soljourner 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
- Start Session: Run resource-lister.py to establish baseline
- Review Report: Claude reviews available packages and tools
- Plan Approach: Select methods based on available resources
- Code Generation: Use only verified available packages
- 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
- Run at Session Start: Establish baseline before beginning work
- Update After Installations: Re-run after installing new packages
- Share Reports: Include in issue reports for debugging
- Cache Results: Store for reference throughout session
- 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