GitHub - maciejjankowski/nsens-framework: Decision Governance Framework - validate ideas before building them. 51 cognitive biases, 4 adversarial personas, phase-gated validation P0-P5.

from nsens import AdversarialGate

# Quick adversarial review of a business idea
gate = AdversarialGate(level="medium")

result = gate.validate({
    "topic": "AI-powered code review tool",
    "initial_confidence": 0.85,
    "pricing": {"model": "$50/seat/month", "validation": "None"},
    "gtm": {"customer_interviews": 3, "sales_process": "Unclear"},
    "features": ["PR review", "Security scan", "Style check", "Docs gen",
                  "Test gen", "Perf analysis", "Dep audit", "Migration",
                  "Refactoring", "API docs", "Schema gen"],
    "technical": {"tech_stack": "kubernetes microservices"},
    "prolog_constraints": 2,
})

print(f"Score: {result['initial_score']:.2f} -> {result['final_score']:.2f}")
print(f"Recommendation: {result['recommendation']}")
print(f"Issues found: {len(result['issues_found'])}")

for issue in result['issues_found']:
    print(f"  [{issue['category']}] {issue['issue']}")
    print(f"    -> {issue['feedback']}")

Score: 0.85 -> 0.43
Recommendation: DON'T BUILD
Issues found: 8
  [revenue_inflation] Pricing model has no validation
    -> Pricing needs customer validation. Talk to 10 customers.
  [gtm_broken] Only 3 customer interviews (need 10+)
    -> Need 10+ customer interviews. You have 3.
  [feature_bloat] 11 features planned (recommend max 5 for MVP)
    -> 11 features is too many for MVP. Focus on 3-5 core features.
  [over_engineering] Over-engineered architecture for MVP
    -> Start simple: monolith. Scale when you have users.
  ...

from pathlib import Path
from nsens import NineSENSEngine

engine = NineSENSEngine(
    plugins_dir=Path("plugins"),
    phases_dir=Path("phases"),
    config_path="config.yaml",
)

result = engine.execute_phase(
    phase_name="p0_research",
    context={
        "idea": "AI-powered supply chain optimizer",
        "target_market": "Mid-size manufacturers",
        "budget": 100,
        "project_name": "supply-chain-ai",
        "next_phase": "p1_discovery",
    },
)

print(f"Status: {result.status}")
print(f"Plugins activated: {result.activated_plugins}")
print(f"Validations: {len(result.validations)}")

nsens/
  core/
    engine.py              # Phase orchestrator
    adversarial_gate.py    # 4-persona adversarial review
    plugin_registry.py     # Smart plugin activation
    validator.py           # Multi-layer validation
    config_loader.py       # YAML config with validation
    decision_log.py        # Append-only audit trail
    handoff_system.py      # Ultra-compressed phase handoffs
    auditable_decision_logger.py  # JSONL + Markdown logging
  catalogs/                # Biases, models, personas, prompts
  phases/                  # P0-P5 phase definitions
  plugins/                 # Extensible validation modules

from nsens.core.plugin_registry import Plugin, PluginMetadata, PluginCategory, PluginPriority, PluginCapability

class MyValidator:
    @property
    def metadata(self) -> PluginMetadata:
        return PluginMetadata(
            name="my_validator",
            category=PluginCategory.BUSINESS,
            capabilities=[
                PluginCapability(
                    name="unit_economics",
                    description="Validates unit economics are viable",
                    inputs=["pricing", "costs"],
                    outputs=["unit_economics_result"],
                )
            ],
            phases=["p0_research", "p2_design"],
            priority=PluginPriority.HIGH,
        )

    def execute(self, context):
        # Your validation logic here
        return {"unit_economics_result": {"viable": True, "ltv_cac_ratio": 4.2}}

    def validate_inputs(self, context):
        return "pricing" in context

    def validate_outputs(self, result):
        return "unit_economics_result" in result