Merge pull request #1 from FlashMS/core-architecture-v0.1

Gravity Binary v0.1 — Core Architecture Publication

Author: FlashMS

README.md

@@ -1,82 +1,69 @@
-Gravity Binary · Modular Trust Engine
-Gravity Binary is a living architecture designed to externalize trust through capsule‑based validation, symbolic execution, and recruiter‑facing clarity.
-
-Reflex Audit Capsule
-- Generated: 2025‑09‑19 23:01:01 EDT
-- Author: Todd Wolf
-Reflex Timeline
-- v106 → v107 → v108: Reflex echo confirmed
-- v109, v109+, v110: Symbolic recursion attempted, no trigger
-- Mutation Injected: Capsule 2B‑Δ promoted to Tier 3
-- Registry Restored: Multiple timestamps
-- Synthetic Commit Injected: Fingerprint SC‑Δ
-- UCE Echo Dispatched: External environment engaged
-- Reflex Listener Probe: GitHub suppression confirmed
-- Resume Overlay Exported: recruiter‑facing capsule complete
-
-What It Is
-- A homepage that evolves through layout iterations (v1–v110+)
-- Validator capsules built in Python, PowerShell, Go, and more
-- Badge tiers, trust scores, and sync metrics rendered in real time
-- A modular registry of 37 entries, each expressing consequence‑aware logic
-
-Why It Exists
-In a world of fragmented signals, Gravity Binary offers a unified trust engine — where architecture isn’t just functional, it’s expressive.
-- Every layout is a proof.
-- Every validator is a signal.
-- Every capsule is a leap.
-
-What’s Inside
-- index.html → Public‑facing homepage with capsule grid and trust overlays
-- Layout markers → Trigger rebuild logic for sync tracking
-- Validator logic → Tier scores, sync history, inertia, flux, and harmonic overlays
-- Recruiter export → Capsule matrix for external proof
-
-Latest Milestone
-- Layout v110 deployed
-- Capsule mutation logic engaged
-- Validator grid shift active
-- Registry delta confirmed
-
-Next Steps
-- Layout v111+: Trust echo dampening, tier rebound index, sync rhythm visualizer
-- Capsule expansion: New validators, badge logic, and symbolic metadata
-- Public registry: External capsule sync and contributor onboarding
-
-Featured Capsules
-- AI Insurance Agent (Python) – Tier 2
-- UCE Engine (Go) – Tier 3
-- Resume Sync Logic (PowerShell) – Tier 2
-- UCE Validator Spine (Go) – Tier 3
-- UCE Echo Dampener (C#) – Tier 2
-- UCE Trust Overlay (Terraform) – Tier 3
-
-Dispatch
-- dispatch-uce.json
-- resume-reflex-overlay.txt
-- index.html (v110)
-
-📂 Project Structure
-Project
-- Gravity Binary Architecture → Master index repo (this one)
-Subprojects (Business-facing)
-- GravityShield
-- GravityAudit
-- GravitAIyWallet
-- GravityValidator
-- WebEditorCapsule
-- ConstructionAppCapsule (placeholder)
-- FreightMarketCapsule (placeholder)
-- ChrisBusinessCapsule International Code Council (placeholder)
-- KalshiCapsule (placeholder)
-- DaaS (placeholder)
-- AI Insurance Agent
-- UCE Universal Context Engine
-
-
-Capsules (Infrastructure parts)
-TrustBeacon · TrustEcho · TrustIndex · TrustPropagation · TrustSeal · CapsuleRegistry · CapsuleBundle · CapsuleHeartbeat · CapsuleIndex · CapsuleTagsIndex · CapsuleReport · ValidatorAudit · ValidatorExpansion · ValidatorMirror · ValidatorOverlay · ValidatorTags · RecruiterCapsule · RecruiterRegistry · RecruiterExport · RecruiterOverlay · ResumeCapsule · BusinessOnboardingCapsule · SOPCapsule · ComplianceCapsule · GravityDomainBinding · GravityDomainCert · GravityDomainPatch · GravityImagePatch
-
-Author
-Todd Wolf · Architect of Reflex Logic · Gravity Binary LLC
+# ?? **Gravity Binary (v0.1 Architecture)**
 
+## **Overview**
+Gravity Binary is a consequence-aware reasoning architecture designed to structure, validate, and execute intent in a deterministic, inspectable, and auditable way. The system treats “intent” as a first-class computational object, enabling controlled execution pipelines that preserve provenance, enforce constraints, and maintain predictable behavior across chained reasoning steps.
+
+This repository contains the **v0.1 Core Architecture**, representing the foundational execution layer of the system. Higher-level layers (Reflex, Validators, Registry, etc.) build on top of this core.
+
+## **Core Principles**
+
+### **1. Intent as a Structured Object**
+Intent is not free-form text.  
+It is a structured frame containing declared purpose, constraints, provenance, execution metadata, and safety boundaries.
+
+### **2. Deterministic Execution**
+Every execution step is validated, constrained, traced, signed, and stored.
+
+### **3. Consequence-Aware Design**
+The system evaluates what the intent is, what it could do, what it should do, and what it must not do before execution.
+
+### **4. Modular, Inspectable Architecture**
+Each component in /core is isolated, testable, and designed to be reasoned about independently.
+
+## **Core Architecture (v0.1)**
+
+### **intent_frame.py**
+Defines the Intent Frame — the atomic reasoning unit.
+
+### **capsule.py**
+Implements Capsules, which wrap executable logic with metadata and lifecycle hooks.
+
+### **validator.py**
+Validates Intent Frames and Capsules for structural correctness and constraint satisfaction.
+
+### **provenance.py**
+Tracks the lineage of every execution step.
+
+### **constraint_engine.py**
+Evaluates and enforces constraints attached to frames or capsules.
+
+### **chaining.py**
+Implements deterministic chaining of frames and capsules.
+
+### **signature.py**
+Defines execution signatures for compatibility checks.
+
+### **storage.py**
+Provides lightweight storage utilities for state passing and caching.
+
+### **execution_engine.py**
+The orchestrator for executing frames and capsules.
+
+### **test_engine_full.py**
+A full-stack test harness validating chaining, execution, provenance, and constraint enforcement.
+
+## **Execution Flow (High-Level)**
+
+Intent Frame ? Validation ? Constraint Evaluation ? Capsule Execution  
+? ? Provenance ? Signature Check ? Storage Hooks ? Chaining Logic ? Output
+
+## **Versioning Philosophy**
+
+### **v0.1 — Core Architecture**
+The first stable, inspectable foundation of the system.
+
+### **v0.2+ — Intelligence Layers**
+Future versions introduce Reflex, Validator, Registry, trust scoring, and higher-order reasoning.
+
+## **Status**
+The core architecture is fully published and validated.

core/README.md

@@ -0,0 +1,44 @@
+# ?? **core/README.md — Gravity Binary Core Architecture (v0.1)**
+
+## **Overview**
+The /core directory contains the foundational execution and reasoning components of the Gravity Binary architecture. These modules define how intent is framed, validated, constrained, executed, chained, and traced. Everything else in the system builds on top of this layer.
+
+## **Module Breakdown**
+
+### **intent_frame.py**
+Defines the Intent Frame, the atomic reasoning unit of Gravity Binary.
+
+### **capsule.py**
+Implements Capsules — wrappers around executable logic with metadata and lifecycle hooks.
+
+### **validator.py**
+Validates Intent Frames and Capsules for structural correctness and constraint satisfaction.
+
+### **constraint_engine.py**
+Evaluates and enforces constraints attached to frames or capsules.
+
+### **provenance.py**
+Tracks the lineage of every execution step.
+
+### **chaining.py**
+Implements deterministic chaining of frames and capsules.
+
+### **signature.py**
+Defines execution signatures for compatibility checks.
+
+### **storage.py**
+Provides lightweight storage utilities for state passing and caching.
+
+### **execution_engine.py**
+The orchestrator for executing frames and capsules.
+
+### **test_engine_full.py**
+A full-stack test harness validating chaining, execution, provenance, and constraint enforcement.
+
+## **Execution Flow (Simplified)**
+
+Intent Frame ? Validation ? Constraint Evaluation ? Capsule Execution  
+? ? Provenance ? Signature Check ? Storage ? Chaining ? Output
+
+## **Purpose of the Core Layer**
+The Core Architecture provides the deterministic, consequence-aware foundation that higher-order reasoning layers build upon.

core/capsule.py

@@ -0,0 +1,38 @@
+from dataclasses import dataclass
+from typing import Any, Dict, List, Callable
+from validator import Validator, ValidatorResult
+from provenance import ProvenanceRecord
+
+
+@dataclass
+class Capsule:
+    name: str
+    transform: Callable[[Dict[str, Any]], Any]
+    validators: List[Validator]
+
+    def run(self, intent_id: str, inputs: Dict[str, Any]) -> ProvenanceRecord:
+        """
+        v0.1 Capsule execution with provenance:
+        1. Run the transform
+        2. Run validators
+        3. Return a ProvenanceRecord
+        """
+        output = self.transform(inputs)
+
+        validation_results: List[Dict[str, Any]] = []
+        for v in self.validators:
+            result: ValidatorResult = v.run(inputs)
+            validation_results.append(
+                {
+                    "name": result.name,
+                    "passed": result.passed,
+                    "details": result.details,
+                }
+            )
+
+        return ProvenanceRecord(
+            intent_id=intent_id,
+            capsule=self.name,
+            output=output,
+            validations=validation_results,
+        )

core/chaining.py

@@ -0,0 +1,14 @@
+from typing import Any, Dict
+
+
+class CapsuleChain:
+    """
+    v0.1 Capsule Chaining:
+    Passes output of one capsule into the next capsule's inputs.
+    """
+
+    @staticmethod
+    def merge_inputs(original: Dict[str, Any], new_output: Dict[str, Any]) -> Dict[str, Any]:
+        merged = original.copy()
+        merged.update(new_output)
+        return merged

core/constraint_engine.py

@@ -0,0 +1,34 @@
+from typing import Any, Dict, List
+
+
+class ConstraintFailure(Exception):
+    pass
+
+
+class Constraint:
+    """
+    v0.1 Constraint:
+    A simple rule that checks the intent inputs before execution.
+    """
+
+    def __init__(self, name: str, rule):
+        self.name = name
+        self.rule = rule  # rule: Callable[[Dict[str, Any]], bool]
+
+    def check(self, inputs: Dict[str, Any]) -> None:
+        if not self.rule(inputs):
+            raise ConstraintFailure(f"Constraint failed: {self.name}")
+
+
+class ConstraintEngine:
+    """
+    v0.1 Constraint Engine:
+    Runs a list of constraints before capsule execution.
+    """
+
+    def __init__(self, constraints: List[Constraint]):
+        self.constraints = constraints
+
+    def validate(self, inputs: Dict[str, Any]) -> None:
+        for constraint in self.constraints:
+            constraint.check(inputs)

core/execution_engine.py

@@ -0,0 +1,49 @@
+from typing import List
+from intent_frame import IntentFrame
+from capsule import Capsule
+from provenance import ProvenanceRecord
+from constraint_engine import ConstraintEngine
+from chaining import CapsuleChain
+from signature import Signature
+from storage import Storage
+
+
+class ExecutionEngine:
+    """
+    v0.2 Execution Engine:
+    - Runs constraints
+    - Executes capsule chain
+    - Generates provenance
+    - Signs provenance
+    - Stores provenance
+    """
+
+    def __init__(self, capsules: List[Capsule], constraints: ConstraintEngine):
+        self.capsules = capsules
+        self.constraints = constraints
+
+    def run(self, intent: IntentFrame) -> List[ProvenanceRecord]:
+        # 1. Validate constraints
+        self.constraints.validate(intent.inputs)
+
+        records: List[ProvenanceRecord] = []
+        current_inputs = intent.inputs
+
+        # 2. Capsule chaining
+        for capsule in self.capsules:
+            record = capsule.run(intent.intent_id, current_inputs)
+
+            # 3. Sign provenance
+            record_dict = record.to_dict()
+            record_dict["signature"] = Signature.sign(record_dict)
+
+            # 4. Store provenance
+            Storage.save(record_dict)
+
+            # 5. Add to results
+            records.append(record)
+
+            # 6. Chain output into next capsule
+            current_inputs = CapsuleChain.merge_inputs(current_inputs, record.output)
+
+        return records

core/intent_frame.py

@@ -0,0 +1,53 @@
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional
+import uuid
+import time
+
+
+def generate_intent_id() -> str:
+    return f"intent-{uuid.uuid4()}"
+
+
+def timestamp() -> str:
+    return time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
+
+
+@dataclass
+class IntentFrame:
+    intent_id: str = field(default_factory=generate_intent_id)
+    goal: str = ""
+    inputs: Dict[str, Any] = field(default_factory=dict)
+    context_window: Dict[str, Any] = field(default_factory=dict)
+    constraints: Dict[str, Any] = field(default_factory=dict)
+    validators: List[str] = field(default_factory=list)
+    success_criteria: Dict[str, Any] = field(default_factory=dict)
+    failure_modes: Dict[str, Any] = field(default_factory=dict)
+    plan: Optional[List[Dict[str, Any]]] = None
+    created_at: str = field(default_factory=timestamp)
+
+    def describe(self) -> Dict[str, Any]:
+        return {
+            "intent_id": self.intent_id,
+            "goal": self.goal,
+            "inputs": self.inputs,
+            "context_window": self.context_window,
+            "constraints": self.constraints,
+            "validators": self.validators,
+            "success_criteria": self.success_criteria,
+            "failure_modes": self.failure_modes,
+            "plan": self.plan,
+            "created_at": self.created_at,
+        }
+
+
+if __name__ == "__main__":
+    frame = IntentFrame(
+        goal="Refactor resume for recruiter visibility",
+        inputs={"resume_text": "..."},
+        context_window={"job_target": "Cloud Engineer"},
+        constraints={"tone": "professional"},
+        validators=["resume-style-check", "keyword-density"],
+        success_criteria={"improved_readability": True},
+        failure_modes={"missing_sections": ["experience", "skills"]},
+    )
+    print(frame.describe())