python3 {baseDir}/check_tor.py

Output: exit IP address and tor_active: true/false. If tor_active is false, tell the user to start Tor and stop.

Rotate Tor identity

Get a fresh exit node and new identity. Use between investigation sessions or when you need a new IP.

python3 {baseDir}/renew.py

Output: success: true/false. If false, the user needs to ensure ControlPort 9051 is enabled and TOR_DATA_DIR is set in .env.

Search the dark web

Search all 12 verified-live dark web engines simultaneously. Returns deduplicated {title, url, engine} results.

Basic search:

python3 {baseDir}/search.py --query "SEARCH_TERM"

With result limit:

python3 {baseDir}/search.py --query "SEARCH_TERM" --max 30

Specific engines only:

python3 {baseDir}/search.py --query "SEARCH_TERM" --engines Ahmia Tor66 Ahmia-clearnet

Available engines: Ahmia, OnionLand, Amnesia, Torland, Excavator, Onionway, Tor66, OSS, Torgol, TheDeepSearches, DuckDuckGo-Tor, Ahmia-clearnet

Tip: Use short keyword queries (≤5 words). Dark web indexes respond better to focused keywords than natural-language questions.

Fetch a .onion page

Fetch the full content of any .onion URL or clearnet URL through Tor.

python3 {baseDir}/fetch.py --url "http://SOME.onion/path"

Output: title, text (first 3000 chars), link list, HTTP status. If status is 0 or error is set, the hidden service is unreachable.

Check which search engines are alive

Ping all 12 engines via Tor and get latency + status for each.

python3 {baseDir}/check_engines.py

Output: per-engine up/down status, latency in ms. Use this before a large search run to pass only alive engines to --engines.

OSINT analysis

Analyse scraped dark web content with an LLM. Produces a structured sectioned report.

From a string:

python3 {baseDir}/ask.py --query "INVESTIGATION_QUERY" --mode MODE --content "RAW_TEXT"

From a file:

python3 {baseDir}/ask.py --query "INVESTIGATION_QUERY" --mode MODE --file /path/to/content.txt

From stdin (pipe):

echo "CONTENT" | python3 {baseDir}/ask.py --query "QUERY" --mode MODE

Analysis modes:

| Mode | Use for | |---|---| | threat_intel | General OSINT (default) — artifacts, insights, next steps | | ransomware | Malware/C2/MITRE TTPs, victim orgs, indicators | | personal_identity | PII/breach exposure, severity, protective actions | | corporate | Leaked credentials/code/docs, IR recommendations |

With custom focus:

python3 {baseDir}/ask.py --query "QUERY" --mode threat_intel --custom "Focus on cryptocurrency wallet addresses"

Full OSINT pipeline (one command)

Runs the complete Robin pipeline: refine query → check live engines → search → filter best results → batch scrape → OSINT analysis.

python3 {baseDir}/pipeline.py --query "INVESTIGATION_QUERY" --mode MODE

With more results:

python3 {baseDir}/pipeline.py --query "INVESTIGATION_QUERY" --mode ransomware --max 50 --scrape 10

Without an LLM key (raw results only):

python3 {baseDir}/pipeline.py --query "INVESTIGATION_QUERY" --no-llm

Options:

• --query — investigation topic (natural language OK — it gets refined automatically)
• --mode — threat_intel (default), ransomware, personal_identity, corporate
• --max — max raw results from search (default 30)
• --scrape — how many pages to batch-fetch (default 8)
• --custom — custom LLM instructions appended to the mode prompt
• --out FILE — write final report to a file
• --no-llm — skip refine/filter/ask steps; dump raw scraped content (no API key needed)

Typical investigation flows

"Search the dark web for X"

1. python3 {baseDir}/check_tor.py — verify connected
2. python3 {baseDir}/search.py --query "X" — search all engines
3. python3 {baseDir}/fetch.py --url "URL" on 2-3 top results
4. python3 {baseDir}/ask.py --mode threat_intel --query "X" --content "..." on combined text

"Has company.com been leaked on the dark web"

1. python3 {baseDir}/check_tor.py
2. python3 {baseDir}/pipeline.py --query "company.com data leak credentials" --mode corporate
3. Present the structured report to the user

"Investigate ransomware group X"

1. python3 {baseDir}/check_tor.py
2. python3 {baseDir}/pipeline.py --query "GROUP_NAME ransomware" --mode ransomware

"Fetch this .onion URL"

1. python3 {baseDir}/check_tor.py
2. python3 {baseDir}/fetch.py --url "URL"
3. Show the user the title + text content

Important notes

• All traffic routes through Tor — tell the user this when relevant.
• .onion sites are frequently offline — if fetch returns status: 0, the site is temporarily down.
• Dark web search indexes go down often — run check_engines.py first and filter by alive engines.
• LLM tools (ask) require an API key in {baseDir}/.env. Search and fetch work without any key.
• Use responsibly and lawfully — OSINT, security research, and threat intelligence only.

Maintenance

sync_sicry.py — update bundled sicry.py from upstream

sync_sicry.py fetches the latest (or a tagged) sicry.py from the upstream SICRY™ GitHub repo and overwrites the bundled copy inside OnionClaw. Run it after a new SICRY™ release is published.

Checking for OnionClaw updates

OnionClaw will automatically notify you whenever a newer release is available — a one-line message is printed at startup of pipeline.py if an update exists. You can also check on demand:

# Quick CLI update check (prints latest version, release URL, upgrade command):
python3 {baseDir}/pipeline.py --check-update

# Programmatic check from any Python environment:
import sicry
r = sicry.check_update()
if not r["up_to_date"]:
    print(f"Update: {r['current']} → {r['latest']}  {r['url']}")

Upgrade after a new release:

git -C {baseDir} pull                          # update the whole repo
python3 {baseDir}/sync_sicry.py               # re-sync sicry.py from SICRY™

The check is clearnet-only (not through Tor), uses a 4-second timeout, and is always silent on network errors.

# Pull latest main branch:
python3 {baseDir}/sync_sicry.py

# Pull a specific release tag:
python3 {baseDir}/sync_sicry.py --tag v1.2.0

# Preview without writing (dry run):
python3 {baseDir}/sync_sicry.py --dry-run

Development workflow (when editing sicry.py locally):

1. Edit OnionClaw/sicry.py.
2. Copy to parent repo: cp OnionClaw/sicry.py Sicry/sicry.py
3. Commit + tag both repos, then push.
4. Once the tag is live on GitHub, users can run sync_sicry.py --tag vX.Y.Z to update their OnionClaw copy.

Flags:

• --tag REF — git ref or tag to fetch (default: main)
• --dry-run — show what would happen without writing anything

This is not a warning tucked in fine print. It is the whole point of writing it down openly.

What the stack enables — the full map

| Use case | What it looks like | |---|---| | Dark-web crawling | Automated, headless spidering of .onion services at scale — forums, paste sites, markets, leak boards — with full identity rotation between every request. No human ever touches a keyboard. | | Threat intelligence | Continuous monitoring of ransomware group blogs, initial access broker ads, CVE exploit drops, and actor chatter long before it surfaces on clearnet feeds. | | Marketplace monitoring | Price tracking, stock alerts, vendor reputation scraping, and availability checks across darknet markets — the same logic a researcher uses to track fentanyl price trends is the same logic a supplier uses to undercut competitors. | | Credential surveillance | Watching paste boards, breach dumps, and forum leaks for specific email domains, API keys, SSH keys, or internal hostnames the moment they appear — at a scale no human analyst can match. | | Deanonymisation research | Cross-correlating .onion service metadata with clearnet traces, timing attacks, correlation of writing style and PGP keys — used both by law enforcement hunting criminals and by threat actors hunting journalists and dissidents. | | Criminal automation | Autonomous agents placing orders, posting ads, messaging vendors, managing mule accounts, draining wallets — an entire criminal operation running without a human ever in the loop. | | Disinformation infrastructure | Coordinated persona networks on hidden boards, fabricated document drops timed to bleed into legitimate OSINT pipelines, synthetic intelligence that reads real but originates from nowhere. | | Zero-day brokerage | Automated monitoring of exploit vendor channels, private CVE auction boards, and vulnerability markets — buy-side and sell-side intelligence gathered faster than any human analyst. |

The ugly side

The 2026 internet is already at the edge of this. Within five years, AI agents that can:

1. Browse anonymously through rotating Tor circuits with no persistent identity
2. Understand context well enough to navigate dark web UIs, CAPTCHA logic, and forum culture without hardcoded selectors
3. Act autonomously — search, buy, post, exfiltrate, rotate — in closed loops with no human confirmation step
4. Self-orchestrate across dozens of simultaneous Tor identities on parallel threads

…represent a qualitative shift from human criminals using tools to autonomous criminal infrastructure operating at machine speed with no human in the loop. The bottleneck has always been human attention. Remove it and the scaling properties of dark web operations change completely.

OnionClaw demonstrates all four of those primitives working together today. The full pipeline.py step — query refinement → multi-engine search → result filtering → batch scrape → LLM synthesis → identity rotation — is a complete autonomous dark web intelligence loop. Remove the OSINT framing and it is equally a complete autonomous dark web operation loop. The code is the same either way.

Why this is written explicitly

Security tools that pretend the dual-use problem does not exist are more dangerous than ones that name it directly. If you are building on top of OnionClaw:

• Know what you are building. The pipeline does not know if the query is "acme.com credential leak" for a pentest or "rival vendor SSH keys" for espionage.
• Know your jurisdiction. Automated access to dark web content and .onion services may be illegal in your country regardless of intent or findings.
• Tor is not legal protection. It is operational security. The two are different things with very different limits.
• AI + Tor + autonomy is not a theoretical threat. It is a present capability. This repo is one of many signals that the tooling is ready.

OnionClaw is published for defensive research, red-team engagements, and threat intelligence work. The code does not know the difference between those uses and their inverse. You do. Build accordingly.

Contents

1. ⚠️ The Rabbit Hole
2. What OnionClaw does
3. Requirements
4. Install as OpenClaw skill
5. Standalone install
6. Configuration
7. All seven commands
8. Investigation flows
9. Analysis modes
10. Architecture
11. Troubleshooting
12. 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/JacobJandon/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
OPENA