Case
8638500190
- Built deep-scraper skill (CDP-based X feed extraction) - Three-stage pipeline: scrape → triage → investigate - Paper trading simulator with position tracking - First live investigation: verified kch123 Polymarket profile ($9.3M P&L) - Opened first paper position: Seahawks Super Bowl @ 68c - Telegram alerts with inline action buttons - Portal build in progress (night shift)
119 lines
3.7 KiB
Python
Executable File
119 lines
3.7 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Investigation report generator for Feed Hunter.
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Reads triage.json and produces investigation tasks as structured prompts
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for the agent to execute.
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Usage:
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python3 investigate.py <triage.json> [--output investigations/]
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This doesn't do the investigation itself — it generates the task list
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that the agent (Case) follows using browser/web tools.
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"""
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import argparse
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import json
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import os
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from datetime import datetime, timezone
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from pathlib import Path
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def generate_investigation_prompt(post):
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"""Generate an investigation prompt for the agent."""
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author = post["author"].get("handle", "unknown")
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text = post["text"][:500]
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claims = post.get("claims", [])
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links = post.get("links", [])
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tasks = post.get("tasks", [])
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prompt = f"""## Investigation: {author}
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**Post:** {text}
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**Claims detected:**
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"""
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for c in claims:
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prompt += f"- [{c['type']}] {c['match']}\n"
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prompt += f"\n**Links found:**\n"
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for l in links:
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prompt += f"- [{l['type']}] {l['url']}\n"
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prompt += f"\n**Investigation tasks:**\n"
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for i, t in enumerate(tasks, 1):
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prompt += f"{i}. **{t['action']}**: {t['description']}\n"
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prompt += f" Method: {t['method']}\n"
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if t.get('url'):
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prompt += f" URL: {t['url']}\n"
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prompt += """
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**Deliver:**
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1. Is the claim verifiable? What does the actual data show?
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2. Is there recent activity? (Last 24-48h)
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3. Is this still actionable or has the window closed?
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4. Risk assessment (1-10, where 10 is highest risk)
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5. Verdict: ACTIONABLE / EXPIRED / EXAGGERATED / SCAM / UNVERIFIABLE
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6. If ACTIONABLE: suggested paper trade parameters (asset, entry, size, stop loss, take profit)
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"""
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return prompt
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("input", help="Path to triage.json")
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parser.add_argument("--output", help="Output directory for investigation files")
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args = parser.parse_args()
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with open(args.input) as f:
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data = json.load(f)
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queue = data.get("investigation_queue", [])
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if not queue:
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print("No posts in investigation queue.")
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return
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output_dir = args.output or os.path.join(os.path.dirname(args.input), "investigations")
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os.makedirs(output_dir, exist_ok=True)
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timestamp = datetime.now(timezone.utc).strftime("%Y%m%d-%H%M%S")
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investigations = []
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for i, post in enumerate(queue):
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inv = {
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"id": f"inv-{timestamp}-{i}",
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"post_author": post["author"].get("handle", "unknown"),
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"post_url": post.get("url", ""),
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"priority": post["priority"],
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"claims": post.get("claims", []),
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"tasks": post.get("tasks", []),
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"prompt": generate_investigation_prompt(post),
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"status": "pending",
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"result": None,
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}
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investigations.append(inv)
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# Save investigation batch
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batch_file = os.path.join(output_dir, f"batch-{timestamp}.json")
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with open(batch_file, "w") as f:
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json.dump({
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"batch_id": timestamp,
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"created_at": datetime.now(timezone.utc).isoformat(),
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"count": len(investigations),
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"investigations": investigations,
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}, f, indent=2)
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print(f"=== Investigation Batch: {timestamp} ===")
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print(f"Tasks: {len(investigations)}")
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for inv in investigations:
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print(f"\n [{inv['priority']}] {inv['post_author']}")
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print(f" Claims: {[c['type'] for c in inv['claims']]}")
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print(f" Tasks: {len(inv['tasks'])}")
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print(f"\nSaved to {batch_file}")
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print(f"\nTo execute: agent reads batch file and runs each investigation prompt")
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if __name__ == "__main__":
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main()
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