135 lines
5.4 KiB
Python
135 lines
5.4 KiB
Python
"""
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Analyzer cross-skill.
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Identifica padroes compartilhados entre skills que poderiam ser
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extraidos em modulos comuns, reducindo duplicacao.
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"""
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from __future__ import annotations
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import re
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from pathlib import Path
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from typing import Any, Dict, List, Tuple
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def analyze_cross_skill(all_skills: List[Dict[str, Any]]) -> Tuple[float, List[Dict[str, Any]]]:
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"""
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Analisa padroes cross-skill. Diferente dos outros analyzers,
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recebe TODAS as skills e retorna findings globais.
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Retorna (score_medio, findings).
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"""
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findings: List[Dict[str, Any]] = []
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if len(all_skills) < 2:
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return 100.0, findings
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# -- 1. Detectar modulos duplicados (mesmo nome de arquivo em multiplas skills) ---
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module_presence: Dict[str, List[str]] = {}
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for skill in all_skills:
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for rel_path in skill.get("python_files", []):
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filename = Path(rel_path).name
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module_presence.setdefault(filename, []).append(skill["name"])
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shared_modules = {k: v for k, v in module_presence.items() if len(v) > 1}
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if shared_modules:
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for module, skills in shared_modules.items():
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if module in ("__init__.py", "requirements.txt"):
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continue
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findings.append({
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"skill_name": "cross-skill",
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"dimension": "cross_skill",
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"severity": "medium",
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"category": "duplicated_module",
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"title": f"Modulo '{module}' presente em {len(skills)} skills",
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"description": f"Skills: {', '.join(skills)}. "
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"Potencial para extrair em modulo compartilhado.",
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"recommendation": f"Avaliar se {module} pode ser extraido para uma lib compartilhada "
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f"(ex: shared-core/scripts/{module})",
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"effort": "medium",
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"impact": "high",
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})
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# -- 2. Padroes de Database (mesmo pattern _connect) --------------------------
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db_skills = [s for s in all_skills if s.get("has_db")]
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if len(db_skills) > 1:
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findings.append({
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"skill_name": "cross-skill",
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"dimension": "cross_skill",
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"severity": "low",
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"category": "shared_db_pattern",
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"title": f"{len(db_skills)} skills com modulo db.py independente",
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"description": f"Skills: {', '.join(s['name'] for s in db_skills)}. "
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"Padrao Database._connect() com WAL mode eh repetido.",
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"recommendation": "Criar classe BaseDatabase em shared-core com _connect(), "
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"e cada skill herdar e definir apenas suas tabelas.",
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"effort": "medium",
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"impact": "medium",
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})
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# -- 3. Padroes de Config (mesmo pattern ROOT_DIR) ----------------------------
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config_skills = [s for s in all_skills if s.get("has_config")]
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if len(config_skills) > 1:
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findings.append({
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"skill_name": "cross-skill",
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"dimension": "cross_skill",
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"severity": "info",
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"category": "shared_config_pattern",
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"title": f"{len(config_skills)} skills com config.py similar",
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"description": "Padrao de paths (ROOT_DIR, DATA_DIR, etc) repetido.",
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"recommendation": "Considerar base_config.py compartilhado com paths padrao",
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"effort": "low",
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"impact": "low",
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})
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# -- 4. Skills sem governanca vs com governanca --------------------------------
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gov_skills = [s for s in all_skills if s.get("has_governance")]
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no_gov_skills = [s for s in all_skills if not s.get("has_governance")]
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if gov_skills and no_gov_skills:
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findings.append({
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"skill_name": "cross-skill",
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"dimension": "cross_skill",
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"severity": "medium",
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"category": "inconsistent_governance",
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"title": "Governanca inconsistente entre skills",
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"description": f"Com governanca: {', '.join(s['name'] for s in gov_skills)}. "
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f"Sem governanca: {', '.join(s['name'] for s in no_gov_skills)}.",
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"recommendation": "Padronizar governanca em todas as skills. "
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"Extrair GovernanceManager para modulo compartilhado.",
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"effort": "medium",
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"impact": "high",
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})
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# -- 5. Padroes de export (json/csv/parquet) -----------------------------------
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export_skills = []
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for skill in all_skills:
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for rel_path in skill.get("python_files", []):
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if "export" in rel_path.lower():
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export_skills.append(skill["name"])
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break
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if len(export_skills) > 1:
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findings.append({
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"skill_name": "cross-skill",
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"dimension": "cross_skill",
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"severity": "low",
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"category": "shared_export_pattern",
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"title": f"{len(export_skills)} skills com modulo de export",
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"description": f"Skills: {', '.join(export_skills)}",
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"recommendation": "Considerar export utils compartilhados (json/csv/parquet)",
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"effort": "low",
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"impact": "low",
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})
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# Score baseado na quantidade de duplicacao encontrada
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score = 100.0
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for f in findings:
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if f["severity"] == "medium":
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score -= 8
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elif f["severity"] == "low":
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score -= 3
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elif f["severity"] == "info":
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score -= 1
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return max(0.0, min(100.0, score)), findings
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