OnionClaw 🧅

by JacobJandon

OpenClaw skill + standalone tool — full Tor / dark web access for AI agents

OnionClaw gives AI agents full access to the Tor network and .onion hidden services. It runs as an OpenClaw skill (drop-in, zero config beyond a .env file) and also works standalone from any terminal.

Based on the SICRY engine — 18 dark web search engines, Robin OSINT pipeline, four LLM analysis modes.

# As an OpenClaw skill:
cp -r OnionClaw ~/.openclaw/skills/onionclaw
# → agent now has 7 dark web commands available in every session

# Standalone:
python3 check_tor.py        # verify Tor
python3 search.py --query "ransomware healthcare"
python3 pipeline.py --query "acme.com data leak" --mode corporate

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Contents

1. What OnionClaw does
2. Requirements
3. Install as OpenClaw skill
4. Standalone install
5. Configuration
6. All seven commands
7. Investigation flows
8. Analysis modes
9. Troubleshooting
10. Credits

---

What OnionClaw does

Seven commands expose the complete Tor OSINT toolkit:

Command	What it does
check_tor.py	Verify Tor is active, show current exit IP
renew.py	Rotate Tor circuit — new exit node, new identity
check_engines.py	Ping all 18 dark web search engines, show latency
search.py	Search up to 18 engines simultaneously, deduplicated results
fetch.py	Fetch any .onion or clearnet URL through Tor
ask.py	LLM OSINT analysis of scraped content (4 modes)
pipeline.py	Full Robin pipeline: refine → search → filter → scrape → analyse

---

Requirements

• Python 3.10+
• Tor running locally (SOCKS proxy on 127.0.0.1:9050)
• pip packages: requests[socks] beautifulsoup4 python-dotenv stem
• LLM key (optional — only needed for ask.py and pipeline.py analysis step)

Install Tor

Linux (Debian/Ubuntu):

apt install tor && tor &

macOS:

brew install tor && tor &

With control port (needed for renew.py):

cat > /tmp/onionclaw_tor.conf << 'EOF'
SocksPort 9050
ControlPort 9051
CookieAuthentication 1
DataDirectory /tmp/tor_data
EOF
tor -f /tmp/onionclaw_tor.conf &

Then set TOR_DATA_DIR=/tmp/tor_data in .env.

Install Python packages

pip install requests[socks] beautifulsoup4 python-dotenv stem

---

Install as OpenClaw skill

1. Clone or copy this repo into your OpenClaw skills directory:

# Option A — clone directly
git clone https://github.com/Ps3wb4n/OnionClaw ~/.openclaw/skills/onionclaw

# Option B — copy local folder
cp -r OnionClaw ~/.openclaw/skills/onionclaw

2. Configure .env in the skill folder:

cp ~/.openclaw/skills/onionclaw/.env.example ~/.openclaw/skills/onionclaw/.env
nano ~/.openclaw/skills/onionclaw/.env   # add LLM key if desired

3. Start a new OpenClaw session — the skill loads automatically on startup. OpenClaw includes onionclaw in the agent context whenever the user asks about dark web topics.

Verify OpenClaw can see the skill:

openclaw skills list
# → onionclaw  🧅  Search the Tor dark web...

OpenClaw trigger phrases:

• "search the dark web for …"
• "investigate this .onion site …"
• "check if my data appeared on the dark web"
• "find ransomware leaks related to …"
• "fetch this .onion URL …"
• "run a Tor OSINT investigation on …"

After install, start a new session — existing sessions will not pick up the new skill.

---

Standalone install

No OpenClaw required. Every script runs directly from a terminal:

git clone https://github.com/JacobJandon/OnionClaw
cd OnionClaw
pip install requests[socks] beautifulsoup4 python-dotenv stem
cp .env.example .env
# Edit .env — add LLM key if desired (optional for most commands)

---

Configuration

Copy .env.example to .env and fill in what you need:

# ── Tor ────────────────────────────────────────────────────────────────
TOR_SOCKS_HOST=127.0.0.1
TOR_SOCKS_PORT=9050
TOR_CONTROL_HOST=127.0.0.1
TOR_CONTROL_PORT=9051
# TOR_CONTROL_PASSWORD=your_password   # only if HashedControlPassword in torrc
# TOR_DATA_DIR=/tmp/tor_data           # DataDirectory path for cookie auth

# ── LLM (needed only for ask.py and pipeline.py analysis step) ──────────
LLM_PROVIDER=openai          # openai | anthropic | gemini | ollama | llamacpp
OPENAI_API_KEY=sk-...
# ANTHROPIC_API_KEY=sk-ant-...
# GEMINI_API_KEY=AIza...
# OLLAMA_MODEL=llama3.2       # local model — no API key needed

No LLM key? Set LLM_PROVIDER=ollama with a running Ollama instance for fully local inference. search.py, fetch.py, check_tor.py, renew.py, and check_engines.py work with no LLM key at all.

---

All seven commands

check_tor.py — verify Tor

python3 check_tor.py

✓ Tor active
  Exit IP  : 185.220.101.20
  Error    : None
{"tor_active": true, "exit_ip": "185.220.101.20", "error": null}

Run this before anything else. Exits with code 1 if Tor is not running or not accessible on port 9050.

---

renew.py — rotate identity

python3 renew.py

Rotating Tor circuit...
✓ Identity renewed — new Tor circuit established
{"success": true, "error": null}

Auth order: password env var → cookie from TOR_DATA_DIR → common system paths → null auth. Works out of the box with the recommended torrc above or with a standard system Tor install.

---

check_engines.py — engine health check

python3 check_engines.py

ALIVE  13/18
──────────────────────────────────────────
  ✓  Ahmia-clearnet       670ms  ███
  ✓  Tor66                749ms  ███
  ✓  Ahmia               1139ms  █████
  ✓  OSS                 1203ms  ██████
  ...
DOWN   5/18
  ✗  Kaizer     timeout
  ✗  Anima      timeout
  ✗  FindTor    timeout

Run before a large search session. Use the alive engine names as arguments to --engines in search.py.

---

search.py — search dark web

# All 18 engines (default)
python3 search.py --query "ransomware healthcare leak"

# Specific engines (faster — use alive engines from check_engines output)
python3 search.py \
  --query "credential dump" \
  --engines Ahmia Tor66 Ahmia-clearnet OSS

# Limit result count
python3 search.py --query "bitcoin mixer" --max 30

Returns a deduplicated {title, url, engine} list across all engines.

Tip: Use short keyword queries (3–5 words). Dark web search indexes respond much better to keywords than natural-language sentences.

---

fetch.py — fetch any .onion page

# Hidden service
python3 fetch.py \
  --url "http://juhanurmihxlp77nkq76byazcldy2hlmovfu2epvl5ankdibsot4csyd.onion"

# With links extracted
python3 fetch.py --url "http://example.onion/page" --links

# JSON output only
python3 fetch.py --url "http://example.onion" --json

Returns: title, text content (up to 8000 chars), extracted links, HTTP status code. A status of 0 means the hidden service is unreachable or offline.

---

ask.py — LLM OSINT analysis

# Inline content
python3 ask.py \
  --query "LockBit ransomware" \
  --mode ransomware \
  --content "page text from scraped pages"

# From file
python3 ask.py \
  --query "acme.com" \
  --mode corporate \
  --file /tmp/scraped_pages.txt

# Pipe from fetch.py
python3 fetch.py --url "http://some.onion" --json | \
  python3 -c "import sys,json; print(json.load(sys.stdin)['text'])" | \
  python3 ask.py --query "investigate" --mode threat_intel

# Custom analysis focus
python3 ask.py \
  --query "mixer services" \
  --mode threat_intel \
  --content "..." \
  --custom "Focus on cryptocurrency wallet addresses only"

Modes: threat_intel · ransomware · personal_identity · corporate — see Analysis modes.

---

pipeline.py — full investigation (one command)

Runs the complete Robin OSINT pipeline automatically:

Step	What happens
1	Verify Tor is active
2	Check which engines are alive
3	Refine query to ≤5 keywords (LLM)
4	Search all alive engines
5	Filter top 20 most relevant results (LLM)
6	Batch-scrape pages concurrently
7	OSINT analysis and report (LLM)

# Basic
python3 pipeline.py --query "hospital ransomware 2026"

# With analysis mode and output file
python3 pipeline.py \
  --query "acme.com credentials leak" \
  --mode corporate \
  --out report.md

# Full options
python3 pipeline.py \
  --query "QUERY" \
  --mode ransomware \
  --max 50 \
  --scrape 12 \
  --custom "Focus on ransom amounts and victim industries" \
  --out report.md

Steps 1–6 work fully without an LLM key. Only steps 3, 5, and 7 use the LLM — they fall back gracefully when no key is set, printing what was collected so far.

---

Investigation flows

Scenario 1: Check for leaked credentials

python3 check_tor.py
python3 pipeline.py \
  --query "acme.com email passwords" \
  --mode corporate \
  --scrape 10 \
  --out acme_leak_report.md

Scenario 2: Ransomware intelligence

python3 check_tor.py
python3 search.py \
  --query "LockBit healthcare 2026" \
  --engines Ahmia Tor66 Ahmia-clearnet \
  --max 40

# Fetch the most relevant URL
python3 fetch.py --url "http://..." > /tmp/page.json

# Analyse
python3 ask.py \
  --query "LockBit healthcare 2026" \
  --mode ransomware \
  --file /tmp/page.json

Scenario 3: Personal data exposure check

python3 pipeline.py \
  --query "john.smith@email.com personal data" \
  --mode personal_identity \
  --scrape 8

Scenario 4: Manual step-by-step investigation

# 1. Verify Tor
python3 check_tor.py

# 2. Find alive engines
python3 check_engines.py

# 3. Search with alive engines
python3 search.py \
  --query "ransomware hospital 2026" \
  --engines Ahmia Tor66 OSS \
  --max 40

# 4. Fetch top pages
python3 fetch.py --url "http://..." --links
python3 fetch.py --url "http://..."

# 5. Analyse
python3 ask.py \
  --query "hospital ransomware" \
  --mode ransomware \
  --content "combined text from pages"

# 6. Rotate identity when done
python3 renew.py

---

Analysis modes

threat_intel (default)

General dark web OSINT. Extracts: IoCs, infrastructure details, actor mentions, next investigation steps.

Output sections: Input Query · Source Links · Investigation Artifacts · Key Insights · Recommended Next Steps

ransomware

Malware/RaaS intelligence. Extracts C2 domains, file hashes, MITRE ATT&CK TTPs, victim sectors, ransom amounts.

Output sections: Input Query · Source Links · Malware/Ransomware Indicators · Threat Actor Profile · Key Insights · Recommended Next Steps

personal_identity

PII and breach exposure. Surfaces SSNs, emails, passwords, passport data, breach sources, risk severity ratings.

Output sections: Input Query · Source Links · Exposed PII Artifacts · Breach/Marketplace Sources · Exposure Risk Assessment · Key Insights · Recommended Next Steps

corporate

Corporate threat intelligence. Detects leaked credentials, source code, internal documents, initial access broker activity.

Output sections: Input Query · Source Links · Leaked Corporate Artifacts · Threat Actor/Broker Activity · Business Impact Assessment · Key Insights · Recommended Next Steps

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Troubleshooting

✗ Tor NOT active

# Is Tor running?
pgrep tor || tor &
# Is port 9050 listening?
ss -tlnp | grep 9050

renew.py → success: false

# Is control port open?
ss -tlnp | grep 9051

# Enable it in torrc:
echo "ControlPort 9051" >> /etc/tor/torrc
echo "CookieAuthentication 1" >> /etc/tor/torrc
systemctl restart tor

# Set correct DataDirectory in .env:
TOR_DATA_DIR=/var/lib/tor      # system Tor (Debian/Ubuntu)
TOR_DATA_DIR=/tmp/tor_data     # custom torrc DataDirectory

fetch.py → status: 0 The hidden service is offline. .onion sites go down frequently — try a different URL from search.py results. Confirm Tor is healthy with check_tor.py.

search.py returns 0 results Dark web indexes fluctuate. Run check_engines.py to find alive engines, then use --engines Ahmia Ahmia-clearnet as reliable fallbacks.

ask.py / pipeline.py LLM error Set LLM_PROVIDER and an API key in .env. For no-key local operation: LLM_PROVIDER=ollama with a running Ollama instance. All five non-LLM scripts (check_tor, renew, check_engines, search, fetch) work without any key.

ERROR: sicry.py not found sicry.py must be in the OnionClaw root (same folder as SKILL.md). It is included in this repo — do not delete or move it. If you cloned and it is missing, re-clone fresh.

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File structure

OnionClaw/
├── SKILL.md              ← OpenClaw skill descriptor (YAML frontmatter)
├── sicry.py              ← SICRY engine (bundled — no separate install needed)
├── .env.example          ← Copy to .env and configure
├── README.md             ← This file
├── check_tor.py          ← Verify Tor / show exit IP
├── renew.py              ← Rotate Tor circuit
├── check_engines.py      ← Ping all 18 engines
├── search.py             ← Search dark web
├── fetch.py              ← Fetch .onion pages
├── ask.py                ← OSINT analysis via LLM
├── pipeline.py           ← Full Robin investigation pipeline
└── sync_sicry.py         ← Sync SICRY engine from upstream

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Credits

• Dark web search engine list and Robin OSINT pipeline from Robin by @apurvsinghgautam — MIT licence
• Core engine: SICRY
• .onion address verification via dark.fail
• Agent runtime: OpenClaw
• Network anonymisation: Tor Project

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License

MIT License — Copyright (c) 2026 JacobJandon

"OnionClaw" and the OnionClaw logo are owned by JacobJandon.

See LICENSE for full text.

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Use responsibly and lawfully. For OSINT, security research, and threat intelligence purposes only.