| name | mimic-my-writing |
| description | Mimic my writing -- force AI to write like you do. Extract a quantitative voice fingerprint from sample text (sentence burstiness, vocabulary anchors, signature phrases, punctuation rate, quirks) and use it as a hard constraint when drafting. Use when the user asks to write in their voice, write like them, mimic a specific author, sound like a sample, match a writing style from examples, or de-AI a draft against a personal style. Also use when asked to grade how well a draft matches an author's voice. |
Mimic My Writing
Force any draft to sound like a specific human by extracting a measurable fingerprint from their samples and writing to those constraints. Stops the model from defaulting to LLM voice.
Quick start
samples/<author-slug>/
scripts/analyze_voice.py samples/<author-slug>/
The script prints a JSON report. Read it, then draft.
The fingerprint, in one breath
The analyzer measures rhythm (sentence-length burstiness, fragment share), vocabulary (TTR, top content words, profanity rate, AI-filler hits), punctuation (em-dash, exclaim, semicolon rates), contractions, signature 2- and 3-grams, sentence openers, and quirks (all-caps emphasis, rhetorical Q+A, "fuck" as intensifier, etc).
Each metric maps to a concrete writing rule. See references/fingerprint.md for the translation table.
Workflow
- Get samples. Need 2-4 pieces, ~1k+ words total. Ask if not provided. Save under
samples/<author-slug>/.
- Run the analyzer.
scripts/analyze_voice.py samples/<author-slug>/. Takes <1 second, stdlib only.
- Translate the JSON using
references/fingerprint.md. Write out the constraints (sentence rhythm targets, vocab anchors, must-use signature phrases, quirks to preserve).
- Draft against those constraints.
- Self-audit with
references/anti-ai-tells.md -- rip out LLM defaults (delve, leverage, tricolon stacks, etc).
- Deliver.
Detailed variants (cold mimic, warm mimic, hybrid voice, critique mode, sample organization) live in references/workflow.md.
Non-negotiables
- Never paraphrase signature phrases. They're the author's verbal tics. Drop 2-3 of them into any mimic draft verbatim.
- Match burstiness, don't average it. If the author swings between 2-word fragments and 30-word runners, do the same. Don't write a uniform-length string of sentences.
- Match the profanity rate. Diluting it sanitizes the voice; inflating it caricatures. Within ±50% of the measured rate.
- Reuse anchor words; do not synonym-cycle. If the author says "call" 10x, you say "call" -- not "conversation, dialogue, exchange."
- Honor every quirk in the
quirks array. They're flags for hard constraints, not suggestions.
When to load which reference
- Translating JSON metrics into writing rules →
references/fingerprint.md
- About to ship a draft, doing the AI-tell sweep →
references/anti-ai-tells.md
- Edge case (no samples, hybrid topic, critique mode, sample layout) →
references/workflow.md
Failure modes to avoid
- Mimicking the topic, not the voice. If samples are about sales and the user asks for a poem, the rhythm/vocab quirks still apply. Topic ≠ voice.
- Surface mimicry only. Copying a few catchphrases without matching sentence rhythm reads like a bad SNL impression. Stats first, vocab second.
- Bleeding authors. If
samples/ has multiple authors, only analyze the requested one's folder. Don't mix fingerprints unless explicitly asked for a fusion.
- Outdated samples. If the user provides a new sample, drop it in and re-run the analyzer. Don't trust an old fingerprint from prior session memory.
- Skipping the script. Eyeballing samples and "writing in their voice" without the fingerprint is how you end up with delve+leverage soup. Run it every time.
Example mini-fingerprint readout (Chad)
From samples/chad/:
- burstiness 0.82, fragment share 30%, long-sentence share 18% → swing hard between one-liners and 30+ word runs
- TTR 0.48 → moderate vocabulary; repeat anchor nouns
- profanity 7.7/1k words → curse freely; "fuck" as intensifier confirmed
- em-dash 8.7/1k chars → dashes are structural, not ornamental
- ALL-CAPS emphasis confirmed → use it on intensifier words (ENTIRE, MOMENT, NOT)
- Signature phrases: "ask for the money", "you can't", "that's the", "i've seen"
- Rhetorical question + self-answer → confirmed move
A draft that hits those numbers reads like Chad. One that doesn't reads like ChatGPT cosplaying Chad.
