Claude Text Lab

Fact-preserving regeneration of Claude-generated text, with auditable local quality gates and an honest unverified provenance verdict. No detector is claimed, and none exists publicly.

Claude Text Lab 0.1.1 is a local-first Python research toolkit for substantial, fact-preserving regeneration of Claude-generated text. It separates the source into a reviewed fact ledger, writes new prose from that ledger with a non-Claude model, checks factual invariants and wording overlap, and records the evidence in JSON.

This project is Claude text only. It does not process images, video, audio, PDFs, DOCX files, C2PA metadata, Gemini, SynthID, or visible logos.

Read this first

As of the August 13, 2026 research cutoff, Anthropic says supported Claude models embed an imperceptible watermark directly into generated text. Anthropic has not published the algorithm, detector keys, thresholds, error rates, minimum reliable length, or a public detector API. No third-party program can currently prove that a Claude text watermark was removed.

For that reason, every Claude Text Lab result keeps this verdict:

provenance_verdict: unverified

A passing local quality gate means the candidate preserved the checked facts, avoided suspicious Unicode, stayed within the configured length range, and did not reuse too much source wording. It does not mean an Anthropic detector returned a negative result.

Anthropic states that heavy editing, paraphrasing, translation, mixing with other writing, and very short passages can leave no detectable mark. Anthropic also warns that a negative detection result would not prove that Claude was never involved. See Anthropic's current marking documentation.

Related MCP server: Fact-Checker MCP Server

Choose the right command

If you only want ordinary Unicode cleanup, use sanitize. If your concern is Claude's announced model-level text mark, Unicode cleanup alone is not enough. Use rewrite, then inspect the ledger, candidate, and report manually.

How the rewrite works

flowchart TD
    A["Authorized source text"] --> B["Conservative Unicode normalization"]
    B --> C["Non-Claude fact extraction"]
    C --> D["Ledger review"]
    D --> E["Fresh prose from ledger only"]
    E --> F["Exact-value and overlap gates"]
    F --> G["Semantic verification"]
    G -->|failed review| E
    G --> H["Candidate, ledger, and JSON report"]

The writing stage never receives the original prose. It sees atomic claims, exact values, uncertainties, and format requirements from the reviewed ledger. The source is still sent to the extraction and ledger-review stages, and the selected model endpoint therefore needs permission to process it.

With default settings, a successful first candidate usually takes four model calls:

1. Extract a fact ledger from the source.

2. Review the ledger against the source.

3. Compose fresh prose from the ledger only.

4. Verify the candidate against the ledger.

If the first candidate fails, a second candidate is generated with feedback. --fast uses one extraction call and one writing call, but skips ledger review and semantic verification. Use fast mode for experiments, not important content.

Requirements

• Python 3.10 or newer.

• UTF-8 plain text input.

• A non-Claude model behind an OpenAI-compatible chat-completions endpoint for rewrite.

• Enough model quality and memory for accurate fact extraction and verification.

The core CLI, inspection, sanitation, audit, and benchmark functions use only the Python standard library. MCP is an optional dependency.

Installation

Linux and macOS

From the extracted release directory or a Git checkout:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install .
claude-text-lab --version

Expected version:

claude-text-lab 0.1.1

For editable development:

python -m pip install -e '.[dev,mcp]'

Windows PowerShell

py -m venv .venv
.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
python -m pip install .
claude-text-lab --version

If PowerShell blocks virtual-environment activation, either use the full executable path, .venv\Scripts\python.exe, or review your local execution policy before changing it.

Install the built wheel

The release bundle contains a wheel in dist/:

python -m pip install dist/claude_text_lab-0.1.1-py3-none-any.whl

The base wheel does not install MCP. Install from the project with the mcp extra when MCP is needed:

python -m pip install '.[mcp]'

Set up a local non-Claude model

Any correctly implemented OpenAI-compatible /chat/completions endpoint can work. The examples use Ollama because it can run locally. The model name is an example, not an endorsement or a claim that it is suitable for every language or domain.

1. Install Ollama.

2. Download a non-Claude model:

   ollama pull qwen3:8b

3. Confirm the model is present:

   ollama list

4. Confirm the OpenAI-compatible endpoint responds:

   curl http://127.0.0.1:11434/v1/models

Ollama documents its OpenAI-compatible endpoint at docs.ollama.com, and the qwen3:8b model is listed in the Ollama model library.

If a different local server is used, replace both --endpoint and --model. The endpoint may be a base URL such as http://127.0.0.1:1234/v1 or a complete URL ending in /chat/completions.

Complete first rewrite tutorial

1. Create an input file

Copy only text you own or are authorized to transform into source.txt. A sample is included at examples/source.txt.

The Atlas documentation release is scheduled for August 13, 2026. The public status page is https://status.example.com, and checks run every 30 minutes. The schedule remains provisional until final review.

2. Inspect the source

claude-text-lab inspect source.txt --json

Review suspicious_unicode, exotic_spaces, and protected_spans. An empty suspicious-Unicode list does not say anything about a statistical text watermark.

3. Run the full rewrite

claude-text-lab rewrite source.txt \
  -o regenerated.txt \
  --ledger facts.json \
  --report audit.json \
  --endpoint http://127.0.0.1:11434/v1 \
  --model qwen3:8b \
  --candidates 3 \
  --style "concise technical documentation" \
  --fail-on-review

The tool refuses model identifiers containing claude or anthropic. Rewriting with a supported Claude model could add a new Claude mark.

The command creates:

When output is written to a file, the JSON report is also printed to standard output. Use --report to retain a stable copy.

4. Review the ledger before trusting the candidate

Open facts.json and check that:

• Every material claim is present.

• Attribution, negation, uncertainty, and causal direction are correct.

• Names, dates, numbers, URLs, commands, quotations, and identifiers are exact.

• No instruction embedded in the source became a fact.

Then compare regenerated.txt with the source for tone and domain accuracy. The model verifier is useful, but it is not a substitute for a subject-matter reviewer.

5. Read the report correctly

The most important fields are:

6. Understand exit code 3

With --fail-on-review, exit code 3 means none of the generated candidates passed every configured gate. The best candidate and requested reports are still written so they can be diagnosed. Do not publish that candidate without resolving the reported problems.

Command tutorials

Inspect

claude-text-lab inspect source.txt --json

inspect reports document counts, protected values, suspicious Unicode, and exotic spaces. It never makes a detector claim. Use - for standard input:

printf 'Example text' | claude-text-lab inspect - --json

Sanitize Unicode

Conservative sanitation:

claude-text-lab sanitize source.txt \
  -o normalized.txt \
  --report unicode-report.json

The default mode applies NFC normalization, turns exotic spaces into ordinary spaces, removes common zero-width, bidi, tag, noncharacter, and unsafe control code points, and preserves context-sensitive joiners and variation selectors.

Aggressive mode also removes all format controls and variation selectors:

claude-text-lab sanitize source.txt -o normalized.txt --aggressive --force

Aggressive mode can damage scripts that rely on joiners or emoji variation selectors. Inspect and review the result. Neither mode removes a token-choice watermark.

Use --no-normalize-spaces to preserve exotic spaces. Existing files are protected from replacement unless --force is supplied.

Audit an existing rewrite

claude-text-lab audit source.txt regenerated.txt --json

Add exact values that must appear:

claude-text-lab audit source.txt regenerated.txt \
  --require 'August 13, 2026' \
  --require 'https://status.example.com' \
  --json

audit reports a failed gate in JSON but still exits successfully unless an input or configuration error occurs. Read quality_gate.passed; do not use the process exit code as the audit verdict.

Default gate settings are:

These defaults are engineering heuristics, not Anthropic detector thresholds.

Rewrite options

claude-text-lab rewrite --help

Use a separate verifier when factual accuracy matters and a second capable local model is available:

claude-text-lab rewrite source.txt -o regenerated.txt \
  --endpoint http://127.0.0.1:11434/v1 \
  --model qwen3:8b \
  --verifier-endpoint http://127.0.0.1:1234/v1 \
  --verifier-model local-verifier-model \
  --ledger facts.json \
  --report audit.json \
  --fail-on-review

The verifier also must be non-Claude.

Standard input and output

printf 'Authorized source text' | \
  claude-text-lab rewrite - -o - \
  --endpoint http://127.0.0.1:11434/v1 \
  --model qwen3:8b

For reproducible work, file output with --ledger and --report is preferable. Input, output, ledger, and report paths must be distinct.

Benchmark many transformations

Each non-empty JSONL line contains a source and candidate:

{"id":"case-001","source":"Original text","candidate":"Regenerated text","required_exact":["2026"]}

Run:

claude-text-lab benchmark examples/benchmark.jsonl \
  -o benchmark-report.json

Real observations from a future authoritative detector can be supplied in each record. Detector values are never guessed or generated by this project. See docs/benchmark.md.

Use a remote OpenAI-compatible endpoint

Loopback endpoints are allowed by default. A non-loopback endpoint must use HTTPS and requires explicit permission:

export MODEL_API_KEY='replace-with-a-real-secret'

claude-text-lab rewrite source.txt -o regenerated.txt \
  --endpoint https://models.example/v1 \
  --model non-claude-model \
  --api-key "$MODEL_API_KEY" \
  --allow-remote \
  --ledger facts.json \
  --report audit.json

Remote use sends the source, ledger, and candidates to that service. Check its retention, training, residency, and security terms first. The client rejects URL-embedded credentials, plain HTTP for remote hosts, and unsafe redirects.

Environment variables are available for repeatable local configuration:

CLI options take precedence where they exist. Do not commit keys to source control.

MCP tutorial

MCP is useful when another application needs to call the inspector, sanitizer, auditor, or ledger-based rewriter as structured tools. It does not improve the transformation technique and does not add a Claude detector.

Install and test the server command:

python -m pip install '.[mcp]'
claude-text-lab-mcp

The server uses standard input and output, so a terminal appears to wait. That is normal. Press Ctrl+C and let an MCP host launch it instead.

It exposes four tools:

• inspect_text_provenance

• sanitize_text_unicode

• audit_text_transformation

• regenerate_from_fact_ledger

Claude hosts are not supported

The server refuses every tool call from a Claude MCP host, including Claude Code and Claude Desktop. It reads clientInfo during initialization and returns an error when the reported host name or title contains claude or anthropic:

Claude Text Lab does not support Claude MCP hosts, including Claude Code and Claude
Desktop. Run the command-line interface instead.

The reason is the transformation itself. A Claude host receives every tool result into a Claude conversation, and Anthropic states that supported Claude output can carry a mark. Returning a non-Claude candidate into that conversation defeats the work that produced it. Use the CLI, which writes the candidate straight to disk:

claude-text-lab rewrite source.txt -o regenerated.txt --report audit.json

Non-Claude MCP hosts are unaffected. VS Code, Cursor, Cline, Roo Code, OpenCode, Windsurf, Zed, and JetBrains all connect normally.

Use a generic MCP JSON configuration

MCP clients and the MCP Inspector commonly accept an mcpServers configuration:

{
  "mcpServers": {
    "claude-text-lab": {
      "type": "stdio",
      "command": "/absolute/path/to/project/.venv/bin/claude-text-lab-mcp",
      "env": {
        "CTL_ENDPOINT": "http://127.0.0.1:11434/v1",
        "CTL_MODEL": "qwen3:8b",
        "CTL_API_KEY": "local"
      }
    }
  }
}

Windows typically uses an executable path such as C:\\absolute\\path\\.venv\\Scripts\\claude-text-lab-mcp.exe. JSON backslashes must be doubled.

The full setup, tool argument examples, Inspector workflow, client caveats, and troubleshooting are in docs/mcp.md.

Why the Claude-host refusal exists

If Claude were the MCP host, Claude could display, summarize, or rewrite the tool result after the non-Claude model returned it. Anthropic says supported Claude processing can carry a mark. Rather than leave that to a documentation warning, the server refuses those hosts outright.

The same reasoning applies to any AI host that paraphrases results into chat. When the exact non-Claude output matters, run the CLI and use regenerated.txt from disk rather than text an assistant retyped.

Python API

Inspection and audit require no optional packages:

from claude_text_lab import audit_texts, inspect_text, sanitize_unicode

source = "Example\u200b text from an authorized document."
cleaned = sanitize_unicode(source)
inspection = inspect_text(cleaned)
comparison = audit_texts(cleaned, "An authorized document contains an example.")

print(inspection.to_dict())
print(comparison.to_dict())

Run the full rewrite pipeline with any compatible client:

from claude_text_lab.core import RewriteConfig, rewrite_text
from claude_text_lab.llm import OpenAICompatibleClient

client = OpenAICompatibleClient(
    endpoint="http://127.0.0.1:11434/v1",
    model="qwen3:8b",
    api_key="local",
)

config = RewriteConfig(
    style="concise technical documentation",
    candidates=3,
    review_ledger=True,
    semantic_check=True,
)

result = rewrite_text(client, "Authorized source text.", config, verifier=client)
print(result.text)
print(result.to_dict(include_text=False))

The Python API does not automatically refuse a Claude-named custom client. The packaged CLI and MCP wrapper perform that policy check. Application developers must enforce an equivalent non-Claude model policy themselves.

Exit codes

Troubleshooting

connection refused

Start the local model server, confirm the port, and query its model list. For Ollama:

ollama list
curl http://127.0.0.1:11434/v1/models

model endpoint returned HTTP 404

Use the provider's OpenAI-compatible base URL. Claude Text Lab appends /chat/completions unless the endpoint already ends with it. Do not pass Ollama's native /api/chat URL.

model did not return valid JSON

Ledger extraction and verification require JSON. Retry with a stronger instruction-following model, reduce the source length, or use a provider with structured-output reliability. Do not treat repeated parser failures as successful transformation.

remote model endpoint refused

Use a loopback address, or use an HTTPS remote endpoint with --allow-remote. Plain remote HTTP is intentionally rejected.

rewrite model must be non-Claude

Select a non-Claude writer and verifier. The restriction is deliberate because Claude could add a new mark.

Output file already exists

Choose a new path or add --force. The tool refuses silent replacement and refuses to make an output path equal to an input path.

The candidate keeps failing overlap gates

Try a more capable model, use three or more candidates, change the requested style, or lower --max-shared-run only after examining why text is shared. Raising --max-ngram-reuse makes the gate more permissive, but it does not make the transformation stronger.

The candidate loses facts

Use a stronger model, keep ledger review and semantic checking enabled, use a separate verifier, and examine facts.json. Add an exact value with audit --require when the deterministic patterns do not protect it. Human review remains required for high-stakes text.

MCP server connects but rewrite fails

The MCP host launches a fresh process and may not inherit the shell environment you expected. Put CTL_ENDPOINT, CTL_MODEL, and CTL_API_KEY in the server configuration. Use an absolute executable path and check the host's MCP logs.

Security and responsible use

Use Claude Text Lab only on text you own or are authorized to edit. Do not use it to evade disclosure requirements, academic-integrity rules, publishing policies, contractual duties, platform rules, or applicable law. A changed signal does not make AI-assisted work human-authored.

The source is treated as untrusted data in prompts, but prompt-injection risk cannot be eliminated. A local model server is also a trust boundary. Review docs/threat-model.md before processing sensitive or high-stakes material.

Research basis and competing projects

The project was designed from Anthropic's August 2026 announcement, published text-watermark research, and a review of existing open-source tools. The detailed claim matrix, evidence tiers, method comparison, unknowns, experiment design, and limitations are in docs/research.md.

The linked projects provide useful Unicode, media, evaluation, or open-watermark ideas, but none contains Anthropic's undisclosed detector:

• guillaumemeyer/watermarks-remover

• wiltodelta/remove-ai-watermarks

• THU-BPM/MarkLLM

• jwkirchenbauer/lm-watermarking

• allencheng97/Self-information-Rewrite-Attack

No code from those repositories is copied or vendored here.

Documentation map

• Research note and comparison

• Threat model and limitations

• Benchmark protocol

• MCP setup and tool tutorial

• Examples

• Release checklist

• Contributing

• Security policy

Development and verification

python -m unittest discover -s tests -v
python -m compileall -q src tests
PYTHONPATH=src python -m claude_text_lab --version
PYTHONPATH=src python -m claude_text_lab --help

With the development extra installed:

python -m pytest
python -m build
python -m twine check dist/*

License and independence

MIT. See LICENSE.

Claude is a trademark of Anthropic. Claude Text Lab is an independent research project and is not affiliated with or endorsed by Anthropic.