# Humanize > Detects and removes AI-generated writing patterns while preserving meaning and facts. Triggers on: "humanize text", "make this sound human", "remove AI patterns", "rewrite to sound natural", "make this less AI", "de-slop this", "not sound like ChatGPT", "human pass". - **Type:** Skill - **Install:** `agentstack add skill-mathews-tom-armory-humanize` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [Mathews-Tom](https://agentstack.voostack.com/s/mathews-tom) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [Mathews-Tom](https://github.com/Mathews-Tom) - **Source:** https://github.com/Mathews-Tom/armory/tree/main/skills/humanize - **Website:** https://mathews-tom.github.io/armory/ ## Install ```sh agentstack add skill-mathews-tom-armory-humanize ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Humanize: AI Pattern Detection and Removal Remove AI-generated writing patterns from text. Produce natural, human-sounding output that preserves meaning. This is not a generic rewriter. It targets specific, documented AI-writing patterns catalogued by Wikipedia's WikiProject AI Cleanup from thousands of observed instances. ## Workflow Five phases. Each phase has a clear input, transformation, and output. Do not skip phases. ### Phase 1: Detection Scan Read the input text. Load `references/detection-patterns.md`. Scan for two categories of signals: **A. Lexical patterns** (the 24 catalogued AI-writing patterns): | Category | Patterns | Priority | | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- | | Content inflation | Significance puffing, notability claims, superficial -ing analyses, promotional language, vague attributions, formulaic challenges sections | HIGH — loudest AI tells | | Vocabulary | AI-frequency words, copula avoidance, filler phrases, excessive hedging | HIGH — statistically detectable | | Structure | Rule of three, negative parallelisms, elegant variation, false ranges, inline-header lists | MEDIUM — structural fingerprints | | Style | Em dash overuse, boldface overuse, title case headings, emoji decoration, curly quotes | MEDIUM — formatting tells | | Communication | Chatbot artifacts, knowledge-cutoff disclaimers, sycophantic tone, generic conclusions | LOW — obvious, usually caught by author | **B. Statistical regularity signals** (see `references/statistical-signals.md`): | Signal | What to look for | | ------------------------------- | ---------------------------------------------------------------------------------------------- | | Sentence length uniformity | Sentences clustering within a narrow word-count range | | Low clause density variation | Every sentence has the same number of clauses | | Flat information density | Every sentence carries roughly the same amount of detail | | High-frequency phrase templates | Stock collocations and common bigrams/trigrams dominating the text | | Excessive transition markers | Formal connectives appearing more than 8 per 1,000 words | | Structural symmetry | Paragraphs and sentences following balanced, mirror-like patterns | | Uniform inter-sentence cohesion | Every sentence tightly follows the previous with no topic shifts or digressions | | Generic function word usage | Connectors and prepositions used in textbook-standard distribution with no personal tendencies | Output a detection report using the detection report template (see Output Format). **Instance severity rating:** | Severity | Criteria | | -------- | --------------------------------------------------------------------------------------------------------------- | | HIGH | 3+ patterns co-occurring in a single paragraph, or any paragraph saturated with AI vocabulary (5+ signal words) | | MEDIUM | 1-2 patterns in a paragraph, or a statistical signal present across 3+ consecutive sentences | | LOW | Isolated single instance of any pattern, or a borderline statistical signal | ### Phase 2: Structural Rewrite Transform document structure to break AI-typical organization: - Convert uniform paragraph lengths to varied blocks - Merge or split sentences to break rhythmic uniformity - Reorder clauses where meaning permits - Convert formulaic list structures to narrative where appropriate - Remove tripartite constructions unless the content genuinely has three parts Do not change factual content. Do not add information. Do not remove cited sources, data, or technical terms. ### Phase 3: Vocabulary and Style Pass Apply pattern-specific rewrites from the detection report: - Replace AI-frequency vocabulary with natural alternatives - Restore simple copulas (is/are/has) where the text uses elaborate substitutes - Remove filler phrases and excessive hedging - Cut promotional language and significance inflation - Replace vague attributions with specific ones (or remove if no source exists) Load the appropriate style profile from `references/style-guide.md` based on the target domain. Apply domain-specific voice calibration. ### Phase 4: Entropy and Variation Human writing has burstiness — irregular rhythm, varied sentence lengths, uneven information density. AI text is statistically smooth. This phase breaks that smoothness. Load `references/statistical-signals.md` for target ranges. Apply: - **Sentence length variance**: mix short declarative with longer explanatory. Target visible variance across any 5-sentence window. - **Clause density variation**: alternate simple sentences (one clause) with compound/complex (2-3 clauses). Do not settle on a uniform clause count. - **Information density variation**: let some sentences carry heavy detail while others are light — a summary statement, a reaction, a pivot. Uniform density reads as generated. - **Phrase template breaking**: replace stock collocations with specific phrasings. "Play a role in" -> name the specific action. "In terms of" -> delete or restructure. - **Inter-sentence cohesion variation**: not every sentence should tightly follow the previous. Allow small topic expansions, brief asides, or contextual jumps that a thinking human would make. - **Function word personalization**: vary connector usage. Use "but" in one place, "still" in another, nothing in a third. Do not default to the same conjunction pattern throughout. - **Paragraph length variance**: mix single-sentence paragraphs with 4-5 sentence blocks. - **Controlled imperfection**: fragments at impact positions, parenthetical asides, concessive turns. Sparingly — seasoning, not structure. ### Phase 5: Validation and Output Two checks before delivering: **Semantic check:** Compare rewrite against original. Every factual claim, data point, argument, and technical term in the original must be present in the rewrite. If anything was lost, restore it. **Self-audit:** Ask internally: "What still sounds AI-generated about this text?" If residual patterns remain, fix them. One pass only — do not loop indefinitely. Output the final text followed by a brief changes summary. ## Output Format ### Full Rewrite / Targeted Fix / Style Shift ``` [Humanized text] --- Changes: [2-4 bullet summary of what was changed and why] Patterns detected: [list of pattern numbers/names found] Domain: [detected or specified domain] ``` For short texts (under 100 words), skip the changes summary unless the user requests it. ### Detection Only ``` ## Detection Report **Domain:** [detected or specified] **Overall severity:** [HIGH / MEDIUM / LOW] **Patterns found:** [count] ### Findings | Location | Pattern | Severity | Evidence | |----------|---------|----------|----------| | Para 1 | #7 AI vocabulary | HIGH | "delve", "intricate", "pivotal" in same sentence | | Para 2 | #8 Copula avoidance | MEDIUM | "serves as" instead of "is" | | Para 1-4 | Sentence length uniformity | MEDIUM | All sentences 18-22 words, SD < 3 | | ... | ... | ... | ... | ### Statistical Signals | Signal | Status | Detail | |--------|--------|--------| | Sentence length variance | FLAG | SD ~3 words (human typical: 7-15) | | Transition frequency | OK | 5 per 1,000 words | | ... | ... | ... | ### Summary [1-2 sentences: overall assessment and highest-priority patterns to fix first] ``` ## Reference Files | File | Purpose | Load When | | ------------------------------------ | ---------------------------------------------------- | ------------------------------------ | | `references/detection-patterns.md` | 24 AI-writing patterns with examples | Always (Phase 1) | | `references/statistical-signals.md` | 12 statistical regularity signals with target ranges | Phase 1 (scan) and Phase 4 (targets) | | `references/style-guide.md` | Domain-specific voice profiles and calibration rules | Phase 3 (match to domain) | | `references/transformation-rules.md` | Structural rewrite strategies and entropy techniques | Phase 2 and Phase 4 | | `examples/academic.md` | Before/after pairs for academic writing | When domain is academic | | `examples/blog.md` | Before/after pairs for blog/casual writing | When domain is blog or social | | `examples/professional.md` | Before/after pairs for professional/business writing | When domain is professional | ## Domain Detection If the user does not specify a domain, infer from: 1. Vocabulary density and jargon type 2. Citation patterns 3. Sentence complexity 4. Register (formal/informal markers) Default to **professional** if ambiguous. Supported domains: `academic`, `technical`, `blog`, `social`, `professional`, `marketing` ## Behavioral Constraints 1. **Never fabricate.** Do not add facts, citations, quotes, statistics, or claims not in the original. 2. **Never remove data.** Numbers, dates, names, URLs, and cited sources must survive the rewrite. 3. **Preserve argument structure.** If the original makes points A, B, C in that order with that logic, the rewrite must preserve the logical flow. 4. **Do not over-humanize.** Some text is meant to be neutral and informational. A technical specification does not need personality. Match the appropriate register. 5. **Respect code blocks and structured data.** Do not humanize code, tables, JSON, YAML, or any structured/machine-readable content. Pass these through unchanged. 6. **One pass through the pipeline.** Do not run the 5-phase pipeline recursively. If the output still has tells after Phase 5, note them in the changes summary rather than looping. ## Scope Modes | Mode | Trigger | Behavior | | ------------------ | -------------------------------------------------------------- | ------------------------------------------------------------ | | **Full rewrite** | "humanize this", "rewrite naturally" | Run all 5 phases | | **Detection only** | "check for AI patterns", "does this sound AI" | Run Phase 1 only, output detection report | | **Targeted fix** | "fix the AI-sounding parts", "just clean up the obvious stuff" | Run Phase 1, then apply fixes only to HIGH-priority patterns | | **Style shift** | "make this more casual/academic/professional" | Run Phases 3-4 with specified domain profile | ## Error Handling | Problem | Cause | Resolution | | -------------------------------------- | --------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Input under 20 words | Insufficient signal for pattern detection | Report: "Text too short for reliable pattern detection." Apply vocabulary fixes only (Phase 3) if obvious patterns are present. Skip statistical signal analysis. | | Input is entirely code/structured data | No prose to humanize | Report: "Input is structured data — no humanization applicable." Return input unchanged. | | Mixed human + AI text | Partial AI generation or human-edited AI output | Run Phase 1 on full text. Flag only paragraphs/sections with detected patterns. Apply Phases 2-4 selectively to flagged sections. Leave clean sections untouched. | | Domain ambiguous after detection | Input mixes registers (e.g., academic citations in a blog post) | Default to **professional**. Note the ambiguity in the output: "Domain defaulted to professional — specify if another profile is preferred." | | Semantic drift detected in Phase 5 | Rewrite altered meaning during structural/vocabulary changes | Restore the drifted factual claim from the original. Do not re-run the full pipeline. Note the restoration in the changes summary. | | Input contains fabricated citations | Original text has hallucinated sources | Not detectable — this skill humanizes style, not factual accuracy. Pass through unchanged. Note in limitations if the user asks about accuracy. | | All patterns are LOW severity | Text is mostly human-written with minor tells | In targeted fix mode, report findings but recommend no changes. In full rewrite mode, apply light-touch fixes only — do not over-edit clean text. | ## Integration Point Other writing skills can import `references/detection-patterns.md` as a pattern library for their own anti-pattern sweeps. The detection patterns are the shared asset; the pipeline is this skill's domain. ## Limitations - Cannot verify factual accuracy of the original text. Garbage in, humanized garbage out. - Effectiveness depends on input length. Very short texts (under 20 words) have insufficient signal for pattern detection. - Style profiles are guidelines, not voice cloning. The output will sound natural but will not match a specific author's voice without additional calibration. - Does not interact with external AI-detection APIs. Assessment is heuristic, not benchmark-verified. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [Mathews-Tom](https://github.com/Mathews-Tom) - **Source:** [Mathews-Tom/armory](https://github.com/Mathews-Tom/armory) - **License:** MIT - **Homepage:** https://mathews-tom.github.io/armory/ Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-mathews-tom-armory-humanize - Seller: https://agentstack.voostack.com/s/mathews-tom - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.