Resource Availability Helper

Purpose

Help Claude quickly determine what resources, tools, packages, and databases are available in the current environment. This skill eliminates guesswork and enables Claude to:

• Use only installed packages in code suggestions
• Recommend appropriate tools for engineering tasks
• Identify missing dependencies before starting work
• Verify database connectivity for data-driven workflows
• Discover available simulation tools (OpenFOAM, ANSYS, etc.)

This diagnostic tool should be run at the beginning of engineering sessions to establish environmental context.

What It Checks

Python Packages

• Core Scientific Computing: numpy, scipy, pandas, matplotlib
• Engineering & Physics: fluids, thermo, CoolProp, pint, sympy
• Optimization: scipy.optimize, pyomo, gekko, cvxpy
• CFD & Simulation: OpenFOAM bindings, PyFluent, pyMesh
• Data Analysis: sklearn, statsmodels, seaborn
• Network Analysis: networkx
• Database Connectors: psycopg2, pymongo, redis, sqlalchemy
• All Installed Packages: Complete pip list with versions

Database Connections

• PostgreSQL: Connection test and version check
• MySQL/MariaDB: Availability and connectivity
• SQLite: Built-in database availability
• MongoDB: Server status and connection
• Redis: In-memory database availability
• Database connection strings: Environment variable detection

Software Installations

• OpenFOAM: Version detection and installation path
• ANSYS: Workbench, Fluent, CFX availability
• COMSOL Multiphysics: Installation check
• MATLAB: Version and toolbox detection
• SolidWorks: CAD software availability
• ParaView: Visualization tool detection
• GiD: Pre/post-processor availability
• Gmsh: Mesh generator detection

Environment Variables

• PATH: Executable search paths
• LD_LIBRARY_PATH: Dynamic library paths
• PYTHONPATH: Python module search paths
• Database URLs: Connection string variables
• License Servers: ANSYS_LICENSE_FILE, LM_LICENSE_FILE
• Tool-Specific: OPENFOAM_DIR, MATLAB_ROOT, etc.

System Information

• Operating System: Linux distribution, version
• Python Version: Interpreter version and location
• Architecture: 64-bit vs 32-bit
• Available Disk Space: Critical for large simulations
• Memory: Total and available RAM
• CPU Info: Cores and model for parallel processing

File Paths

• Project Directories: Common engineering workspace locations
• Data Directories: Standard data storage paths
• Temporary Storage: Scratch space for simulations
• License Files: Software license locations

Resource Reporting Format

The tool generates a structured report with:

Summary Section

=== ENVIRONMENT RESOURCE REPORT ===
Timestamp: 2025-11-07 21:30:45
Python: 3.11.14 (/usr/bin/python3)
OS: Ubuntu 22.04 LTS (Linux 5.15.0)
Architecture: x86_64

Package Availability

[✓] numpy 1.24.3
[✓] scipy 1.10.1
[✓] pandas 2.0.2
[✗] CoolProp (not installed)
[✓] matplotlib 3.7.1

Database Status

PostgreSQL: ✓ Connected (version 14.8)
Redis: ✓ Running (version 7.0.11)
MySQL: ✗ Not installed
MongoDB: ✗ Not running

Software Tools

OpenFOAM: ✓ v10 (/opt/openfoam10)
ANSYS: ✗ Not found
MATLAB: ✗ Not found
ParaView: ✓ v5.11.0 (/usr/bin/paraview)

Recommendations

MISSING PACKAGES FOR FULL ENGINEERING WORKFLOW:
- pip install CoolProp (thermodynamic properties)
- pip install fluids (fluid mechanics calculations)
- pip install thermo (chemical engineering thermodynamics)

OPTIMIZATION:
- Consider installing pyomo for optimization problems

Usage

Quick Check

python3 resource-lister.py

Detailed Report

python3 resource-lister.py --detailed

Check Specific Categories

# Check only Python packages
python3 resource-lister.py --packages-only

# Check only databases
python3 resource-lister.py --databases-only

# Check only software tools
python3 resource-lister.py --software-only

# Check specific package
python3 resource-lister.py --check numpy scipy pandas

JSON Output

# Machine-readable format for automation
python3 resource-lister.py --json > resources.json

Export Report

# Save detailed report to file
python3 resource-lister.py --detailed --output report.txt

Example Output

=== ENGINEERING ENVIRONMENT RESOURCE REPORT ===
Generated: 2025-11-07 21:30:45
System: Linux Ubuntu 22.04 LTS (x86_64)
Python: 3.11.14 (/usr/bin/python3)
Working Directory: /home/user/projects

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
PYTHON PACKAGES (38 installed)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Core Scientific Computing:
  [✓] numpy           1.24.3
  [✓] scipy           1.10.1
  [✓] matplotlib      3.7.1
  [✗] pandas          Not installed

Engineering Libraries:
  [✗] fluids          Not installed
  [✗] thermo          Not installed
  [✗] CoolProp        Not installed
  [✗] pint            Not installed
  [✓] sympy           1.12

Optimization:
  [✓] scipy.optimize  (included in scipy)
  [✗] pyomo           Not installed
  [✗] gekko           Not installed

Network Analysis:
  [✗] networkx        Not installed

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
DATABASES
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

[✓] PostgreSQL  14.8    /usr/bin/psql
    Connection: SUCCESS (test database)

[✓] Redis       7.0.11  /usr/bin/redis-server
    Status: Running on port 6379

[✗] MySQL       Not installed
[✗] MongoDB     Not installed
[✓] SQLite3     3.37.2  (built-in)

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
SOFTWARE TOOLS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Simulation & CFD:
  [✗] OpenFOAM        Not found
  [✗] ANSYS Fluent    Not found
  [✗] COMSOL          Not found

CAD & Visualization:
  [✓] ParaView        5.11.0  /usr/bin/paraview
  [✗] SolidWorks      Not found (Windows only)
  [✓] Gmsh            4.10.5  /usr/bin/gmsh

Computing:
  [✗] MATLAB          Not found

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
SYSTEM RESOURCES
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

CPU: 8 cores (Intel Xeon E5-2680 v4)
RAM: 32.0 GB total, 28.4 GB available
Disk: 450 GB available in /home
Tmp Space: 50 GB available in /tmp

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
RECOMMENDATIONS
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Essential Engineering Packages:
  pip install pandas fluids thermo pint

Recommended for Fluid Mechanics:
  pip install CoolProp networkx

For Optimization Workflows:
  pip install pyomo

For Enhanced Data Analysis:
  pip install seaborn scikit-learn

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Integration with Claude Workflows

Typical Workflow

1. Start Session: Run resource-lister.py to establish baseline
2. Review Report: Claude reviews available packages and tools
3. Plan Approach: Select methods based on available resources
4. Code Generation: Use only verified available packages
5. Error Handling: If missing dependencies detected, suggest installation

Use Cases

Case 1: Pump Design Task

User: "Design a centrifugal pump for water transport"

Claude runs resource-lister.py and sees:
- numpy, scipy, matplotlib: Available
- fluids, thermo: Not available
- CoolProp: Not available

Claude Response:
"I can design the pump using numpy and scipy for calculations.
However, I recommend installing 'fluids' package for more
accurate hydraulic calculations:
  pip install fluids

For now, I'll use scipy for fluid properties with manual
property definitions."

Case 2: CFD Simulation

User: "Set up an OpenFOAM simulation for pipe flow"

Claude runs resource-lister.py and sees:
- OpenFOAM: Not installed
- ParaView: Available

Claude Response:
"OpenFOAM is not currently installed. I can:
1. Provide installation instructions for OpenFOAM
2. Create case files that will work once installed
3. Use ParaView for post-processing once simulation runs

Would you like me to proceed with case setup or help
with OpenFOAM installation?"

Case 3: Database-Backed Analysis

User: "Analyze pump performance data from database"

Claude runs resource-lister.py and sees:
- PostgreSQL: Connected and running
- pandas: Not installed
- numpy, scipy: Available

Claude Response:
"I can query your PostgreSQL database and perform analysis.
For better data handling, I recommend:
  pip install pandas

For now, I'll use numpy arrays to process the query results."

Files Included

• SKILL.md: This documentation
• resource-lister.py: Main diagnostic script

Notes

• The tool requires no external dependencies (uses only Python standard library)
• Database connection tests are non-destructive and read-only
• Software detection is path-based and environment variable-based
• Reports can be cached for session duration to avoid repeated checks
• Safe to run repeatedly without side effects

Best Practices

1. Run at Session Start: Establish baseline before beginning work
2. Update After Installations: Re-run after installing new packages
3. Share Reports: Include in issue reports for debugging
4. Cache Results: Store for reference throughout session
5. Version Tracking: Monitor version changes between sessions

Extensibility

Add custom checks by modifying resource-lister.py:

• Domain-specific packages (aerospace, chemical, structural)
• Custom software installations
• Cloud service availability
• License server status
• Network-mounted resources