orcaflex-batch-manager-batch-results-json
Sub-skill of orcaflex-batch-manager: Batch Results JSON (+1).
Best use case
orcaflex-batch-manager-batch-results-json is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of orcaflex-batch-manager: Batch Results JSON (+1).
Teams using orcaflex-batch-manager-batch-results-json 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/batch-results-json/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How orcaflex-batch-manager-batch-results-json Compares
| Feature / Agent | orcaflex-batch-manager-batch-results-json | 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: Batch Results JSON (+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
# Batch Results JSON (+1)
## Batch Results JSON
```json
{
"batch_id": "operability_20260117_143022",
"start_time": "2026-01-17T14:30:22",
"end_time": "2026-01-17T18:45:33",
"total_files": 500,
"successful": 495,
"failed": 5,
"success_rate": 99.0,
"total_time_seconds": 15311,
*See sub-skills for full details.*
## Progress Log
```
2026-01-17 14:30:22 - Batch started: 500 files
2026-01-17 14:30:22 - Workers: 20 (adaptive)
2026-01-17 14:31:45 - Progress: 50/500 (10.0%) - Success: 50
2026-01-17 14:33:12 - Progress: 100/500 (20.0%) - Success: 100
2026-01-17 14:33:12 - Checkpoint saved
2026-01-17 14:35:40 - Workers adjusted: 20 -> 15 (memory 82%)
...
2026-01-17 18:45:33 - Batch complete: 495 successful, 5 failed
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