parallel-file-processor-mode-selection
Sub-skill of parallel-file-processor: Mode Selection (+2).
Best use case
parallel-file-processor-mode-selection is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of parallel-file-processor: Mode Selection (+2).
Teams using parallel-file-processor-mode-selection 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/mode-selection/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How parallel-file-processor-mode-selection Compares
| Feature / Agent | parallel-file-processor-mode-selection | 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 parallel-file-processor: Mode Selection (+2).
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
# Mode Selection (+2) ## Mode Selection | Workload Type | Recommended Mode | Reason | |---------------|------------------|--------| | File I/O | `THREAD_POOL` | IO-bound, threads avoid GIL issues | | Data parsing | `THREAD_POOL` | Pandas releases GIL during IO | | CPU computation | `PROCESS_POOL` | Bypasses GIL for true parallelism | | Network requests | `ASYNC` | Best for many concurrent connections | | Simple operations | `SEQUENTIAL` | Overhead may exceed benefit | ## Worker Count ```python import os # IO-bound (reading files, network) io_workers = os.cpu_count() * 2 # CPU-bound (heavy computation) cpu_workers = os.cpu_count() # Memory-constrained (large files) memory_workers = max(2, os.cpu_count() // 2) ``` ## Batch Size - **Small files (<1MB):** Large batches (500-1000) - **Medium files (1-100MB):** Medium batches (50-100) - **Large files (>100MB):** Small batches (10-20) or one at a time
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