@veldica/prose-linter

A deterministic style-contract library and AI-writing style audit for high-quality prose.

@veldica/prose-linter provides the engine for evaluating text against explicit structural targets (sentence length, lexical density, scannability) and identifies common "AI-style" markers. It is designed to be used programmatically in CLIs, editors, and agent feedback loops.

Features

• Deterministic Style Contracts: Define targets for word counts, complexity, and structural variety.
• AI Marker Inventory: Detect stock phrases, assistant residue, and repetitive structural patterns common in LLM output.
• Actionable Revision Levers: Get prioritized advice on how to fix style violations.
• Readability Integration: Built-in support for Gunning-Fog, Flesch-Kincaid, and more.

Installation

npm install @veldica/prose-linter

API Usage

Linting Text

The lintText function is the primary entry point for evaluating text against a profile.

import { lintText } from '@veldica/prose-linter';

const profile = {
  targets: {
    sentence_metrics: {
      avg_words_per_sentence: { value: 20, operator: 'at_most' },
      sentence_length_stddev: { value: 5, operator: 'at_least' }
    }
  },
  track_ai_patterns: true,
  track_words: ["revolutionary", "leverage"] // Track specific words
};

const result = lintText("Your prose goes here...", profile);

console.log(result.summary.score); // Fit score out of 100
console.log(result.violations);    // Array of failed CheckResults

Inventory AI Markers

Audit a document specifically for "AI-ish" patterns without full structural linting.

import { inventoryMarkers } from '@veldica/prose-linter';

const analysis = inventoryMarkers(text, { track_ai_patterns: true });

console.log(analysis.style_band); // "low" | "moderate" | "high" | "very_high"
console.log(analysis.matches);    // Array of matches with line/column locations

Content Integrity Comparison

Check if a rewrite preserved critical factual anchors (names, dates, URLs, version numbers) from the original text.

import { compareIntegrity } from '@veldica/prose-linter';

const original = "The system supports AES-256 encryption. Visit https://example.com for more.";
const revised = "The system uses encryption. Go to our site.";

const report = compareIntegrity(original, revised, {
  aliases: {
    "AES-256": ["encryption"]
  }
});

console.log(report.integrity_score); // 0-100 score
console.log(report.anchor_recall);    // Ratio of anchors preserved
console.log(report.anchors);          // List of added, dropped, and shifted anchors
console.log(report.polarity_shift_count); // Number of anchors whose negation changed

Available options:

• aliases: Map of canonical terms to their allowed variations.
• track_fiction: Enable fiction mode (treats more words as proper nouns).

Using Pre-defined Templates

The library includes a collection of high-quality templates for common writing styles.

import { lintText, CATALOG_TEMPLATES } from '@veldica/prose-linter';

const result = lintText(text, CATALOG_TEMPLATES.thriller_fast_paced);

Available templates:

• thriller_fast_paced: Optimized for action with short, punchy sentences.
• academic_rigorous: High complexity, formal vocabulary, and rigorous structure.
• technical_docs: Clear, instructional, and highly scannable.
• business_direct: Professional and concise for quick decision making.

Style Profile Configuration

A StyleProfile allows you to set specific targets across several metric groups:

Group	Key Metrics
counts	word_count, sentence_count, paragraph_count
sentence_metrics	avg_words_per_sentence, sentence_length_stddev, max_words_per_sentence, sentence_length_p95
paragraph_metrics	avg_words_per_paragraph, max_words_per_paragraph, percent_paragraphs_over_100_words
lexical_metrics	complex_word_ratio, unique_word_count, repetition_ratio
scannability_metrics	heading_density, list_density, paragraph_scannability_score
fiction_metrics	dialogue_ratio, scene_density_proxy, sensory_term_density
formulas	flesch_kincaid_grade_level, gunning_fog, consensus_grade
word_tracking_metrics	Counts for words defined in track_words

Result Structure

The FullLintResult object returned by lintText includes:

• summary: High-level compliance numbers (score, counts).
• checks: All checks performed (passed, failed, and skipped).
• violations: Specifically failed checks.
• skipped_checks: Checks that couldn't be run due to missing data.
• ai_analysis: Detailed report on AI-style markers.
• revision_levers: Ranked suggestions for improvement.
  • id: Unique identifier for the lever (e.g., "shorten_long_sentences").
  • label: Human-readable name.
  • score: Priority score (0-100).
  • explanation: Detailed advice.
  • evidence: Specific violations triggering this lever.

License

MIT

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Built by Veldica — see the prose-linter product page.