Extraction & Ingestion Skill

Taxonomy note: This skill provides domain expertise (HOW) for extracting structured information from documents and sources. See [[TAXONOMY.md]] for the skill/workflow distinction.

General-purpose extraction skill that intelligently routes to specialized workflows based on input type. Extracts structured information from various sources and stores it appropriately (public framework vs. private data).

Framework Context

[[AXIOMS.md]] — including [[AXIOMS.md#P52]] (Read-Then-Write Memory)

Search Before Creating

MANDATORY before creating any new extracted content: search PKB for existing knowledge on the same subject.

mcp__pkb__search(query="[topic/person/document subject]")

• If a match exists → augment it rather than creating a duplicate
• If people are mentioned → retrieve existing relationship context to enrich extraction
• If no match → proceed with creation

This prevents duplicate memories and grounds extraction in accumulated knowledge. See [[remember]] skill's "search first" step as the model.

Purpose

Provide a unified entry point for all extraction tasks:

• Training data extraction from feedback documents
• Archive information extraction (emails, correspondence)
• Decision extraction from task queues
• Knowledge extraction from documents
• Review pair extraction for LLM training

Workflow Routing

When invoked, analyze the input and route to the appropriate workflow:

1. Training Data Extraction

Signals:

• Input contains review feedback + source document
• User mentions "training", "extract patterns", "learning", "dataset"
• Documents contain tracked changes, comments, or annotations
• Goal is to build LLM training data

Route to: workflows/training-data.md

Storage:

• Sensitive data (actual review content, source documents): $ACA_DATA/processed/review_training/
• Generalized patterns (depersonalized principles): Framework docs or peer-review skill

2. Archive Information Extraction

Signals:

• Input is email archive, correspondence, receipts
• User mentions "archive", "preserve", "remember"
• Goal is to capture significant events/relationships
• Source is historical documents

Route to: Existing archived/skills/extractor/SKILL.md logic

Storage: Use Skill(skill="remember") for PKB storage

3. Decision Extraction

Signals:

• User mentions "decisions", "pending", "blocking"
• Goal is to surface approval/choice items
• Source is task queue

Route to: Existing aops-core/skills/decision-extract/SKILL.md

Storage: Daily note with decision formatting

4. Document Knowledge Extraction

Signals:

• Single document needs key information extracted
• User mentions "extract", "parse", "ingest"
• Not training data, not archive, not decisions
• Goal is structured information retrieval

Route to: workflows/document-knowledge.md (to be created)

Storage: Depends on content - PKB or framework docs

Workflow: Training Data Extraction

Input Types

Type A: Review with Inline Comments (DOCX with tracked changes)

• Example: Peer review with inline comments and suggestions
• Procedure: procedures/review-inline-comments.md
• Output: Training pairs + generalized principles

Type B: Separate Review + Source Documents

• Example: review.txt + source.pdf + metadata.json
• Workflow: archived/skills/review-training/SKILL.md
• Output: Training pairs matching feedback to source evidence

Type C: Revision History

• Example: Git history, Google Docs revision history, track changes
• Workflow: workflows/revision-history.md (to be created)
• Output: Before/after pairs with change rationales

Extraction Process

See procedures/review-inline-comments.md for detailed procedure.

Quick summary:

1. Convert to workable format (preserve markup)
2. Extract feedback units (text + comment pairs)
3. Categorize feedback (type, scope, action)
4. Identify patterns (group similar feedback)
5. Generalize principles (abstract to transferable form)
6. Separate sensitive/public (raw data vs. patterns)
7. Store appropriately (sensitive → $ACA_DATA, patterns → framework)

Storage Rules

CRITICAL: Training data often contains sensitive information (author names, unpublished work, specific critiques).

Sensitive data → $ACA_DATA/processed/review_training/{collection_name}/:

• extracted_examples.json (full text/feedback pairs)
• training_pairs.jsonl (machine-readable format)
• collection_summary.md (with identifying information)
• Source documents (if retained)

Generalized patterns → Framework (public repo):

• aops-core/skills/hydrator/workflows/peer-review.md (update with principles)
• aops-core/skills/*/references/ (depersonalized examples)
• No names, no specific unpublished content, no identifying details

Quality Standards

High-quality extraction:

• Clear connection between feedback and source
• Sufficient context for learning
• Well-categorized with teaching points
• Patterns are transferable

Generalization quality:

• Principles are specific enough to apply
• Principles are general enough to transfer
• Examples span different contexts
• Limitations are documented

Workflow: Archive Information Extraction

Delegate to archived/skills/extractor/SKILL.md.

Key principle: Most archival documents have NO long-term value. Be highly selective.

Extract: Concrete outcomes, significant relationships, financial records Skip: Newsletters, invitations, administrative routine, mass communications

Storage: Use Skill(skill="remember") with proper tags and canonical identifiers.

Workflow: Decision Extraction

Delegate to aops-core/skills/decision-extract/SKILL.md.

Key principle: Extract tasks requiring approval/choice that are blocking other work.

Storage: Daily note with formatted decision list for batch processing.

Sensitive Data Handling

What is Sensitive?

• Author names and identifying information
• Unpublished work content
• Specific critiques of individuals' work
• Email content and correspondence
• Personal information
• Institutional confidential information

Storage Location: $ACA_DATA/processed/

Directory structure:

$ACA_DATA/processed/
├── review_training/
│   ├── {collection_name}/
│   │   ├── extracted_examples.json
│   │   ├── training_pairs.jsonl
│   │   ├── collection_summary.md
│   │   └── source_documents/
│   └── ...
├── email_archive/
│   └── ...
└── ...

Access: This directory is:

• Outside the public academicOps repo
• In personal data directory ($ACA_DATA = /home/nic/brain)
• Should be backed up separately
• Not committed to git

Depersonalization for Public Framework

When adding examples to public framework docs:

1. Remove all names (use "Author", "Reviewer", or generic placeholders)
2. Remove specific work titles (use generic descriptions)
3. Remove institutional affiliations
4. Generalize to principle, not specific instance
5. Use constructed examples if real ones can't be depersonalized

Integration with Other Skills

When to use /extract vs. specialized skills

Use /extract:

• Unclear what type of extraction is needed
• Multiple types of documents to process
• Want intelligent routing to appropriate workflow

Use specialized skill directly:

• /remember - When you know you want to add to knowledge base
• /decision-extract - When specifically extracting decisions
• /review-training - When processing matched review/source pairs (legacy)

Skill Composition

/extract → analyze input → route to:
  - /remember (for archival preservation)
  - /decision-extract (for pending decisions)
  - training-data workflow (for LLM training data)
  - document-knowledge workflow (for general extraction)

Error Handling

Scenario	Behavior
Unclear input type	Ask user to clarify extraction goal
Cannot convert document format	Try alternative conversion, document failure
Ambiguous feedback	Flag with "quality": "ambiguous", include with caveats
No clear extraction value	Ask user if they want to skip or force extraction
Storage location unclear	Default to $ACA_DATA/processed/, confirm with user

Examples

Example 1: Peer Review Extraction

Input: DOCX file with inline comments from peer review

User: /extract /path/to/review.docx --type peer-review

Agent:

1. Detects inline comments → routes to review-inline-comments workflow
2. Converts DOCX with pandoc --track-changes=all
3. Extracts 18 feedback units
4. Identifies 10 generalisable principles
5. Stores sensitive data in $ACA_DATA/processed/review_training/aoir2026/
6. Updates aops-core/skills/hydrator/workflows/peer-review.md with depersonalized principles

Example 2: Email Archive

Input: Directory of email MSG files

User: /extract emails/2025-Q1/ --type archive

Agent:

1. Detects email archive → routes to extractor skill logic
2. Processes each email, applies judgment criteria
3. Extracts significant events/relationships
4. Uses /remember to store in PKB
5. Skips 90% of emails as noise

Example 3: Auto-Detection

Input: Mixed documents without type specified

User: /extract documents/

Agent:

1. Analyzes each document
2. Detects: 2 peer reviews (tracked changes), 5 emails, 1 grant application
3. Routes peer reviews → training-data workflow
4. Routes emails → archive extraction
5. Asks user about grant application (unclear extraction goal)

Validation Checklist

Before completing extraction:

Completeness:

• All extractable items identified
• All items processed (or skipped with reason)
• Output files created in correct locations

Quality:

• Teaching value is clear (for training data)
• Categorization is accurate
• Context is sufficient

Sensitivity:

• Sensitive data stored in $ACA_DATA/processed/
• Public framework contains only depersonalized content
• No identifying information in public docs

Documentation:

• Extraction process documented
• Decisions and ambiguities noted
• Collection summary created

Future Enhancements

• Semi-automated pattern detection
• Batch processing for multiple documents
• Integration with continuous ingestion pipeline
• Quality metrics and validation tools
• Cross-collection pattern analysis