Args:
    command: Shell command to execute (e.g. "python train.py --epochs 100")
    name: Job name for identification in SLURM queue
    nodes: Number of compute nodes to allocate
    gpus_per_node: Number of GPUs per node (0 for CPU-only)
    ntasks_per_node: Number of tasks per node
    cpus_per_task: Number of CPUs per task
    memory_per_node: Memory per node (e.g. "32GB", "64G")
    time_limit: Wall time limit (e.g. "4:00:00", "1-00:00:00")
    partition: SLURM partition name (e.g. "gpu", "cpu")
    nodelist: Specific nodes to use (e.g. "node001,node002")
    conda: Conda environment name to activate before running
    venv: Path to Python virtual environment to activate
    env_vars: Additional environment variables as key-value pairs
    log_dir: Directory for stdout/stderr log files
    work_dir: Working directory for the job (defaults to cwd)
    use_ssh: If true, submit via SSH to remote SLURM cluster

Args:
    use_ssh: If true, query jobs via SSH on remote cluster

Args:
    job_id: SLURM job ID to check
    use_ssh: If true, query via SSH on remote cluster

Args:
    job_id: SLURM job ID to cancel
    use_ssh: If true, cancel via SSH on remote cluster

Args:
    job_id: SLURM job ID
    job_name: Optional job name to help locate log files
    use_ssh: If true, fetch logs via SSH from remote cluster

Args:
    partition: Specific partition to check (None for all partitions)
    use_ssh: If true, query resources via SSH on remote cluster

Can sync using a configured mount point (profile_name + mount_name),
or using explicit paths (local_path + remote_path).

Args:
    profile_name: SSH profile name (uses current profile if not specified)
    mount_name: Mount point name from the SSH profile to sync
    local_path: Local directory path (alternative to mount_name)
    remote_path: Remote directory path (alternative to mount_name)
    dry_run: If true, show what would be transferred without actually syncing

Generates a YAML workflow definition that can be executed with run_workflow.
Each job in the workflow can depend on other jobs, forming a DAG.

Args:
    name: Workflow name for identification
    jobs: List of job definitions. Each job dict should contain:
        - name (required): Job identifier
        - command (required for regular jobs): Command as string or list of strings
        - script_path (required for shell jobs): Path to shell script
        - depends_on: List of job names this job depends on (e.g. ["preprocess"])
          Supports dependency types: "afterok:job_a", "after:job_a", "afterany:job_a"
        - retry: Number of retry attempts on failure (default 0)
        - retry_delay: Seconds between retries (default 60)
        - resources: Dict with nodes, gpus_per_node, ntasks_per_node,
          cpus_per_task, memory_per_node, time_limit, partition, nodelist
        - environment: Dict with conda, venv, env_vars, container
        - log_dir: Log directory path
        - work_dir: Working directory path
    output_path: File path to write the YAML workflow (e.g. "workflow.yaml")
    args: Optional template variables for Jinja2 templating in job definitions
    default_project: Default SSH project/mount name for file syncing

Checks for valid YAML syntax, correct job structure, dependency resolution,
and circular dependency detection.

Args:
    yaml_path: Path to the YAML workflow file to validate

Jobs are executed in dependency order - independent jobs run in parallel,
dependent jobs wait for their prerequisites to complete.

Args:
    yaml_path: Path to the YAML workflow file
    from_job: Start execution from this job (skip earlier jobs)
    to_job: Stop execution at this job (skip later jobs)
    single_job: Execute only this specific job, ignoring dependencies
    dry_run: If true, show what would be executed without actually running
    args: Optional mapping merged over the YAML ``args`` section before
        Jinja rendering. ``python:`` prefix values are rejected.
    sweep: Optional sweep spec: ``{"matrix": {...}, "fail_fast": bool,
        "max_parallel": int}``. When present, the request goes through
        :class:`SweepOrchestrator` and the response contains
        ``sweep_run_id``.
    mount: Optional mount name from the active SSH profile. When
        provided, the run is routed through the configured cluster
        adapter with mount-aware path translation for ``work_dir`` /
        ``log_dir``. When omitted (default), the run stays on the
        local SLURM client — same behaviour as pre-5a.

Scans the directory for YAML files that contain a valid srunx workflow
structure (must have 'name' and 'jobs' keys).

Args:
    directory: Directory to search for workflow files (default: current directory)

Args:
    yaml_path: Path to the YAML workflow file