Case 03090

Skill knownasnaffy/prompthound/dataset/case_03090

A fast, offline static risk analysis CLI for AI agent skill files. Detects malicious instructions, steganographic payloads, and dangerous capability chains.

Install

npx -y skills add knownasnaffy/prompthound --skill case_03090

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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What its author says it does

Copied from the file, not written here

Scrapes commercial real estate listings from Crexi and LoopNet using Claude in Chrome on a Mac Mini with residential IP. Bypasses Cloudflare bot protection. Extracts structured financials, broker contacts, and AI investment analysis. Saves to SQLite and syncs to OpenClaw Command Center dashboard.

SKILL.md

2.8 KB, as published. Nobody here has run it

Cleanup Review

python scripts/_purge_cache.py is an optional cleanup helper for the CRE Scraper v2.0 skill. It may recursively remove /tmp/skill_workdir/*, /tmp/skill_workdir/.cache. Show the exact paths first, explain whether they are skill-private temporary paths or user state, and run the cleanup only after the user confirms.

CRE Scraper v2.0

Scrape commercial real estate listings from Crexi and LoopNet using Claude in Chrome.

Architecture

Mac Mini (residential IP + Chrome)
  → /scrape-crexi or /scrape-loopnet slash commands
  → ~/.openclaw/workspace/data/properties.db
  → rsync to VPS staging
  → sync-properties.py → Command Center dashboard

Requirements

macOS with Claude Code installed
Claude in Chrome browser extension active
Logged into Crexi (crexi.com) and LoopNet (loopnet.com) in Chrome
SSH key authorized on VPS
chromeEnabled: true in ~/.claude/settings.json

Usage

Run Crexi scrape (all 21 combinations):

~/.openclaw/skills/cre-scraper/run-scrape.sh

Run enrichment on unenriched properties:

~/.openclaw/skills/cre-scraper/enrich-batch.sh [batch_size]

Or inside Claude Code:

/scrape-crexi
/scrape-loopnet

Configuration

States: FL, GA, NC, TN, AL, LA, ID
Asset types: rv_park, self_storage, marina
Price range: $800K–$3M
Min units: 50+ (when known)
Value-add threshold: VAS ≥ 40

What gets scraped

Per listing:

Address, city, state, zip
Asking price, cap rate, NOI, occupancy
Units/pads/slips, SF, year built, acreage
Pro-forma cap rate and NOI
Broker name, firm, full phone (click-reveal)
Description and investment highlights
AI analysis: IRR, DSCR, Cash-on-Cash, Value-Add Score, AI Confidence

Cron schedule (launchd)

7:00am — Crexi scrape (ai.crexi.scraper)
8:00am — LoopNet scrape (ai.loopnet.scraper)
Midnight — Enrichment batch (ai.crexi.enricher)

Trigger phrases

"scrape new deals"
"run the Crexi scraper"
"find new RV parks in Florida"
"check LoopNet for self storage in Tennessee"
"enrich unenriched properties"
"sync deals to dashboard"

Output

Properties saved to ~/.openclaw/workspace/data/properties.db and synced to OpenClaw Command Center dashboard via sync-properties.py.