Ghost Writer Documentation

Complete reference for the Ghost Protocol methodology, 5-stage pipeline, 40-point QA system, platform specs, voice profiles, detection evasion, and API integration.

1. Ghost Protocol Methodology

Ghost Writer operates under three core laws that govern every piece of content:

1. Controlled Chaos — Deliberate imperfection in every piece. Sentence fragments, unexpected metaphors, paragraphs that run long because the thought demands it. Never uniform.
2. Signature Voice — Every content type gets a specific voice profile. The engine writes as a defined persona with consistent tone, vocabulary, and structural habits.
3. Invisible Architecture — Detection evasion is baked into generation, not bolted on. The writing passes because of how it's constructed, not because of post-processing.

2. 5-Stage Pipeline

Every piece of content flows through five sequential stages:

1. Profile — Load voice profile, set perplexity/burstiness targets, identify platform constraints.
2. Writer — GPT-5.4 generation with Ghost Protocol system prompt.
3. QA Engine — 40-point check across 10 blocks.
4. Adapter — Format for target platform (18 supported).
5. Polish — Human-pass simulation with 2–3 small edits.

3. The 40-Point QA System

Every piece is validated against 40 checks organized into 10 blocks. Hard checks must pass; soft checks inform quality scoring.

Block A: Statistical (#1–7)

Block B: Classifier Resistance (#8–12)

Block C: Linguistic (#13–18)

Block D: Watermark (#19–20)

Block E: Scoring (#21–25)

Confidence targeting, sentence-level clean, plagiarism check, anti-humanizer resistance, language authenticity.

Block F: Bias (#26–28)

Non-native bias clear, domain patterns validated, length optimization.

Block G: Adversarial (#29–31)

Pattern diversity, translation proof, authorship consistency.

Block H: Infrastructure (#32–34)

Multi-detector validation, plain text normalization, platform compliance.

Block I: Evaluation (#35–37)

Third-party benchmark, FPR exploitation clear, AI-assisted classification.

Block J: Governance (#38–40)

Disclosure compliance, audit trail, provenance proof.

4. Platform Specs

All 18 supported platforms with character limits, truncation rules, format, and best practices.

5. Voice Profiles

Four built-in profiles plus custom import:

Voice Import

Paste 2–5 writing samples → engine analyzes sentence length, vocabulary, structure, tone → extracts fingerprint → saves as custom profile.

6. Detection Methodology

How each detector works and how Ghost Writer defeats it:

GPTZero

Uses perplexity + burstiness + 7 indicators. We target perplexity >30, burstiness stdev >5, inject fragments and unexpected word choices.

Pangram v3

Classifies as AI / AI-Assisted / Human with model attribution. We vary token distribution patterns and use voice-specific vocabulary.

Originality.ai v2

99% accuracy, catches paraphrasing. We generate with human patterns from scratch rather than paraphrasing AI text.

7. API Reference

POST /api/writing-agent — Generate content

// Request
{
  "platform": "linkedin",
  "voice": "john-williams",
  "topic": "Why PMax works better with brand campaigns",
  "context": "B2B SaaS audience",
  "length": "500"
}

// Response
{
  "content": "...",
  "platform": "linkedin",
  "checks": { "passed": 38, "failed": 2, "details": [...] }
}

POST /api/writing-agent-check — Check existing text

// Request
{
  "text": "Your existing content to analyze..."
}

// Response
{
  "readability": { "fleschKincaid": 65, "grade": "8th grade", ... },
  "tone": ["confident", "direct"],
  "aiScore": 0.12,
  "suggestions": [...]
}

POST /api/writing-agent-voice — Voice profile management

// Request (analyze samples)
{
  "action": "analyze",
  "samples": ["Sample 1...", "Sample 2...", "Sample 3..."]
}

// Response
{
  "fingerprint": {
    "sentenceLength": { "mean": 16, "stdev": 9, ... },
    "vocabulary": { "ttr": 0.52, "domainTerms": [...], ... }
  }
}

8. Research & Citations

• Mitchell et al. (2023) — DetectGPT
• Bao et al. (2023) — Fast-DetectGPT
• Liang et al. (2023) — GPT Detectors Biased Against Non-Native Writers
• Hans et al. (2024) — Binoculars
• MASH (2026) — Style Humanization
• AuthorMist (2025) — RL-Based Evasion