doc-extraction-yield-reality-wrk-1246-corpus-assessment

Sub-skill of doc-extraction: Yield Reality (WRK-1246 Corpus Assessment).

5 stars

Best use case

doc-extraction-yield-reality-wrk-1246-corpus-assessment is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Sub-skill of doc-extraction: Yield Reality (WRK-1246 Corpus Assessment).

Teams using doc-extraction-yield-reality-wrk-1246-corpus-assessment 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

$curl -o ~/.claude/skills/yield-reality-wrk-1246-corpus-assessment/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/_archive/engineering/doc-extraction/yield-reality-wrk-1246-corpus-assessment/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/yield-reality-wrk-1246-corpus-assessment/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How doc-extraction-yield-reality-wrk-1246-corpus-assessment Compares

Feature / Agentdoc-extraction-yield-reality-wrk-1246-corpus-assessmentStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Sub-skill of doc-extraction: Yield Reality (WRK-1246 Corpus Assessment).

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

# Yield Reality (WRK-1246 Corpus Assessment)

## Yield Reality (WRK-1246 Corpus Assessment)


WRK-1246 assessed deep extraction yield across 420K+ text-extractable documents.
Only two content types have proven extraction yield from the current parsers:

| Content type | Yield | Status |
|-------------|-------|--------|
| tables | 69-93% | **Production-ready** — primary extraction target |
| figure_refs | 1-52% | **Partial** — metadata only, varies by stratum |
| equations | 0% | Not yet implemented — parsers do not reliably detect |
| constants | 0% | Not yet implemented — parsers do not reliably detect |
| procedures | 0% | Not yet implemented — parsers do not reliably detect |
| worked_examples | 0% | Not yet implemented — parsers do not reliably detect |

These content types are NOT currently extractable from the corpus — they exist in the
manifest schema (documented below) but the parsers do not reliably detect them. The
schema is retained for future parser development.

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