# How Humanizer Uses Voice Matching When You Provide a Writing Sample > Discover how Humanizer uses voice matching with your writing sample to capture your unique authorial style. Learn how it analyzes sentence length, punctuation, and word choice for a personalized rewrite. - Repository: [Siqi Chen/humanizer](https://github.com/blader/humanizer) - Tags: how-to-guide - Published: 2026-09-13 --- **Humanizer analyzes stylistic elements from your writing sample—including sentence length, punctuation habits, and word choice—to establish a voice baseline that overrides generic editing rules, ensuring the rewrite mirrors your unique authorial style.** The `blader/humanizer` repository provides an AI-powered text transformation tool that goes beyond standard grammar correction. When you supply a writing sample, the system performs **voice matching**, a process that captures your specific linguistic fingerprint to maintain consistency across generated content. ## How Voice Matching Works in Humanizer Voice matching operates by examining the structural and stylistic DNA of your provided text. Unlike generic AI rewrites that apply universal rules, Humanizer treats your sample as the authoritative standard for the output. ### Analyzing Stylistic Fingerprints When you provide a writing sample, Humanizer extracts several key characteristics to build a voice profile: - **Sentence length and rhythm** – Short, punchy constructions versus elaborate, flowing syntax - **Word choice preferences** – Specific vocabulary patterns and formality levels - **Punctuation patterns** – Distinctive use of em-dashes, semicolons, or comma placement - **Opening and transition styles** – How you typically begin sentences and connect ideas These extracted features become the constraints for the rewrite generation process. ### Overriding Default Pattern Rules Normally, Humanizer applies standard pattern-based rules defined in the skill configuration. However, as documented in [`SKILL.md`](https://github.com/blader/humanizer/blob/main/SKILL.md) at line 40, **when a writing sample is present, voice matching overrides these generic rules**. For example, while the default configuration in section 8 might standardize dash usage across all outputs, your sample's specific dash styling takes precedence. The system prioritizes preserving your original punctuation habits over applying generic editorial standards. ## Providing a Writing Sample to Humanizer You can invoke voice matching by including your reference text when calling the Humanizer skill. The examples below demonstrate how different samples produce distinct outputs. ### Preserving Punctuation Style When you want to maintain specific punctuation habits like em-dash spacing: ```text User sample: "The new feature — announced yesterday — improves performance." Humanizer's rewrite (voice-matched): "The new feature — announced yesterday — improves performance." ``` The system recognizes the spaced em-dash pattern from your sample and preserves it, rather than converting to standard closed em-dashes or commas. ### Matching Sentence Rhythm For content with distinctive rhythmic patterns: ```text User sample: "Quick insights, bold moves, lasting impact." Humanizer's rewrite (voice-matched): "Quick insights, bold moves, lasting impact." ``` Humanizer maintains the parallel structure and staccato cadence, ensuring the rewrite feels like it came from the same author. ### Fallback to Genre-Based Voice If you do not provide a sample, Humanizer defaults to genre-based voice selection as defined in the Voice section of [`SKILL.md`](https://github.com/blader/humanizer/blob/main/SKILL.md): ```text User request (no sample): "Explain how caching works in Next.js." Humanizer's rewrite (genre-based): "Next.js caches data at multiple layers, including request memoization, the data cache, and the router cache." ``` Without sample-based voice matching, the system selects a technical blog voice from predefined genres (blog, technical, legal, etc.). ## Technical Implementation The voice matching functionality spans several key files in the repository. ### Skill Definition The core logic resides in [`SKILL.md`](https://github.com/blader/humanizer/blob/main/SKILL.md), which defines the Humanizer skill's architecture. The Voice section (around line 40) explicitly describes how writing samples trigger voice matching mode and how this mode takes precedence over default pattern handlers. ### Usage Integration The [`README.md`](https://github.com/blader/humanizer/blob/main/README.md) documents how end users invoke Humanizer with writing samples, explaining the interface for passing reference text to the skill during execution. ### Agent Configuration In [`agents/openai.yaml`](https://github.com/blader/humanizer/blob/main/agents/openai.yaml), the packaging configuration for OpenAI-compatible agents shows how the writing sample parameter is passed through the agent framework to the underlying Humanizer processing engine. ## Summary - **Voice matching** analyzes your writing sample's stylistic DNA—sentence structure, punctuation, and vocabulary—to create a rewrite baseline. - When active, voice matching **overrides default pattern rules** (such as standard dash handling) to preserve your unique style. - Without a sample, Humanizer **falls back to genre selection** (blog, technical, legal) from predefined voice categories. - The functionality is configured in [`SKILL.md`](https://github.com/blader/humanizer/blob/main/SKILL.md), with usage documented in [`README.md`](https://github.com/blader/humanizer/blob/main/README.md) and agent integration handled in [`agents/openai.yaml`](https://github.com/blader/humanizer/blob/main/agents/openai.yaml). ## Frequently Asked Questions ### How does Humanizer analyze my writing sample for voice matching? Humanizer examines specific stylistic markers including sentence length distribution, word choice patterns, punctuation habits (particularly em-dash and comma usage), and transition structures. According to the [`SKILL.md`](https://github.com/blader/humanizer/blob/main/SKILL.md) source code, these characteristics form a baseline profile that constrains the rewrite generation, ensuring the output mirrors your original voice rather than applying generic editorial standards. ### What happens if I don't provide a writing sample? If no sample is supplied, Humanizer defaults to genre-based voice selection. As implemented in the `blader/humanizer` repository, the system selects from predefined categories such as blog, technical, or legal writing styles. This fallback mechanism applies standard pattern-based rules rather than personalized voice matching. ### Can voice matching override specific grammar rules? Yes. The voice matching process explicitly overrides generic pattern-based rules when they conflict with your sample's style. For instance, if your sample uses spaced em-dashes (like "word — word") while the default rules specify closed em-dashes, the sample's styling takes precedence. This override behavior is documented in the Voice section of [`SKILL.md`](https://github.com/blader/humanizer/blob/main/SKILL.md). ### Where is the voice matching logic configured in the repository? The primary configuration resides in [`SKILL.md`](https://github.com/blader/humanizer/blob/main/SKILL.md), specifically in the Voice section around line 40, which defines how writing samples trigger voice matching mode. The [`README.md`](https://github.com/blader/humanizer/blob/main/README.md) provides user-facing documentation for supplying samples, while [`agents/openai.yaml`](https://github.com/blader/humanizer/blob/main/agents/openai.yaml) handles the technical interface for passing samples through OpenAI-compatible agent frameworks.