agentmods.dev

dataclaw AGENTS.md

Project instructions for DataClaw, a tool that exports Claude Code, Codex and other coding-agent conversations to Hugging Face. They define a staged process for authentication, configuration, review and publishing, including a privacy check for personal data.

In plain words
What is it for?
Checking export status, choosing conversation and project scope, setting redacted strings, reviewing an export, confirming it and publishing it to Hugging Face.
Why use it?
They prevent exports from being published before the required review and confirmation steps. Following the tool's next-step instructions reduces the risk of skipping configuration or sending unreviewed conversation data.

Instructions file for CodexOpenCode

2.1k 2mo ago A scan 1,395 tokens original MIT
View the source file peteromallet/dataclaw
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add instructions/peteromallet/dataclaw/agents-md
Clone the repo
git clone --depth 1 https://github.com/peteromallet/dataclaw

Made for: Codex, OpenCode.

Per session 1,395 This file is loaded in full into every session.
When invoked 1,395 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.01395 $0.01395
Opus 5 $0.00698 $0.00698
Sonnet 5 $0.00279 $0.00279
Haiku 4.5 $0.00139 $0.00139

Measured 2d ago against content hash 7b08ba4ce9a8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

dataclaw AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AGENTS.md · 98 lines

How it starts

The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.

DataClaw

Export Claude Code, Codex, and other coding-agent conversation history to Hugging Face.

THE RULE

Every dataclaw command outputs next_steps. FOLLOW THEM.

Do not memorize the flow. Do not skip steps. Do not improvise. Run the command -> read the output -> follow next_steps. That's it.

Runtime guidance follows this checklist:

  1. Install
  2. Install skill
  3. Prep 3A. Choose source scope 3B. Choose project scope 3C. Set redacted strings
  4. Export locally
  5. Review and confirm
  6. Publish

The CLI tracks your stage as 1-4: auth -> configure -> review -> done. dataclaw export (push) is gated - you must run dataclaw confirm first or it will refuse.

Getting Started

Run dataclaw status (or dataclaw prep for full details) and follow the next_steps.

Output Format

  • dataclaw prep, dataclaw config, dataclaw status, and dataclaw confirm output pure JSON
  • dataclaw export outputs human-readable text followed by ---DATACLAW_JSON--- and a JSON block
  • Always parse the JSON and act on next_steps

Key fields:

  • stage / stage_number / total_stages - where you are
  • next_steps - follow these in order
  • next_command - the single most important command to run next (null if user input needed first)

PII Audit (Stage 5)

After dataclaw export --no-push, follow the next_steps in the JSON output. The flow is:

  1. Ask the user their full name - then grep the export for it
  2. Run the pii_commands from the JSON output and review results with the user
  3. Ask the user what else to look for - company names, client names, private URLs, other people's names, custom domains
  4. Deep manual scan - sample ~20 sessions (beginning, middle, end) and look for anything sensitive the regex missed
  5. Fix and re-export if anything found: dataclaw config --redact "string" then dataclaw export --no-push
  6. Run dataclaw confirm with text attestations - pass --full-name, --attest-full-name, --attest-sensitive, and --attest-manual-scan. It runs PII scan, verifies attestations, shows project breakdown, and unlocks pushing.
  7. Push only after explicit user confirmation: dataclaw export --publish-attestation "User explicitly approved publishing to Hugging Face."

Read the full file on GitHub · 98 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 98 lines · 1,395 tokens per session scan A 7b08ba4ce9a8

Subscribe to this mod's changes

dataclaw AGENTS.md is an instructions file published in the GitHub repository peteromallet/dataclaw (2,109 stars, last pushed 2mo ago), licensed MIT. It adds 1,395 tokens to every session, about $0.0070 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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