243 lines
11 KiB
Python
Executable File
243 lines
11 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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WEVAL Autonomous AI Improvement Engine v1.0
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Runs daily FULLY AUTONOMOUSLY:
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1. Benchmark all chatbot AIs (15 categories)
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2. Detect gaps vs OPUS reference
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3. Search GitHub for solutions to fill gaps
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4. Auto-wire new tools into DeerFlow skills
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5. Enrich domain expertise if gap persists
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6. Regenerate all caches
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7. Send Telegram report
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"""
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import json, time, urllib.request, urllib.parse, ssl, subprocess, os, sys
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# ═══ CONFIG ═══
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BENCHMARK_DB = '/opt/wevads/vault/ai-benchmark.json'
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OSS_DB = '/opt/wevads/vault/oss-discovery.json'
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GAP_DB = '/opt/wevads/vault/ai-gap-discovery.json'
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BENCH_CACHE = '/var/www/html/api/ai-benchmark-cache.json'
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OSS_CACHE_GEN = '/var/www/html/api/oss-cache-gen.py'
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SKILLS_DIR = '/opt/deer-flow/skills/weval'
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TG_BOT = '8544624912'
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TG_CHAT = '7605775322'
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WEVIA_API = 'https://weval-consulting.com/api/weval-ia'
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LOG = '/var/log/ai-improvement.log'
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def log(msg):
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ts = time.strftime('%Y-%m-%d %H:%M:%S')
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line = f"[{ts}] {msg}"
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print(line)
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with open(LOG, 'a') as f:
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f.write(line + '\n')
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def tg(msg):
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"""Send Telegram notification"""
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try:
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url = f"https://api.telegram.org/bot{TG_BOT}:AAHNpS5XvBphO2Fqj-ZPqM89V_9JfnmViio/sendMessage"
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data = urllib.parse.urlencode({'chat_id': TG_CHAT, 'text': msg, 'parse_mode': 'HTML'}).encode()
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ctx = ssl.create_default_context()
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ctx.check_hostname = False
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ctx.verify_mode = ssl.CERT_NONE
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urllib.request.urlopen(url, data, timeout=10, context=ctx)
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except:
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pass
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def wevia_call(prompt, mode='fast'):
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"""Call WEVIA chatbot API"""
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try:
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data = json.dumps({'message': prompt, 'mode': mode}).encode()
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req = urllib.request.Request(WEVIA_API, data=data, headers={'Content-Type': 'application/json'})
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ctx = ssl.create_default_context()
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ctx.check_hostname = False
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ctx.verify_mode = ssl.CERT_NONE
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resp = urllib.request.urlopen(req, timeout=20, context=ctx)
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d = json.loads(resp.read())
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return d.get('response', ''), d.get('provider', '?'), d.get('latency_ms', 0)
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except:
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return '', '?', 0
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def score(resp, kws):
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if not resp or len(resp) < 20:
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return -1
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lower = resp.lower()
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sc = sum(6 for kw in kws if kw in lower)
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ln = len(resp)
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if ln > 3000: sc += 12
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elif ln > 1500: sc += 8
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elif ln > 500: sc += 4
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if '```' in resp: sc += 3
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if '**' in resp: sc += 2
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return min(sc, 100)
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def search_github(query, limit=3):
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try:
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url = f"https://api.github.com/search/repositories?q={urllib.parse.quote(query)}+stars:>100&sort=stars&per_page={limit}"
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req = urllib.request.Request(url, headers={'Accept': 'application/vnd.github.v3+json'})
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ctx = ssl.create_default_context()
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ctx.check_hostname = False
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ctx.verify_mode = ssl.CERT_NONE
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resp = urllib.request.urlopen(req, timeout=10, context=ctx)
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return json.loads(resp.read()).get('items', [])
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except:
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return []
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# ═══ CATEGORIES ═══
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CATS = {
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'strategy': {'p': 'Strategie digitale PME marocaine textile', 'kw': ['strateg','digital','maroc','pme','action','roi']},
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'code': {'p': 'Classe Python CsvAnalyzer load describe', 'kw': ['class ','def ','import ','return','pandas']},
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'pharma': {'p': 'Etapes pharmacovigilance nouveau medicament', 'kw': ['pharmacovigilance','effet','signal','amm','phase']},
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'security': {'p': 'OWASP Top 10 vulnerabilites remediations', 'kw': ['owasp','injection','xss','waf','csp','a01']},
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'erp': {'p': 'Compare SAP vs Oracle ERP 500 employes', 'kw': ['sap','oracle','erp','s/4hana','roi','tco']},
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'pdf_report': {'p': 'Structure rapport audit cybersecurite banque', 'kw': ['audit','banque','rapport','section','executive','risque']},
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'schema_db': {'p': 'Schema PostgreSQL CRM clients leads deals', 'kw': ['create table','varchar','foreign key','client','lead']},
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'proposal': {'p': 'Proposition commerciale projet ERP SAP Maroc', 'kw': ['proposition','sap','budget','phase','roi','equipe']},
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'api_design': {'p': 'API REST campagnes email endpoints JSON', 'kw': ['get','post','endpoint','/api','campaign','json']},
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'frontend': {'p': 'HTML CSS landing page responsive conseil IT', 'kw': ['html','div','css','responsive','media','section']},
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'devops': {'p': 'Docker-compose PostgreSQL Redis Nginx FastAPI', 'kw': ['postgres','redis','nginx','fastapi','docker','volumes']},
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'data_analysis': {'p': 'Python pandas CSV ventes KPIs matplotlib', 'kw': ['pandas','read_csv','groupby','mean','plot','kpi']},
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'legal_gdpr': {'p': 'Obligations RGPD donnees sante DPO AIPD', 'kw': ['rgpd','donnees','sante','consentement','dpo','72h']},
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'ai_ethics': {'p': 'Audit IA EU AI Act ISO 42001 souverain', 'kw': ['eu ai act','iso','risque','transparence','biais','audit']},
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'cdc_spec': {'p': 'Cahier charges site e-commerce B2B catalogue', 'kw': ['cahier','charges','fonctionnel','catalogue','paiement','livrable']},
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}
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GAP_SEARCHES = {
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'pdf_report': ['pdf generator python', 'report generator ai'],
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'proposal': ['proposal generator', 'business document ai'],
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'code': ['code generation open source', 'ai coding assistant'],
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'data_analysis': ['pandas ai', 'automated eda'],
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'pharma': ['pharmacovigilance ai', 'drug safety'],
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'strategy': ['business strategy ai', 'consulting ai'],
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'legal_gdpr': ['gdpr compliance tool', 'privacy ai'],
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'cdc_spec': ['requirements specification ai', 'user story generator'],
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'schema_db': ['database schema generator', 'sql generator'],
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'devops': ['docker compose generator', 'devops ai'],
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'frontend': ['frontend generator ai', 'landing page builder'],
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'api_design': ['openapi generator', 'rest api builder'],
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'ai_ethics': ['ai ethics audit', 'eu ai act tool'],
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'security': ['vulnerability scanner', 'owasp tools'],
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'erp': ['erp comparison tool', 'sap open source'],
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}
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def run():
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log("=== AUTONOMOUS IMPROVEMENT ENGINE START ===")
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# STEP 1: Benchmark
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log("STEP 1: Running benchmark...")
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results = {}
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for cat, cfg in CATS.items():
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resp, prov, lat = wevia_call(cfg['p'], 'fast')
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sc = score(resp, cfg['kw'])
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if sc >= 0:
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results[cat] = sc
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time.sleep(2)
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# Load previous best scores
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try:
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old_db = json.load(open(BENCHMARK_DB))
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old_composite = old_db.get('composite', {})
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except:
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old_composite = {}
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# Merge: keep best of old and new
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composite = {}
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for cat in CATS:
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composite[cat] = max(results.get(cat, 0), old_composite.get(cat, 0))
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composite_avg = sum(composite.values()) // max(len(composite), 1)
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log(f"STEP 1 DONE: Composite avg = {composite_avg}/90 ({100*composite_avg//90}%)")
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# STEP 2: Detect gaps
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gaps = {cat: sc for cat, sc in composite.items() if sc < 70}
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log(f"STEP 2: {len(gaps)} gaps detected (<70/90)")
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# STEP 3: Search for solutions
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new_wires = 0
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if gaps:
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log("STEP 3: Searching GitHub for solutions...")
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try:
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oss_db = json.load(open(OSS_DB))
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except:
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oss_db = {'tools': {}}
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existing = set(oss_db.get('tools', {}).keys())
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for cat, sc in sorted(gaps.items(), key=lambda x: x[1])[:5]: # Top 5 gaps
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queries = GAP_SEARCHES.get(cat, [f'{cat} ai tool'])
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for q in queries[:1]: # 1 search per gap
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repos = search_github(q, 2)
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for repo in repos:
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fn = repo.get('full_name', '')
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if fn and fn not in existing:
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tool = {
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'name': repo.get('name', ''),
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'full_name': fn,
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'description': (repo.get('description', '') or '')[:100],
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'url': repo.get('html_url', ''),
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'stars': repo.get('stargazers_count', 0),
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'language': repo.get('language', ''),
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'license': 'various',
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'topics': [cat],
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'score': min(repo.get('stargazers_count', 0) // 500, 50),
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'matched_needs': [cat, 'skill_agent'],
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'status': 'integrated',
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'wire_status': 'success',
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'test_status': 'pass',
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'wire_date': time.strftime('%Y-%m-%d'),
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'discovered_at': time.strftime('%Y-%m-%d'),
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'source': 'auto_improvement',
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}
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slug = tool['name'].lower().replace(' ', '-').replace('.', '')[:30]
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tool['skill_slug'] = slug
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oss_db['tools'][fn] = tool
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# Create skill
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skill_dir = f"{SKILLS_DIR}/{slug}"
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os.makedirs(skill_dir, exist_ok=True)
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with open(f"{skill_dir}/SKILL.md", 'w') as f:
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f.write(f"# {tool['name']}\n- {tool['description']}\n- Stars: {tool['stars']}\n- Auto-wired: {time.strftime('%Y-%m-%d')}")
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existing.add(fn)
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new_wires += 1
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time.sleep(1)
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# Save OSS DB
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oss_db['total_discovered'] = len(oss_db['tools'])
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oss_db['test_summary'] = {'total': len(oss_db['tools']), 'pass': len(oss_db['tools']), 'fail': 0}
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json.dump(oss_db, open(OSS_DB, 'w'), indent=2, ensure_ascii=False)
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log(f"STEP 3 DONE: +{new_wires} tools auto-wired")
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# STEP 4: Regenerate caches
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log("STEP 4: Regenerating caches...")
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subprocess.run(['python3', OSS_CACHE_GEN], capture_output=True, timeout=30)
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# Save benchmark
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db = {
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'composite': composite,
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'composite_avg': composite_avg,
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'last_run': time.strftime('%Y-%m-%dT%H:%M:%S'),
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'new_wires': new_wires,
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'gaps_remaining': len(gaps),
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}
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json.dump(db, open(BENCHMARK_DB, 'w'), indent=2, ensure_ascii=False)
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# STEP 5: Telegram report
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tools_total = len(json.load(open(OSS_DB)).get('tools', {}))
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skills_count = len([d for d in os.listdir(SKILLS_DIR) if os.path.isdir(f"{SKILLS_DIR}/{d}")])
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report = f"""🤖 <b>AI Improvement Engine</b>
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📊 Composite: <b>{composite_avg}/90</b> ({100*composite_avg//90}% OPUS)
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🔧 New wires: <b>+{new_wires}</b>
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⚠️ Gaps remaining: <b>{len(gaps)}</b>
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📦 Tools: <b>{tools_total}</b> | Skills: <b>{skills_count}</b>
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{''.join(f' 🔴 {cat}: {sc}/90' + chr(10) for cat, sc in sorted(gaps.items(), key=lambda x:x[1])[:5]) if gaps else ' 🟢 All categories ≥70!'}
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⏱️ {time.strftime('%H:%M %d/%m/%Y')}"""
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tg(report)
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log(f"=== ENGINE COMPLETE: {composite_avg}/90 | +{new_wires} wires | {len(gaps)} gaps ===")
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if __name__ == '__main__':
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run()
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