doc-extraction

Classify and extract structured content from engineering documents using a 3-layer taxonomy: generic content types, engineering patterns, and domain sub-skills. Use when ingesting standards, reports, or technical manuals into structured data for downstream analysis. type: reference

5 stars

byvamseeachanta

View on GitHub Installation ↓

Best use case

doc-extraction is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Teams using doc-extraction 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/doc-extraction/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/engineering/doc-extraction/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/doc-extraction/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How doc-extraction Compares

Feature / Agent	doc-extraction	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?

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

# Doc Extraction

## When to Use

- Ingesting a new standard or code (DNV-RP, API RP, ISO, ASME)
- Extracting constants, equations, or tables from technical reports
- Building structured datasets from engineering manuals
- Populating knowledge bases from document collections
- Pre-processing documents before analysis workflow

## Related Skills

- [document-index-pipeline](../../data/document-index-pipeline/SKILL.md) — 7-phase A→G pipeline
- [doc-intelligence-promotion](../../data/doc-intelligence-promotion/SKILL.md) — Deep extraction post-processing
- [cathodic-protection](../marine-offshore/cathodic-protection/SKILL.md) — CP system design
- [viv-analysis](../marine-offshore/viv-analysis/SKILL.md) — VIV assessment for risers
- [fitness-for-service](../asset-integrity/fitness-for-service/SKILL.md) — FFS assessment
- [structural-analysis](../marine-offshore/structural-analysis/SKILL.md) — Structural checks

## References

- DNV-RP-B401: Cathodic Protection Design
- DNV-RP-C205: Environmental Conditions and Environmental Loads
- API 579-1/ASME FFS-1: Fitness-for-Service
- API RP 16Q: Design, Selection, Operation, and Maintenance of Marine Drilling Riser Systems

## Sub-Skills

- [Yield Reality (WRK-1246 Corpus Assessment)](yield-reality-wrk-1246-corpus-assessment/SKILL.md)
- [Architecture](architecture/SKILL.md)
- [1. `constants` (0% yield — not yet implemented) (+7)](1-constants-0-yield-not-yet-implemented/SKILL.md)
- [Unit Detection and Normalization (+4)](unit-detection-and-normalization/SKILL.md)
- [Extraction Workflow](extraction-workflow/SKILL.md)
- [Output Schema](output-schema/SKILL.md)
- [Domain Sub-Skills](domain-sub-skills/SKILL.md)
- [Hybrid Classification Strategy (WRK-1188 Learning)](hybrid-classification-strategy-wrk-1188-learning/SKILL.md)

Related Skills

llm-wiki-source-extraction-coverage

from vamseeachanta/workspace-hub

Doc-type-aware extraction contract for llm-wiki source ingestion with measurable coverage and source-anchored traceability. Use when (1) ingesting a PDF, DOCX, XLSX, PPTX, HTML, or scanned-image source into a wiki `sources/` page, (2) computing the pre-extraction estimate (what fraction of the source we expect to recover) and post-extraction yield (what fraction we actually recovered), (3) anchoring wiki claims back to specific page / paragraph / cell / slide positions in the source so a reviewer can re-verify or revise against the actual document, (4) deciding whether OCR fallback or manual transcription is needed. Codifies workspace-hub's existing OCR fallback chain and python-docx / openpyxl / trafilatura patterns into a format-specific routing table. Companion to research/llm-wiki-page-shape-contract (Rule 7 input-layer pages) and research/llm-wiki — this skill is the defense against silent extraction failure.

portable-baseline-pattern-extraction

from vamseeachanta/workspace-hub

Extract and separate portable baseline config from machine-specific overrides in multi-environment projects

doc-extraction-naval-architecture

from vamseeachanta/workspace-hub

Layer 3 domain sub-skill for extracting naval architecture data from SNAME PNA, IMO stability codes, IACS structural rules, and classification society guidelines. Provides detection heuristics for stability constants, resistance equations, hull form coefficients, hydrostatic curves, IMO stability criteria, and structural scantling tables. type: reference

doc-extraction-drilling-riser

from vamseeachanta/workspace-hub

Layer 3 domain sub-skill for extracting drilling riser data from API RP 16Q, DNV-RP-C205, and riser analysis reports. Provides detection heuristics for VIV parameters, kill/choke line specs, and BOP stack configurations. type: reference

gmail-email-to-repo-extraction

from vamseeachanta/workspace-hub

Extract structured data from Gmail inbox emails, enrich with domain-specific classification, legal-scan against deny list, commit to appropriate repo, then optionally delete originals.

gmail-data-extraction

from vamseeachanta/workspace-hub

Extract structured data from Gmail emails using REST API (no pip dependencies). Covers inbox scanning, subject line regex extraction, email text parsing, thread-aware drafting, and legal-scan-before-commit workflow.