cryptorugmunch
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PHASE 2.3 (AUDIT-2026-Q3.md):
Task 1 — Wire-in Wave 3 (1 router mounted, 2 deferred):
- app.routers.unified_scanner_router mounted at /api/v2/scanner/* (2 routes:
POST /api/v2/scanner/token/scan, POST /api/v2/scanner/wallet/scan).
Refactored prefix from /api/v2 -> /api/v2/scanner to avoid future conflicts
with the v1 /api/v1/scanner/ stub.
- app.routers.unified_wallet_scanner DEFERRED (no router APIRouter attribute;
library module consumed by unified_scanner_router via get_wallet_scanner()).
- app.routers.admin_extensions DEFERRED (DORMANT per audit; 25 routes at
/api/v1/admin/* would shadow /api/v1/admin/alerts_webhook).
Task 2 — Archive 136 dead-code files to app/_archive/legacy_2026_07/:
- 73 routers in app/routers/ (reach graph showed zero reach into mount.py).
- 63 flat app/*.py (domain modules never imported by live code).
- 1 file RESTORED post-archive: app/routers/x402_bridge_health.py (caught by
tests/unit/test_bridge_health.py which directly imports it; reach graph
considered tests/ only as transitive reach — to be patched in next cycle).
Forced-LIVE (NOT archived per user directive):
- app/ai_pipeline_v3.py (3 importers in audit window, importers themselves DEAD)
- app/splade_bm25.py (LIVE via app.rag_service)
- app/wallet_manager_v2.py (LIVE via x402_enforcement, x402_tools, sweep_all, sweep_now)
- app/crypto_embeddings.py (NOT in audit ARCHIVE list; heavy import graph)
Verification (forward-import closure from mount.py + main.py + factory.py + lifespan.py):
- imports = 348 app.* modules
- reached = 194 files reachable from roots
- archive set = audit_dead (186) - reached - forced_live (4) - test_live (1) = 136
- Net delta: 136 files moved, 44,932 LOC reduction, 293->295 active routes (+2 from Wave 3)
pyproject.toml updates:
- setuptools.packages.find: added exclude for app._archive*
- ruff.extend-exclude: added "app/_archive/"
- mypy.exclude: added "app/_archive/"
Smoke test: pytest tests/ — 817 passed, 3 pre-existing failures unchanged
(0 new failures; 0 routes lost; all 4 forced-LIVE files still importable).
Restoration: git mv app/_archive/legacy_2026_07/<name>.py <original-path>
and add the import to app/mount.py ROUTER_MODULES.
Refs: AUDIT-2026-Q3.md /home/dev/pry/rmi-final-deadcode-2026-07-06.md
113 lines
4.1 KiB
Python
113 lines
4.1 KiB
Python
#!/usr/bin/env python3
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"""
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RMI AI Pipeline Part 2 - Remaining 7 Modules
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=============================================
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Community Forensics | Cross-Chain Entity | Ghost Blog | Social Media | Token Compare
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All Ollama Cloud deepseek-v4-flash. ~$0.001/operation.
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"""
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import json
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import logging
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import os
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from urllib.request import Request, urlopen
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logger = logging.getLogger("rmi.ai_pipeline2")
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OLLAMA_KEY = os.getenv("OLLAMA_API_KEY", os.getenv("DEEPSEEK_API_KEY", ""))
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OLLAMA_URL = "https://ollama.com/v1/chat/completions"
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MODEL = "deepseek-v4-flash"
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def _call_ai(system: str, prompt: str, max_tokens: int = 250, temp: float = 0.3) -> str:
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try:
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body = json.dumps(
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{
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"model": MODEL,
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"messages": [
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{"role": "system", "content": system},
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{"role": "user", "content": prompt},
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],
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"max_tokens": max_tokens,
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"temperature": temp,
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}
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).encode()
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req = Request(
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OLLAMA_URL,
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data=body,
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headers={"Authorization": f"Bearer {OLLAMA_KEY}", "Content-Type": "application/json"},
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)
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resp = urlopen(req, timeout=15)
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return json.loads(resp.read())["choices"][0]["message"]["content"].strip()
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except Exception as e:
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logger.error(f"AI call failed: {e}")
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return ""
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# ── 8. COMMUNITY FORENSICS AUTO-ANALYSIS ──
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FORENSICS_SYSTEM = """You are a crypto forensics investigator. A community member submitted a suspicious token for review.
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Analyze the information and provide:
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1. Initial verdict (LIKELY SCAM / SUSPICIOUS / NEEDS MORE INFO)
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2. Key concerns (2-3 bullet points)
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3. Recommended next steps for the investigator
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Keep it under 150 words."""
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def analyze_community_submission(submission: dict) -> str:
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return _call_ai(FORENSICS_SYSTEM, json.dumps(submission)[:1500], max_tokens=250)
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# ── 10. CROSS-CHAIN ENTITY DETECTION ──
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CROSSCHAIN_SYSTEM = """You identify crypto entities operating across multiple blockchains.
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Given wallet data from different chains, determine if they're the same entity.
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Reply format: MATCH|confidence_0-100|reason OR NO_MATCH|reason"""
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def detect_cross_chain(wallets: dict) -> str:
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return _call_ai(CROSSCHAIN_SYSTEM, json.dumps(wallets)[:1500], max_tokens=100)
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# ── 11. GHOST BLOG AUTO-DRAFT ──
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GHOST_SYSTEM = """You are a crypto security blogger for Rug Munch Intelligence (rugmunch.io).
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Write a blog post draft from scanner data and incident reports.
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Structure:
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- Title (catchy, SEO-friendly, under 80 chars)
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- Hook (1 sentence that grabs attention)
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- Body (3-4 paragraphs explaining the threat)
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- Key takeaways (2-3 bullet points)
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- Call to action (check your tokens, use our scanner)
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Use markdown formatting. Professional but engaging tone."""
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def draft_blog_post(topic: str, data: dict) -> str:
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prompt = f"Topic: {topic}\n\nData:\n{json.dumps(data)[:2000]}"
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return _call_ai(GHOST_SYSTEM, prompt, max_tokens=500, temp=0.6)
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# ── 13. SOCIAL MEDIA POST GENERATOR ──
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SOCIAL_SYSTEM = """You are the social media manager for Rug Munch Intelligence (@CryptoRugMunch).
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Write a tweet/telegram post about a crypto security finding.
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Rules:
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- Under 280 chars for Twitter, under 500 for Telegram
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- Start with a hook (stat, warning, or question)
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- Include $TICKER if relevant
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- End with a call to action or link
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- Use emojis sparingly (1-2 max)
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- No hashtag spam (2-3 max)
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Reply format: TWITTER: <tweet> | TELEGRAM: <post>"""
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def generate_social_post(incident: dict, platform: str = "both") -> str:
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return _call_ai(SOCIAL_SYSTEM, json.dumps(incident)[:1000], max_tokens=200, temp=0.7)
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# ── 14. TOKEN COMPARISON ENGINE ──
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COMPARE_SYSTEM = """Compare two crypto tokens for safety. Given their scanner results, determine which is safer and why.
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Reply format:
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SAFER: <token_name>
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REASON: <2-3 sentence comparison>
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SCORE_DIFF: <token1_score> vs <token2_score>
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KEY_DIFFERENCES: <bullet points>"""
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def compare_tokens(token_a: dict, token_b: dict) -> str:
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prompt = f"Token A:\n{json.dumps(token_a)[:800]}\n\nToken B:\n{json.dumps(token_b)[:800]}"
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return _call_ai(COMPARE_SYSTEM, prompt, max_tokens=200)
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