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feat(academic-audit): InteractionLogger, AcademicAuditTrail e TokenEconomyMonitor v1.0
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‎.evolve/audit-logs/session-SESSION-20260524-161634-3C6F.jsonl‎

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{"type": "session_start", "session_id": "SESSION-20260524-161634-3C6F", "timestamp": "2026-05-24T16:16:34.650725+00:00", "version": "1.0.0", "paradigm": "", "level": 1}
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{"type": "paradigm_set", "session_id": "SESSION-20260524-161634-3C6F", "timestamp": "2026-05-24T16:16:34.653739+00:00", "paradigm": "Pragmatista (mista)"}
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{"type": "interaction", "session_id": "SESSION-20260524-161634-3C6F", "interaction_id": "INT-0001", "timestamp": "2026-05-24T16:16:34.655653+00:00", "query": "PIB do Brasil em 2023", "response_summary": "PIB per capita: US$ 10,043.62 (World Bank WDI)", "routing": {"domain": "economic", "source": "WorldBankAnalyzer", "confidence": 0.67, "fallback_used": false, "fallback_chain": []}, "tokens": {"estimated_input": 800, "estimated_output": 300, "context_size": 200000, "efficiency_ratio": 0.0, "level": 2}, "pipeline_stage": "SEEKER", "decision_path": ["parse_intent", "route_economic", "wbgapi_fetch"], "paradigm": "Pragmatista (mista)", "hash": "7b51d4b265d38e1e"}
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{"type": "interaction", "session_id": "SESSION-20260524-161634-3C6F", "interaction_id": "INT-0002", "timestamp": "2026-05-24T16:16:34.662222+00:00", "query": "artigos sobre IA", "response_summary": "", "routing": {"domain": "", "source": "", "confidence": 0.0, "fallback_used": false, "fallback_chain": []}, "tokens": {"estimated_input": 0, "estimated_output": 0, "context_size": 200000, "efficiency_ratio": 0.0, "level": 2}, "pipeline_stage": "SEEKER", "decision_path": [], "paradigm": "Pragmatista (mista)", "hash": "8014fe2d3456cbf2"}
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{"type": "decision", "session_id": "SESSION-20260524-161634-3C6F", "timestamp": "2026-05-24T16:16:34.663240+00:00", "interaction_id": "INT-0002", "decision": "Fonte primaria: arXiv", "rationale": "Cobertura superior a Semantic Scholar", "context": "", "stage": ""}
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{"type": "artifact", "session_id": "SESSION-20260524-161634-3C6F", "timestamp": "2026-05-24T16:16:34.664244+00:00", "artifact_type": "article_draft", "artifact_path": "artigo_v1.md", "metadata": {"words": 3500}}
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{"type": "paradigm_set", "session_id": "SESSION-20260524-161634-3C6F", "timestamp": "2026-05-24T16:16:34.666243+00:00", "paradigm": "Pragmatista (mista)"}
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{"type": "decision", "session_id": "SESSION-20260524-161634-3C6F", "timestamp": "2026-05-24T16:16:34.670287+00:00", "interaction_id": "INT-0002", "decision": "Paradigma definido: Pragmatista (mista)", "rationale": "Escolha consciente do pesquisador", "context": "AcademicAuditTrail.set_paradigm", "stage": "AcademicAuditTrail.set_paradigm"}
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{"type": "artifact", "session_id": "SESSION-20260524-161634-3C6F", "timestamp": "2026-05-24T16:16:34.671301+00:00", "artifact_type": "paragraph", "artifact_path": "P01", "metadata": {"text_length": 67}}
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{"type": "artifact", "session_id": "SESSION-20260524-161634-3C6F", "timestamp": "2026-05-24T16:16:34.672304+00:00", "artifact_type": "paragraph", "artifact_path": "P02", "metadata": {"text_length": 76}}
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{"type": "decision", "session_id": "SESSION-20260524-161634-3C6F", "timestamp": "2026-05-24T16:16:34.673303+00:00", "interaction_id": "INT-0002", "decision": "Evidência vinculada: 10.1038/s41524-017-0032-0 → P01", "rationale": "IA generativa na ciencia dos materiais", "context": "record_evidence", "stage": "record_evidence"}
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{"type": "decision", "session_id": "SESSION-20260524-161634-3C6F", "timestamp": "2026-05-24T16:16:34.677501+00:00", "interaction_id": "INT-0002", "decision": "Evidência vinculada: arXiv:2301.12345 → P01", "rationale": "Survey de LLMs", "context": "record_evidence", "stage": "record_evidence"}
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{"type": "decision", "session_id": "SESSION-20260524-161634-3C6F", "timestamp": "2026-05-24T16:16:34.679013+00:00", "interaction_id": "INT-0002", "decision": "Evidência vinculada: 10.1596/978-1-4648-1328-8 → P02", "rationale": "World Bank P&D investment data", "context": "record_evidence", "stage": "record_evidence"}
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{"type": "artifact", "session_id": "SESSION-20260524-161634-3C6F", "timestamp": "2026-05-24T16:16:34.682561+00:00", "artifact_type": "audit_report", "artifact_path": "C:\\Users\\marce\\.config\\opencode\\.evolve\\audit-trails\\audit-SESSION-20260524-161634-3C6F.json", "metadata": {}}
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{"type": "session_end", "session_id": "SESSION-20260524-161634-3C6F", "timestamp": "2026-05-24T16:16:34.686596+00:00", "total_interactions": 2, "paradigm": "Pragmatista (mista)", "level": 2, "log_file": "C:\\Users\\marce\\.config\\opencode\\.evolve\\audit-logs\\session-SESSION-20260524-161634-3C6F.jsonl"}

‎.evolve/audit-trails/audit-SESSION-20260524-161634-3C6F.json‎

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{
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"session_id": "SESSION-20260524-161634-3C6F",
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"paradigm": "Pragmatista (mista)",
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"timestamp": "2026-05-24T16:16:34.680544+00:00",
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"summary": {
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"paragraphs": 2,
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"sources": 3,
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"total_evidence": 3
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},
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"citation_map": {
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"10.1038/s41524-017-0032-0": [
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"P01"
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],
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"arXiv:2301.12345": [
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"P01"
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],
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"10.1596/978-1-4648-1328-8": [
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"P02"
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]
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},
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"paragraphs": {
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"P01": {
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"tsac_score": 0,
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"tsac_violations": [],
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"evidence_count": 2,
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"peer_reviewed": true
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},
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"P02": {
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"tsac_score": 0,
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"tsac_violations": [],
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"evidence_count": 1,
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"peer_reviewed": false
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}
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}
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}

‎.evolve/token-monitor/tokens-SESSION-20260524-161634-3C6F.md‎

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# Relatório de Economia de Tokens
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**Nível**: 2 — Standard Paper/Q1-Q2
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**Modo economia**: Sim
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## Consumo
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| Métrica | Valor |
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|---------|-------|
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| Interações | 5 |
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| Tokens entrada | 4,000 |
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| Tokens saída | 1,500 |
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| Tokens totais | 5,500 |
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| Orçamento | 200,000 |
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| Restante | 194,500 (2.8%) |
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| Eficiência (out/in) | 0.38 |
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| Médio in/interação | 800 |
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| Médio out/interação | 300 |
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## Economias Estimadas
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| Estratégia | Tokens Economizados |
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|-----------|-------------------:|
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| Contexto em chinês (+40%) | 1,600 |
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| Progressive disclosure (25%) | 1,000 |
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| MCP Lazy Init (10%) | 400 |
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| **Total estimado** | **3,000** |

‎skills/system/academic-audit/SKILL.md‎

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# academic-audit: Auditoria Acadêmica Caixa Branca com Rastreamento Minucioso
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**Status**: v1.0.0 | **Categoria**: system | **Tipo**: Auditoria + Logging
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## Descrição
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Sistema completo de auditoria acadêmica caixa branca que registra **todas** as interações do pesquisador com o ecossistema OpenCode, fornecendo rastreabilidade minuciosa de cada afirmação → evidência → decisão. Compatível com protocolo TSAC (87 palavras banidas), padrões Qualis A1 e paradigmas epistemológicos múltiplos.
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## Componentes
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| Módulo | Arquivo | Função |
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|--------|---------|--------|
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| **InteractionLogger** | `interaction_logger.py` | Logging caixa branca de todas as interações (JSONL imutável) |
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| **AcademicAuditTrail** | `academic_audit_trail.py` | Trilha de auditoria: parágrafo → evidência → fonte |
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| **TokenEconomyMonitor** | `token_economy_monitor.py` | Monitor de consumo de tokens por nível (1/2/3) |
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## Uso Rápido
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```python
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from interaction_logger import get_logger
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from academic_audit_trail import AcademicAuditTrail
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from token_economy_monitor import TokenEconomyMonitor
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# Iniciar sessão
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logger = get_logger()
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logger.set_paradigm("Pragmatista (mista)")
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logger.set_level(2) # Standard Paper
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# Trilha de auditoria
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trail = AcademicAuditTrail()
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trail.set_paradigm("Pragmatista (mista)")
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# Registrar parágrafo + evidência
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trail.record_paragraph("P12", "A IA generativa tem transformado...")
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trail.record_evidence("P12", "10.1038/s41524-017-0032-0",
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claim="IA generativa na ciência dos materiais",
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source_type="doi", confidence=0.95)
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# Verificar TSAC
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trail.run_tsac_check("P12")
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# Gerar relatório
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report = trail.generate_audit_report(format="markdown")
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trail.save()
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# Monitor de tokens
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monitor = TokenEconomyMonitor(level=2)
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monitor.record_usage("INT-0001", estimated_input=500, estimated_output=200)
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print(monitor.get_efficiency_report())
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# Fechar sessão
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logger.close_session()
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```
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## Estratégias de Economia de Tokens (3 Níveis)
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| Nível | Nome | Agentes | Orçamento/Sessão | Economia |
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|:-----:|------|:------:|:----------------:|:--------:|
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| 1 | Magnum/Tese/Qualis A1 | 43 | 500K tokens | Nenhuma (rigor máximo) |
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| 2 | Standard Paper/Q1-Q2 | 20 | 200K tokens | Exigida |
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| 3 | Short Communication | 10 | 50K tokens | Máxima |
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## Protocolo TSAC
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Lista de 87 palavras banidas detectadas automaticamente. Exemplos: "crucial", "essencialmente", "notavelmente", "fundamentalmente", "intrinsecamente".
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## Rastreabilidade
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Cada interação gera um registro JSONL imutável com:
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- `session_id` único
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- `interaction_id` sequencial
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- Hash SHA-256 para integridade
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- Timestamp UTC-3
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- Roteamento completo (domínio, fonte, confiança)
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- Decisões do pipeline
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## Arquivos
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- `interaction_logger.py` — Logger caixa branca (append-only JSONL)
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- `academic_audit_trail.py` — Trilha de auditoria acadêmica
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- `token_economy_monitor.py` — Monitor de economia de tokens

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