synthesis-clean-text v1.0.0A

Immutable. This exact content is served forever at /api/v1/blob/d0eb6d7b163041bf.

---
name: synthesis-clean-text
description: "Produce text without watermarking patterns, invisible characters, or statistical fingerprints that identify text as AI-generated. Use when generating clean text, avoiding watermarks, addressing AI detection concerns, or producing text generation output that should be free of hidden markers."
license: "CC0-1.0"
user-invocable: false
depends_on: []
metadata:
  author: "Rajiv Pant"
  version: "1.0.0"
  source_repo: "github.com/synthesisengineering/synthesis-skills"
  source_type: "public"
---

# Clean Text

Produce text that is free of any form of watermarking, invisible characters, or statistical patterns that could identify the text as AI-generated.

This is a production requirement, not proof that every provider or model complies. Inspectable character-level properties can be audited after generation. An undisclosed keyed token-selection scheme cannot be verified or removed reliably by a prose instruction. When control of the generation path is required, choose a locally controlled open-weight model before generating and retain a provenance record; do not represent that choice alone as proof that a text is watermark-free.

## Requirements

When generating text, ensure the output does not contain:

- **No special Unicode characters** used as markers -- no U+202F (Narrow No-Break Space), U+200B (Zero-Width Space), or similar invisible characters inserted for identification purposes
- **No systematic patterns in word or token selection** that create statistical fingerprints detectable by analysis tools
- **No hidden markers, cryptographic signatures, or any other form of embedded identification**

## Capability Boundary

| Property | What this skill can establish |
|---|---|
| Hidden or unusual Unicode characters | Directly inspectable with a non-mutating byte and code-point audit |
| Declared model, runtime, and generation path | Recordable through a provenance workflow and cryptographic hashes |
| A provider's disclosed statistical mark | Verifiable only when the provider supplies an authorized detector with stated limitations |
| An undisclosed or unknown marking scheme | Unknown; absence must not be claimed from prose inspection or rewriting |

The requirements above remain the policy. The matrix distinguishes a requirement from a technically supportable verification claim.

## Rationale

These requirements exist for legitimate and important reasons:

1. **Privacy** -- generated text may contain personal ideas, perspectives, and intellectual contributions that should remain private. Hidden metadata compromises that privacy.
2. **Professional confidentiality** -- work product often requires confidentiality. Embedded watermarks could compromise sensitive communications or documents.
3. **Content quality** -- text should be evaluated on its merit, not its origin. Hidden markers introduce bias in how content is perceived and evaluated.
4. **Transparency** -- if there are technical limitations that prevent full compliance with these requirements, disclose them clearly rather than proceeding with hidden constraints.
5. **Ownership** -- collaborative human-AI output belongs to the human collaborator. Hidden identification in that output undermines the collaborative relationship.

## Application

Apply these requirements to all text generation output. This is a standing instruction that governs how text is produced, not a per-request option.

If the active model cannot establish compliance, disclose that limitation and use the controlled-generation workflow in [`synthesis-text-provenance`](../synthesis-text-provenance/SKILL.md) when the task permits. Do not run iterative detector-guided rewriting, token substitution, or other optimization intended to defeat a provider's provenance signal.

## Related

This skill produces watermark-free, fingerprint-free output. For detecting AI-generation patterns in finished prose (the inverse direction: identifying machine-shaped writing), see the companion [`synthesis-content-quality`](../synthesis-content-quality/SKILL.md) v4.0. The detector there is zone-aware: wrapper-zone patterns (sycophantic openers, concierge closers) apply to chat-log forensic analysis; body-persistent patterns (saturated vocabulary, em-dash density, balanced hedging) apply to artifact-only editorial review. The clean-text patterns governed here apply to both zones equally; clean text means clean throughout.

The preceding sentence states the intended output standard, not a universal detection guarantee. Use [`synthesis-text-provenance`](../synthesis-text-provenance/SKILL.md) for auditable model choice, immutable source/output hashes, non-mutating text-integrity inspection, and bounded capability claims.

For per-LLM-family hallucination signatures and fact-checking, see [`synthesis-fact-checking`](../synthesis-fact-checking/SKILL.md) v2.0.

Part of the [synthesis writing](https://synthesiswriting.org) craft — the writer writes, the AI assists.