CortexStratum

CortexStratum

Persistent memory, cognitive pipeline, simulation, and agent orchestration. 122 MCP tools across memory, engineering simulation, focus management, context compaction, and agent orchestration.

Zero cloud dependencies. Zero GPU. Zero API keys. Pure Python stdlib core.

Usage Scenarios

1. Engineering Simulation

# Beam stress analysis for a 3m steel beam
read_sim_mech_stress(moment=1000, distance_neutral=0.05, I=1.2e-5)
# → {"stress_mpa": 4.17, "formula": "σ = M*y / I"}

# Column buckling check
read_sim_mech_buckle(E=200e9, I=1.2e-5, K=1.0, L=3.0)
# → {"critical_load_kN": 2631.89, "slenderness_ratio": 86.6}

# Pipe flow pressure drop (Darcy-Weisbach)
read_sim_cfd_pipe(rho=1000, v=2.0, D=0.1, mu=1e-3, L=50)
# → {"delta_P_kPa": 8.2, "Reynolds": 200000, "regime": "turbulent"}

# FEA beam element stiffness matrix
read_sim_fea_beam(E=200e9, I=1.2e-5, L=3.0)
# → {"stiffness_matrix": [[4x4 matrix]], "dof": 4}

# Solve Ax = b with LaTeX derivation
read_sim_matrix_solve(A=[[2,1],[1,3]], b=[5,6])
# → {"solution": [1.8, 1.4], "latex": "x = \\begin{bmatrix} 1.8 \\\\ 1.4 \\end{bmatrix}"}

2. Focus & Scope Management

# A convoluted prompt arrives — decompose it
read_focus_decompose(prompt_text="Build an API, also add a dashboard, 
  and can you make a mobile app too? Oh and fix the database migration")
# → {"tasks": [{"id": 1, "category": "backend", "description": "Build API"},
#              {"id": 2, "category": "frontend", "description": "Add dashboard"},
#              {"id": 3, "category": "mobile", "description": "Make mobile app"},
#              {"id": 4, "category": "database", "description": "Fix migration"}],
#    "total_tasks": 4}

# Prioritize them
read_focus_prioritize(tasks=[...])
# → {"ordered_plan": [4, 1, 2, 3], "rationale": "Migration blocks API, API blocks dashboard"}

# Check for scope creep mid-session
read_focus_scope_check(input_text="Actually let's rewrite everything in Rust instead")
# → {"classification": "scope_creep", "nudge": "That's a new project — store in Global Memory?"}

# Store an off-task idea for later
write_focus_store_global(project="rust-rewrite", task="Evaluate Rust for backend")

3. Context Compaction

# Check token velocity (how fast context is growing)
read_compact_token_velocity()
# → {"velocity_5min": 12, "spike_detected": true, "recommendation": "compact now"}

# Condense verbose output into a summary
read_compact_synthesize(content="[300 lines of build logs...]")
# → {"summary": "Build failed: ModuleNotFound in 3 files.\nFixed: npm install react-dom",
#     "compression_ratio": 0.04, "protected_blocks": 2}

# Execute full compaction cycle
write_compact_execute(content="[session content...]")
# → {"status": "compacted", "compression_ratio": 0.12}

4. Agent Orchestration

# Analyze task complexity and generate workstream plan
python scripts/task-orchestrator.py --task "Create FEA module and wire it" --plan
# → Workstreams: mod-1 (parallel create) → wire-1 (serial wiring)

# Execute with DAG coordination
python scripts/dag-coordinator.py --dag data/dag-definitions/master-spec-full-build.json --dry-run
# → 3 levels: 6 parallel module nodes → 1 serial wiring → 1 verification

# Auto-detect module pattern (parallel create → serial wire)
python scripts/task-orchestrator.py --task "Create compact and mutation modules" --orchestrate
# → Phase 1 (parallel): mod-1
# → Phase 2 (serial):   wire-1
# → Post-merge: python scripts/phase-verify-full.py

# Resume an interrupted orchestration
python scripts/task-orchestrator.py --resume <plan-id>
# → Loads saved workstream state and continues

5. Code Analysis & Review

# Analyze code for issues
read_coder_analyze_code(code="def add(a,b): return a+b", language="python")
# → {"complexity": "low", "issues": [], "suggestions": ["Add type hints"]}

# Deep review across multiple dimensions
read_coder_review(code="...", language="python", focus="security")
# → {"vulnerabilities": 2, "severity": "medium", "fixes": ["Use parameterized queries"]}

# Debug an error with full context
read_coder_debug(error="TypeError: unsupported operand", code_context="...", language="python")
# → {"root_cause": "str + int concatenation", "fix": "Cast to str(): str(value)"}

6. Web Browsing & Data Extraction

# Fetch a URL as clean text
read_sensory_fetch(url="https://example.com", method="browser", mode="text")
# → {"content": "Page text content...", "source": "browser"}

# Extract article content
read_sensory_fetch(url="https://example.com/blog", method="article")
# → {"title": "...", "body": "...", "author": "..."}

# Take a screenshot
read_sensory_screenshot(url="https://example.com")
# → {"screenshot_path": "...", "resolution": "1024x1024"}

7. DevOps & Infrastructure

# Debug a failing container
read_devops_container_debug(error_log="container exited with code 1", runtime="podman")
# → {"likely_cause": "Entry point script not found", "fix": "Check CMD in Dockerfile"}

# Generate a docker-compose file
read_devops_compose_generator(services=[{"name": "web", "image": "nginx"}])
# → {"compose": "version: '3'\nservices:\n  web:\n    image: nginx"}

# Troubleshoot network issues
read_devops_network_troubleshoot(symptom="containers can't reach each other")
# → {"likely_cause": "No custom network defined", "fix": "Create network: docker network create app-net"}

8. Pedagogy & Adaptive Learning

# Assess user understanding from their queries
read_pedagogy_assess(queries=["what is a tensor", "explain backpropagation"], topic="deep learning")
# → {"current_depth": 3, "suggested_depth": 4, "level": "advanced"}

# Generate an explanation at the right depth
read_pedagogy_adapt(topic="convolutional neural networks", complexity=2, format="analogy")
# → {"pedagogy_prompt": "Explain CNNs at basic level using analogies. Output: analogy"}

# Store user's preferred depth
write_pedagogy_profile(depth=4, topic="machine learning")
# → {"status": "stored", "current_depth": 4}

9. Memory Consolidation & Cross-Pollination

# Check current memory state
read_memory_status()
# → {"memory_count": 85, "storage": "BM25 + SQLite", "last_consolidation": "2026-07-18"}

# Search memory with mode selection
read_memory_search(query="how to handle async errors", mode="bm25")
# → {"results": [...], "mode": "bm25", "latency_ms": 0.5}

# Cross-pollinate linked memories
write_consolidation_run()
# → {"links_found": 15, "entries_analyzed": 80, "status": "consolidated"}

# View discovered links
read_consolidation_links(limit=5, min_similarity=0.3)
# → {"links": [{"source": "error: module not found", "target": "fix: npm install", "similarity": 0.85}]}

Architecture

Layer 1: Persistent Memory & Storage
  BM25 search · SQLite session store · Structured registries (errors, decisions, goals)

Layer 2: Cognitive Pipeline
  /compact  → Context compaction, token velocity, state condensation
  /mutate   → Scope assessment, redundancy audit, execution
  /focus    → Scope detection, nudges, prompt decomposition, session pipeline
  /plumber  → Socket inspection, handoff tracing, artifact checkpointing

Layer 3: Simulation Engines
  sim_mech  → Beam stress, column buckling, fatigue (Goodman/Miner), fasteners
  sim_fea   → Beam elements, truss, modal analysis, heat conduction
  sim_cfd   → Pipe flow, boundary layer, drag, Bernoulli
  sim_math  → Matrix solve (Ax=b), ODE (RK4), ASCII plot, LaTeX generation

Layer 4: Agent Skills
  sensory   → Web browsing (Playwright), scraping, PDF/OCR, RSS
  coder     → Analyze, review, debug, convert, scaffold
  devops    → Containers, compose, Samba, network
  gamedev   → Design, scaffold, mechanics, monetization
  audio     → WAV analysis, waveform, frequency, music theory
  art       → SVG, themes, palettes, design concepts
  literature→ Text analysis, concepts, study guides

Layer 5: Orchestration
  task-orchestrator.py  → Module-pattern detection, auto-DAG, parallel subagents
  dag-coordinator.py    → Topological sort, conditional branching, fan-in merge
  phase-verify-full.py  → Cross-phase integration tests

Quick Start

One-Click Install (Windows)

Double-click ONE-CLICK.cmd — it downloads Docker, builds the container, and prints your MCP config.

Manual Install

git clone https://github.com/ohmpatel3877/CortexStratum.git
cd CortexStratum
python scripts/tools-mcp-server.py

Memory works immediately with zero dependencies (stdlib only). Optional: pip install sentence-transformers for vector search.

Connect to OpenCode

{
  "mcpServers": {
    "CortexStratum": {
      "command": "python",
      "args": ["scripts/tools-mcp-server.py"]
    }
  }
}

Tool Inventory

122 tools across 26 domains:

Project Structure

CortexStratum/
  scripts/
    tools-mcp-server.py      # MCP server entrypoint (122 tools)
    memory_search.py          # BM25 + SQLite engine
    compact-module.py         # Context compaction
    mutation-module.py        # Algorithmic mutation
    focus-module.py           # Scope & session management
    plumber-module.py         # Execution pipelines
    sim-mechanics-module.py   # 14 mechanics tools
    sim-fea-module.py         # 4 FEA tools
    sim-cfd-module.py         # 4 CFD tools
    sim-math-module.py        # 4 math tools
    pedagogy-module.py        # Teaching adaptation
    consolidation-daemon.py   # TF-IDF cross-pollination
    sensory-module.py         # Web browsing (Playwright)
    coder-module.py           # Code analysis
    audio-module.py           # Audio processing
    art-module.py             # SVG generation
    literature-module.py      # Text analysis
    devops-module.py          # Container/network ops
    game-dev-module.py        # Game development
    utils.py                  # Shared load_json/save_json utilities
    focus-module.py           # Scope & session management
    phase-verify-full.py      # Cross-phase integration tests
    tool-def-validator.py     # Tool definition integrity checker
    task-orchestrator.py      # Parallel subagent orchestration
    dag-coordinator.py        # DAG execution engine
  cad-module/                 # 3D printing (OpenSCAD)
  electrical-module/          # Circuit design
  hermes-plugin/              # Agent MemoryProvider
  future/                     # Spec-first development blueprints
  docs/                       # Guides, audits, architecture reviews
  data/                       # Persistent storage (JSON + SQLite)
  opencode.json               # Project config

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