FLMA Documentation

In Development

FLMA documentation is being developed alongside the system. Documentation will include:

  • FLMA architecture and design
  • ServiceNow workflow configuration for Army maintenance
  • GCSS-A integration guides
  • Army maintenance digital twin setup
  • Field-level maintenance workflow documentation

Coming Soon: FLMA documentation will be available as the system development progresses.

ASPM Quick Start

Production Ready

Installation

# Install dependencies
pip install -r requirements.txt

Start Dashboard

Run the Streamlit dashboard locally:

# Windows
start_dashboard.bat

# Or directly
streamlit run aspm_streamlit/app.py

Access at: http://localhost:8501

Start API Server

Run the FastAPI server:

# Windows
start_api_server.bat

# Or directly
python aspm\server_fastapi.py

Access API docs at: http://localhost:8000/docs

ASPM API Endpoints

PMO Value Ring Assessment

POST /api/pmo/value_ring/assess

Assess PMO service catalog and calculate value scores across PMI governance domains.

ADKAR Change Readiness

POST /api/ocm/adkar/intake

Assess organizational change readiness using ADKAR methodology.

SPM Pattern Detection

POST /api/spm/patterns/detect

Detect SPM/PPM administration patterns and symptoms.

OKR Composition

POST /api/okr/compose

Generate OKRs from strategic goals.

KPI Composition

POST /api/kpi/compose

Generate KPIs and signals for performance measurement.

View Interactive API Docs

ASPM Usage Examples

Basic Agent Usage

from aspm.agents import SPMEnvAgent

# Initialize an agent
agent = SPMEnvAgent()

# Use the agent
result = agent.process_request("Help with SPM environment assessment")

Microsoft Copilot Integration

# Generate Copilot agent configuration
python scripts/create_copilot_agent_config.py \
    --api-url https://your-api.com \
    --output copilot_agent_config.json

Agent Training & Scenario Testing

# Extract scenarios from documents
python scripts/scenario_test_runner.py --extract-scenarios

# Run all scenarios against agents
python scripts/scenario_test_runner.py --run-all

# Set up automated monitoring
python scripts/agent_monitoring_scheduler.py --schedule daily

Using AIE (Agent Improvement Engine)

from aspm.aie import evaluate_agent_behavior, generate_improvement_plan

# Evaluate an agent run
evaluation = evaluate_agent_behavior(
    agent_name="SPMEnvAgent",
    agent_metadata={"domain": "spm"},
    transcript="Agent output here..."
)

# Generate improvement plan
plan = generate_improvement_plan(evaluation)

ASPM Documentation Resources

GitHub Repository

Access the complete source code, issues, and contributions: