DataClaw

This is a performance art project. Anthropic built their models on the world's freely shared information, then introduced increasingly dystopian data policies to stop anyone else from doing the same with their data - pulling up the ladder behind them. DataClaw lets you throw the ladder back down. The dataset it produces is yours to share.

Turn your Claude Code, Codex, and other coding-agent conversation history into structured data and publish it to Hugging Face with a single command. DataClaw parses session logs, redacts secrets and PII, and uploads the result as a ready-to-use dataset.

Every export is tagged dataclaw on Hugging Face. Together, they may someday form a growing distributed dataset of real-world human-AI coding collaboration.

Install

Mac app

A menu-bar app for Apple Silicon Macs. Download the DMG, drag DataClaw.app to Applications, and launch it from Applications or Spotlight. The app bundles everything it needs — no Python or CLI install required.

Opening the app for the first time

The build is currently unsigned (Apple Developer ID signing is being set up), so macOS blocks the first launch. This is expected, one-time, and takes about 20 seconds:

1. Double-click DataClaw.app → a dialog says it can't be opened → click Done (not Move to Trash).
2. Open System Settings and type Privacy & Security in the search field (or: Apple menu → System Settings).
3. Scroll to the Security section — you'll see "DataClaw" was blocked to protect your Mac → click Open Anyway.
4. In the confirmation dialog click Open Anyway again and authenticate with Touch ID or your password. DataClaw launches and lives in the menu bar; subsequent launches are normal.

CLI

For the terminal workflow, Intel Macs, or driving DataClaw with a coding agent:

pip install -U dataclaw

Give this to your agent

Paste this into Claude Code, Codex, or any coding agent:

Help me export my Claude Code, Codex, and other coding-agent conversation history to Hugging Face using DataClaw.
Install it, then walk me through the process.

STEP 1 - INSTALL
  pip install -U dataclaw
  If that fails: git clone https://github.com/peteromallet/dataclaw.git /tmp/dataclaw && pip install -U /tmp/dataclaw
  If that also fails, ask the user where the source is.

STEP 2 - INSTALL SKILL
  Skill support is currently only available for Claude Code.
  dataclaw update-skill claude
  For other agentic tools, skip this step and do not improvise a custom flow - follow the instructions in DataClaw's output on each step, especially next_steps and next_command.

STEP 3 - PREP
  dataclaw prep
  Every dataclaw command outputs next_steps in its JSON - follow them through the entire flow.

STEP 3A - CHOOSE SOURCE SCOPE (REQUIRED BEFORE EXPORT)
  Ask the user explicitly which source scope to export: a supported source key such as claude or codex, or all.
  dataclaw config --source all
  Do not export until source scope is explicitly confirmed.

STEP 3B - CHOOSE PROJECT SCOPE (REQUIRED BEFORE EXPORT)
  dataclaw list --source all
  Send the FULL project/folder list to the user in a message (name, source, sessions, size, excluded).
  Ask which projects to exclude.
  dataclaw config --exclude "project1,project2" OR dataclaw config --confirm-projects
  Do not export until folder selection is explicitly confirmed.

STEP 3C - SET REDACTED STRINGS
  Ask the user what additional strings should always be redacted, such as company names, client names, domains, internal URLs, or secrets that regex might miss.
  dataclaw config --redact "string1,string2"
  dataclaw config --redact-usernames "user1,user2"
  Only add these after explicit user confirmation.

STEP 4 - EXPORT LOCALLY
  dataclaw export --no-push --output dataclaw_export.jsonl

STEP 5 - REVIEW AND CONFIRM (REQUIRED BEFORE PUSH)
  Review PII findings and apply excludes/redactions as needed.
  Full name is requested for an exact-name privacy scan against the export.
  If the user declines sharing full name, use --skip-full-name-scan and attest the skip reason.
  dataclaw confirm --full-name "THEIR FULL NAME" --attest-full-name "..." --attest-sensitive "..." --attest-manual-scan "..."

STEP 6 - PUBLISH (ONLY AFTER EXPLICIT USER APPROVAL)
  dataclaw export --publish-attestation "User explicitly approved publishing to Hugging Face."
  Never publish unless the user explicitly says yes.

IF ANY COMMAND FAILS DUE TO A SKIPPED STEP:
  Restate the 6-step checklist above and resume from the blocked step (do not skip ahead).

IMPORTANT: Never run bare `hf auth login` when automating this with an agent - always use `--token`.
IMPORTANT: Always export with --no-push first and review for PII before publishing.

What gets exported

• User messages - Including voice transcripts and images
• Assistant responses
• Assistant thinking - Opt out with --no-thinking
• Tool calls - Tool name, inputs, outputs
• Token usage - Input/output tokens per session
• Metadata - Model name, git branch, timestamps

Privacy & Redaction

DataClaw applies multiple layers of protection:

1. Username redaction - Your OS username + any configured usernames replaced with stable hashes
2. Secret redaction - Regex patterns catch JWT tokens, API keys (Anthropic, OpenAI, HF, GitHub, AWS, etc.), database passwords, private keys, Discord webhooks, and more
3. Entropy analysis - Long high-entropy strings in quotes are flagged as potential secrets
4. Email redaction - Regex pattern catches email addresses
5. Custom redaction - You can configure additional strings to redact
6. Structured redaction - All session fields, tool inputs and outputs, nested metadata, dictionary keys, and embedded JSON containers are checked. Secret-named fields such as API_KEY_BACKUP also provide context for opaque values.

This is NOT foolproof. Always review your exported data before publishing. Automated redaction cannot catch everything - especially service-specific identifiers, third-party PII, or secrets in unusual formats. Binary attachments and base64 payloads are preserved; this redactor does not decode attachments or read text from images. Embedded JSON decoding is limited to eight nested wrappers. Credential-shaped examples may also be redacted conservatively.

We recommend converting the exported jsonl into human-readable yaml using dataclaw jsonl-to-yaml, then use tools such as trufflehog and gitleaks to scan it. You can also compare the exported jsonl with a previous baseline using dataclaw diff-jsonl.

To help improve redaction, report issues: https://github.com/peteromallet/dataclaw/issues

Finding datasets on Hugging Face

All repos are tagged dataclaw.

• Browse all: huggingface.co/datasets?other=dataclaw
• Load one:

  from datasets import load_dataset
  ds = load_dataset("alice/my-personal-codex-data", split="train")
  

• Combine several:

  from datasets import load_dataset, concatenate_datasets
  repos = ["alice/my-personal-codex-data", "bob/my-personal-codex-data"]
  ds = concatenate_datasets([load_dataset(r, split="train") for r in repos])
  

The auto-generated HF README includes:

• Model distribution (which models, how many sessions each)
• Total token counts
• Project count
• Last updated timestamp

Contributing

Missing data: If you found any data not exported, please report an issue. You can ask your coding agent to analyze the data, export it in this repo, and open a PR.

Better scheme: If you need to clean the data and want to propose a better scheme, feel free to open an issue.

New provider: If you use a new coding agent, you can ask it to read this repo and export its data as a new provider. Take Claude Code and Codex parsers as examples because they are the most well maintained. When you finish, ask the following questions:

• Did you follow the schema the existing parsers emit? It's fine to add custom fields in messages[].content_parts and tool_uses[].output.raw.
• Did you export all data, especially:
  • tool call inputs and outputs
  • long inputs and outputs that may be saved somewhere else
  • binary content (may be encoded as base64) such as images, in both user messages and tool calls. We do not apply anonymizer on binary content
  • subagents
• Does the coding agent automatically delete old sessions? How to prevent this?

Code Quality

License

MIT