Dark Factory

Overview

The dark factory is the execution engine of the three-skill pipeline. It takes a structured specification produced by intent-engineering, validates it, runs behavioral tests, generates code, executes tests, and produces a cryptographically signed Provable Outcome Report — all autonomously.

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                      Agent (Orchestrator)                       │
└─────────────────────────────────────────────────────────────────┘
                              ▲
                              │
        ┌─────────────────────┼─────────────────────┐
        │                     │                     │
        ▼                     ▼                     ▼
┌──────────────────┐  ┌──────────────────┐  ┌──────────────────┐
│ intent-          │  │ dark-factory     │  │ feedback-loop    │
│ engineering      │  │                  │  │                  │
│ (The "Why")      │  │ (The "How")      │  │ (The "Learn")    │
└──────────────────┘  └──────────────────┘  └──────────────────┘
        │                     │                     │
        └─────────────────────┼─────────────────────┘
                              ▼
                    ┌──────────────────┐
                    │  Shared Data     │
                    │  Contracts       │
                    └──────────────────┘

Workflow

Step 1 — Validate the Specification

python /home/ubuntu/skills/dark-factory/scripts/specification_validator.py my_spec.json

Checks required fields, validates structure, ensures behavioral scenarios are complete, and provides warnings.

Step 2 — Run Behavioral Tests

python /home/ubuntu/skills/dark-factory/scripts/behavioral_test_engine.py my_spec.json

Executes all behavioral scenarios against a mock environment, calculates pass rates, and generates a test report.

Step 3 — Run the Full Dark Factory

python /home/ubuntu/skills/dark-factory/scripts/orchestrator.py my_spec.json

The orchestrator runs the complete workflow in sequence:

1. Load and validate the specification
2. Execute behavioral tests
3. Generate code (AI agent integration point)
4. Execute unit and integration tests
5. Generate the signed outcome report

Output: \x3Cspec-name>_outcome_report.json

Data Contracts

Input — Specification from intent-engineering

{
  "specification_id": "spec-12345678",
  "title": "Feature Name",
  "description": "What should be built",
  "behavioral_scenarios": [
    {
      "scenario": "Description",
      "input": {},
      "expected_output": {}
    }
  ],
  "success_criteria": {
    "test_pass_rate": 0.95
  }
}

Output — Provable Outcome Report

{
  "report_id": "report-12345678",
  "specification_id": "spec-12345678",
  "status": "success",
  "generated_code": {},
  "test_results": {},
  "security_evidence": {},
  "cryptographic_signature": {}
}

Key Features

The dark factory provides four core capabilities. Specification-Driven Development ensures all execution is grounded in a validated, human-readable specification before any code is generated. Behavioral Validation runs all scenarios against a mock environment first, catching ambiguities early. Autonomous Execution coordinates code generation, unit testing, integration testing, and deployment without human intervention. Provable Outcomes produce a cryptographically signed report that can be independently verified and fed into the feedback-loop for continuous improvement.

Key Metrics

Use Cases

Autonomous Skill Development — define a specification in intent-engineering, run the dark factory to build the skill autonomously, then verify with feedback-loop.

Specification-Driven Testing — validate and test a specification before committing to implementation using specification_validator.py and behavioral_test_engine.py independently.

Continuous Integration — integrate into CI/CD pipelines by running the validator and orchestrator as pipeline steps.

Resources