humanize

name	humanize
description	Scan for AI writing patterns in markdown, docs, comments, and user-facing strings. Detects 24 cataloged AI-writing tells across content, language, style, communication, and filler categories. Triggers: 'check prose', 'AI writing', 'humanize', or /axiom:humanize. Do NOT use for code quality — use other axiom skills.
user-invokable	true
metadata	{"author":"lvlup-sw","version":"0.1.0","category":"assessment","dimensions":["prose-quality"]}

Humanize — Prose Quality Assessment

Overview

Detects signs of AI-generated writing in prose content. Based on Wikipedia's "Signs of AI writing" guide maintained by WikiProject AI Cleanup. Covers 24 patterns across 5 categories: content, language/grammar, style, communication, and filler/hedging.

Triggers

Use when:

• Reviewing documentation, README files, or user-facing copy
• Checking comments and docstrings for AI tells
• User says "humanize", "check prose", "AI writing patterns", "de-slop"

Do NOT use when:

• Reviewing code logic or architecture (use axiom:critique)
• Checking error handling (use axiom:harden)
• Running full quality audit (use axiom:audit, which includes humanize)

Process

Step 1: Scope Resolution

Determine assessment scope:

• File: Assess a single file
• Directory: Assess all prose-containing files (recursive)
• Codebase: Assess the entire project

Default file scope: *.md, *.txt, *.mdx, plus comments and user-facing strings in source files (*.ts, *.js, *.py, etc.).

Exclude: node_modules/, dist/, .git/, CHANGELOG.md, lock files, binary files, generated files.

Step 2: Deterministic Scan

Run axiom:scan with dimensions: prose-quality for the resolved scope. This executes the PQ-* check catalog against the content.

Step 3: Qualitative Assessment

Layer qualitative assessment on top of scan results:

• Read flagged sections in context -- some patterns are acceptable in certain contexts
• Check for pattern clustering (multiple AI tells in the same paragraph/section is worse than isolated occurrences)
• Assess overall tone -- does the content read as expert-written or machine-generated?
• Apply false-positive filtering per guidance in @skills/humanize/references/ai-writing-patterns.md

Step 4: Produce Findings

Output findings in standard format: @skills/backend-quality/references/findings-format.md

Each finding includes:

• dimension: "prose-quality"
• severity: HIGH | MEDIUM | LOW per @skills/humanize/references/severity-guide.md
• title: Pattern name and location
• evidence: File path and line numbers
• explanation: What pattern was detected and why it matters
• suggestion: Concrete rewrite suggestion

Error Handling

• No prose files in scope: Return "No prose content found in scope" with no findings
• Binary/generated files: Skip silently
• Large files (>10K lines): Sample first 1000 and last 1000 lines

References

• Pattern catalog: @skills/humanize/references/ai-writing-patterns.md
• Severity mapping: @skills/humanize/references/severity-guide.md
• Finding format: @skills/backend-quality/references/findings-format.md
• Dimension taxonomy: @skills/backend-quality/references/dimensions.md