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Skill

simulation_skill

run and monitor simulations

Published by NVIDIA Updated Jul 14
Covers Monitoring Automation NVIDIA Simulation

Description

Run, monitor, and control simulations. Use when executing simulations, checking progress, or stopping running simulations. Supports foreground and background execution, progress monitoring, and process management.

SKILL.md

Simulation Skill

This skill provides tools for executing and managing simulations.

Overview

The Simulation skill enables agents to:

  • Run simulations from simulator input files
  • Run and heal run + auto-fix on failure, it will launch both run and monitor in one go from user input files
  • Monitor simulation progress and status
  • Stop running simulations when needed
  • Manage simulation processes and output directories

Tools

run_and_heal

Runs a simulation and, on failure, automatically parses PRT errors, optionally consults RAG (simulator_manual, simulator_examples), and produces a fixed DATA file. Implemented as a chain: run_simulation in parallel with PRT wait + error parsing; if errors are found, extract keyword, call RAG tools if config is available, then create a fixed DATA file (e.g. *_FIXED.DATA).

Implementation: simulator_agent/tools/self_heal_chain.py

Usage (script):

python -m simulator_agent.tools.self_heal_chain \
  --prt-loc-dir /path/to/output/ \
  --data-file /path/to/CASE.DATA \
  [--config /path/to/config.yaml] \
  [--output-file /path/to/output/]

Parameters: data_file, output_dir (optional), num_mpi_processes (default: 1, for MPI parallel runs with np>1).

When to use:

  • When the user wants to run a case and automatically attempt to fix and re-run on simulation failure (run-and-heal flow).
  • Alternative to manual run_simulation → inspect failure → patch_simulation_input_keyword/modify_simulation_input_file → run_simulation.

run_simulation

Runs a simulation from a DATA file. Supports both foreground (wait for completion) and background (return immediately) execution.

Usage:

run_simulation(
    data_file: str,
    output_dir: Optional[str] = None,
    num_threads: int = 1,
    num_mpi_processes: int = 1,
    additional_args: Optional[str] = None,
    background: bool = True
) -> str

Parameters:

  • data_file: Path to the simulator input (DATA) file to run
  • output_dir: Optional output directory (default: same directory as DATA file)
  • num_threads: Number of threads for parallel simulation (default: 1)
  • num_mpi_processes: Number of MPI processes (np). When > 1, runs via mpirun -np N flow ... (default: 1). Set OPM_MPI_LAUNCHER=mpiexec to use mpiexec instead.
  • additional_args: Optional additional command-line arguments
  • background: If True, start and return immediately. If False, wait for completion and return full report including return code, stdout, stderr, and parsed PRT errors on failure.

Example:

run_simulation(
    data_file="SPE1CASE1_AGENT_GENERATED.DATA",
    output_dir="/path/to/output",
    num_threads=4,
    background=False
)

Returns:

  • Background mode: Simulation start confirmation with PID, output directory, and log paths
  • Foreground mode: Full completion report with return code, stdout, stderr, and PRT errors (if failed)

When to use:

  • Direct run (TOOL_DECISION_TREE.md Section 2.3) when user provides DATA file path + run intent
  • After scenario modifications (Section 2.4) to execute the modified case
  • HITL confirm run flow (Section 2.1) after user confirms
  • After auto-fix (Section 2.2) to re-run the fixed case

monitor_simulation

Monitors the progress of a running simulation by reading the PRT file.

Usage:

monitor_simulation(
    output_dir: str,
    tail_lines: int = 80,
    use_llm_summary: bool = False,
    llm_model: Optional[str] = None
) -> str

Parameters:

  • output_dir: Directory containing simulation output files (must contain .PRT file)
  • tail_lines: Number of lines to read from end of PRT file (default: 80)
  • use_llm_summary: If True, use LLM to summarize the PRT tail (default: False)
  • llm_model: Optional LLM model override for summary

Example:

monitor_simulation(
    output_dir="/path/to/output",
    tail_lines=100,
    use_llm_summary=True
)

Returns: Simulation status (running/completed/failed) and PRT tail content, optionally with LLM summary.

When to use:

  • When user asks to check simulation progress (Section 3.5, LLM choice)
  • For background runs to track completion
  • When debugging simulation issues

stop_simulation

Stops a running simulation by process ID.

Usage:

stop_simulation(
    pid: int,
    output_dir: Optional[str] = None,
    force: bool = False
) -> str

Parameters:

  • pid: Process ID of the simulation to stop
  • output_dir: Optional output directory with run metadata
  • force: If True, use SIGKILL (force kill). If False, use SIGTERM (graceful shutdown).

Example:

stop_simulation(
    pid=12345,
    output_dir="/path/to/output",
    force=False
)

Returns: Confirmation message with PID, signal used, and metadata update status.

When to use:

  • When user requests to stop a running simulation (Section 3.5, LLM choice)
  • For process management and cleanup

Workflow Integration

This skill integrates with the Simulator Agent's decision tree (TOOL_DECISION_TREE.md):

  1. Direct Run (Section 2.3):
    run_and_heal (optionally after HITL confirm)
    
  2. Scenario Test Chain (Section 2.4):
    parse_simulation_input_file → simulator_manual → simulator_examples → modify_simulation_input_file → run_and_heal
    
  3. HITL Confirm Run (Section 2.1):
    User confirms → run_and_heal
    
  4. After run_and_heal (Section 3.3):
    run_and_heal → (auto-fix if failed) → summarize
    
  5. Monitoring (Section 3.5):
    monitor_simulation (via LLM choice)
    stop_simulation (via LLM choice)
    

Implementation Details

Tools are implemented as LangChain tools with Pydantic input schemas. The skill uses:

  • Subprocess management: For running Flow executables
  • Process tracking: PID and metadata stored in run.json
  • Output parsing: PRT file parsing for progress and errors
  • Error handling: Comprehensive error messages and failure reporting

Error Handling

All tools return descriptive error messages if:

  • DATA files are not found
  • Flow executable is not available
  • Output directories cannot be created
  • Processes cannot be stopped
  • PRT files cannot be read

Environment Variables

  • OPM_PROJECT_ROOT: Used to resolve paths in Docker environments
  • OPM_FLOW_EXTRA_ARGS: Additional arguments passed to Flow (e.g., --CheckSatfuncConsistency=0)

References

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