doc-extraction-hybrid-classification-strategy-wrk-1188-learning
Sub-skill of doc-extraction: Hybrid Classification Strategy (WRK-1188 Learning).
Best use case
doc-extraction-hybrid-classification-strategy-wrk-1188-learning is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of doc-extraction: Hybrid Classification Strategy (WRK-1188 Learning).
Teams using doc-extraction-hybrid-classification-strategy-wrk-1188-learning 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/hybrid-classification-strategy-wrk-1188-learning/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How doc-extraction-hybrid-classification-strategy-wrk-1188-learning Compares
| Feature / Agent | doc-extraction-hybrid-classification-strategy-wrk-1188-learning | 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 doc-extraction: Hybrid Classification Strategy (WRK-1188 Learning).
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
# Hybrid Classification Strategy (WRK-1188 Learning) ## Hybrid Classification Strategy (WRK-1188 Learning) For large homogeneous collections, prefer deterministic classifiers over LLM: | Collection | Strategy | Cost | Accuracy | |-----------|----------|------|----------| | ASTM (25,537 docs) | Designation prefix → discipline | $0 | 86% vs LLM | | API/DNV/ISO (1,062) | LLM (Codex Haiku CLI) | ~$2 | Baseline | | Unknown org (484) | LLM (Codex Haiku CLI) | ~$1 | Baseline | **Rule**: If org has predictable title/designation patterns, write a deterministic classifier first. Validate with 100-doc LLM sample. Accept if >85%. See `data/document-index-pipeline` skill for full pipeline orchestration.
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