document-rag-pipeline-performance-metrics-real-world
Sub-skill of document-rag-pipeline: Performance Metrics (Real-World).
Best use case
document-rag-pipeline-performance-metrics-real-world is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of document-rag-pipeline: Performance Metrics (Real-World).
Teams using document-rag-pipeline-performance-metrics-real-world 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/performance-metrics-real-world/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How document-rag-pipeline-performance-metrics-real-world Compares
| Feature / Agent | document-rag-pipeline-performance-metrics-real-world | 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 document-rag-pipeline: Performance Metrics (Real-World).
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
# Performance Metrics (Real-World) ## Performance Metrics (Real-World) From O&G Standards processing (957 documents): | Metric | Value | |--------|-------| | Total documents | 957 | | Text extraction | 811 PDFs | | OCR processed | 96 PDFs | | DRM protected | 50 PDFs | | Total chunks | 1,043,616 | | Embedding time | ~4 hours (CPU) | | Search latency | <2 seconds |
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