits, non-native speaker bias	Understanding how detection works, what statistical properties matter, accuracy limitations

Never Do

Use kill-list vocabulary
Use em-dashes or en-dashes (use commas, periods, parentheses, or colons instead)
Add emojis unless the user explicitly requests them
Write uniform paragraph lengths
Start consecutive sentences with the same structure
Hedge when a direct statement serves better
Treat all subjects with equal professional distance
Explain what you're about to explain before explaining it
End with a conclusion that repeats the introduction
Produce text that could fit any prompt equally well