Archive Information Extraction Skill

Assess archival documents (emails, correspondence, receipts) to identify information worth preserving in the knowledge base. This skill provides judgment criteria only - actual storage is handled by Skill(skill="remember").

Purpose

Filter noise from archival documents and identify genuinely significant information that should be preserved. Most archival documents have NO long-term value - be highly selective.

Assessment Criteria

Decision Framework

For each document, use LLM judgment to answer:

Is this important enough to mention at a monthly research meeting?
Would I want to remember this in 5 years?
Is this a concrete outcome, or just noise?
Did Nic take significant action, or is this passive/routine?

Trust your judgment - you understand context better than regex patterns.

What to Extract

1. Projects & Publications

Papers: Actual submissions, acceptances, revisions, publications (not CFPs)
Grants: Applications submitted, outcomes received, progress reports
Books/Chapters: Contracts signed, submissions, publications
Research Projects: Project initiation, significant milestones, new collaborations
Awards: Significant awards or recognitions
Milestones: Major career milestones, important advocacy/policy work
Media: Media appearances or public engagement

Think: Is this a concrete action or outcome? Or just noise?

2. Professional Activities

Events Organized: Conferences, workshops, panels Nic ran/chaired/coordinated
Speaking: Confirmed talks, keynotes where Nic actually spoke
Service: OSB decisions, editorial board work, advisory roles
Reviews: Actual reviews submitted (not review requests)

Think: Did Nic DO something significant? Or just receive an invitation?

3. Applications & Career

Grant Applications: Submitted applications (not drafts or ideas)
Job Applications: Roles applied for
Awards/Honors: Nominations received, awards won
Promotion/Tenure: Applications, outcomes

Think: Is this a concrete milestone or application? Or just planning?

4. Important Contacts & Relationships

Collaborations: Research collaboration proposals or agreements
Partnerships: Grant writing partnerships, joint projects
Networks: Connections made at conferences, international research networks
Students: PhD supervision milestones, significant correspondence
Institutions: New institutional relationships, key ongoing relationships

Think: Is this the start/milestone of an important relationship? Or routine correspondence?

5. Financial Records

Receipts, invoices, contracts by project and category
Log with: message ID, date, entity, project, description, amount, currency, payment status
Essential for grant acquittal and tax purposes

Think: Will we need this for financial reporting?

What to Skip

Most documents are noise. Skip:

Mass communications (newsletters, announcements, marketing, digests)
Administrative routine (meeting scheduling, calendar invites, reminders)
Declined invitations (conferences not attended, webinars skipped)
Generic outreach (CFPs, generic collaboration requests, surveys)
Automated systems (notifications, confirmations - unless significant)
Spam/phishing
Personal chitchat without professional substance
Room bookings and logistics
Normal student supervision (unless exceptional)
Regular staff interactions
Forum posts and mailing list discussions
Course coordination and teaching logistics
Standard reference requests

Test: Would I forget this within a week with no consequence?

Uncertain Cases

When uncertain:

Lean toward extraction in initial pass
Tag output with #review-classification for manual review
Better to extract and discard later than miss important information

Judgment Examples

EXTRACT: "Your paper 'Platform Governance' has been accepted by Nature" → Clear publication outcome

SKIP: "CFP: Submit to Journal of Platform Studies by Dec 1" → Generic invitation, no submission

EXTRACT: "Congratulations, your FT210100263 grant has been awarded $500K" → Grant outcome with specifics

SKIP: "Reminder: Your FT210100263 annual report is due next month" → Administrative reminder

EXTRACT: "Following our chat at IGF, I'd love to collaborate on disinformation research..." → New substantive collaboration starting

SKIP: "You're invited to join our webinar on content moderation" → Mass invitation, not personal

EXTRACT: Email from examiner with detailed feedback on PhD thesis → Supervision milestone

SKIP: "HDR student seminars happening this week" → Generic announcement

EXTRACT: "OSB Case 2025R final decision: Upheld with modifications..." → Actual OSB work product

SKIP: "OSB weekly meeting reminder" → Routine scheduling

Key Information to Identify

When you decide to extract, identify these elements:

People

Full name and affiliations
Contact information (if provided)
Role/position
Relationship to Nic (collaborator, contact, student, etc.)

Organizations

Universities and research institutions
Research groups or labs
Funding bodies
Conferences or professional organizations

Events

Conference/workshop name
Speaking engagements
Meetings with significance
Research visits

Projects/Collaborations

Research topics discussed
Grant proposals
Joint publications
Ongoing or proposed collaborations

Dates & Timing

When correspondence occurred
When events happened or will happen
Project timelines if mentioned

Topics/Tags

Research areas (platform-governance, content-moderation, AI-ethics, etc.)
Geographic locations
Types of activity (collaboration, speaking, reviewing)

Identifiers (CRITICAL)

Always extract canonical identifiers to allow future reference:

Email message ID (preferred)
Source UUID or unique identifier
URL or DOI
If no canonical ID exists, create citation: "email from [sender] to [recipient] dated [YYYY-MM-DD] subject: [subject]"

Store identifier inline with extracted information.

Storage

Use Skill(skill="remember") to store extracted information in the knowledge base. The remember skill handles:

Entity creation/updates
Format validation
Deduplication checking
Proper categorization

Store information using the memory server with properly formatted markdown. Tag extracted information appropriately for searchability and future retrieval.

Common approaches for email extraction:

Email metadata (sender, date, subject) → Use memory tags like #email, #correspondence
Project information → Use memory tags like #project
Coordinator/team roles → Store with descriptive relationships
Timeline/duration → Use memory tags like #timeline, #deadline
Status information → Use memory tags like #status
Relationships between people → Document relationships clearly with role descriptions

Storage principles:

Use clear, structured memory entries
Include all relevant metadata and timestamps
Tag appropriately for semantic search
Avoid inventing custom categories or types
Delegate all storage to the remember skill

Quality Standards

Confidence Levels

Only extract when information is clear and valuable
Mark uncertain extractions with #review-classification
Skip ambiguous or unclear information

Atomic Information

Each piece of information should be specific and verifiable
Include dates, sources, and context
Tag appropriately for searchability

Fail Fast

If document is malformed, skip it and log error
If extraction is ambiguous, skip it or tag for review
Continue processing other documents