v2.0.0 to v3.0.0

---

name: synthesis-clean-text

description: "Enforce clean-text and no-hidden-marker requirements, audit inspectable characters and provenance, and state the verification boundary for statistical text marks. Use when generating clean text, checking hidden characters, addressing watermark concerns, or selecting a controlled generation path."

license: "CC0-1.0"

user-invocable: false

depends_on: []

metadata:

author: "Rajiv Pant"

source_repo: "github.com/synthesisengineering/synthesis-skills"

source_type: "public"

---

# Clean Text

Set and audit the requirement that generated text contain no hidden markers, invisible-character identifiers, or intentional statistical provenance signals. Report separately what the active generation path and available checks can actually establish.

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 enforces a watermark-free, fingerprint-free output policy and audits the parts of that policy that are technically inspectable. For detecting model-shaped patterns in finished prose, see the companion [`synthesis-content-quality`](../synthesis-content-quality/SKILL.md). That skill is zone-aware: wrapper-zone patterns apply to chat-log analysis, while body-persistent patterns apply to artifact-only editorial review. Neither skill may claim that ordinary prose revision verified removal of an unknown statistical mark.

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.