orcaflex-batch-manager-basic-batch-configuration
Sub-skill of orcaflex-batch-manager: Basic Batch Configuration (+1).
Best use case
orcaflex-batch-manager-basic-batch-configuration is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of orcaflex-batch-manager: Basic Batch Configuration (+1).
Teams using orcaflex-batch-manager-basic-batch-configuration should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/basic-batch-configuration/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How orcaflex-batch-manager-basic-batch-configuration Compares
| Feature / Agent | orcaflex-batch-manager-basic-batch-configuration | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of orcaflex-batch-manager: Basic Batch Configuration (+1).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Basic Batch Configuration (+1)
## Basic Batch Configuration
```yaml
# configs/batch_config.yml
batch:
# Input files
input:
directory: "models/"
pattern: "*.yml" # or *.dat
recursive: false
# Output settings
output:
directory: "results/"
sim_subdirectory: ".sim"
log_directory: "logs/"
# Processing settings
processing:
mode: "parallel" # parallel, sequential, chunked
max_workers: 20 # Maximum parallel workers
adaptive_scaling: true # Auto-adjust workers
# Chunk settings for large batches
chunk_size: 50 # Files per chunk
pause_between_chunks: 5 # Seconds
# Analysis settings
analysis:
run_statics: true
run_dynamics: true
simulation_duration: 10800 # 3 hours
# Error handling
error_handling:
continue_on_error: true
max_retries: 2
timeout_per_file: 3600 # 1 hour max per file
# Progress tracking
progress:
enabled: true
update_interval: 10 # Seconds
save_checkpoint: true
checkpoint_interval: 100 # Files
```
## Advanced Batch Configuration
```yaml
# configs/batch_advanced.yml
batch:
# Input filtering
input:
directory: "models/operability/"
pattern: "*.yml"
filters:
include_patterns:
- "*_100yr_*"
- "*_10yr_*"
exclude_patterns:
- "*_draft_*"
- "*_test_*"
sort_by: "file_size" # Process largest first
# Resource optimization
resources:
max_workers: 30
min_workers: 4
# CPU management
cpu_threshold: 90 # Reduce workers if >90%
cpu_check_interval: 30 # Seconds
# Memory management
memory_threshold: 80 # Reduce workers if >80%
memory_check_interval: 60 # Seconds
# File size optimization
file_size_scaling: true
small_file_threshold: 1 # MB
large_file_threshold: 10 # MB
workers_for_large: 5 # Fewer workers for large files
# Processing pipeline
pipeline:
stages:
- name: "validation"
enabled: true
action: "validate_model"
- name: "preprocessing"
enabled: true
action: "prepare_environment"
- name: "simulation"
enabled: true
action: "run_simulation"
- name: "postprocessing"
enabled: true
action: "extract_results"
# Notifications
notifications:
on_start: true
on_complete: true
on_error: true
email: null # Optional email alerts
# Performance tracking
metrics:
track_per_file: true
track_memory: true
track_cpu: true
export_metrics: true
metrics_file: "batch_metrics.json"
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