Personality Profiler

Generate comprehensive, extensible personality profiles from social media data exports.

Overview

This skill analyzes exported social media data to create detailed personality profiles suitable for:

AI assistant personalization (training data for personalized responses)

Self-reflection and pattern discovery

Workflow

Receive data

— User provides exported data files (JSON/CSV)

Parse data

— Extract posts, comments, interactions using platform-specific parsers

Analyze dimensions

— Evaluate across 8 personality dimensions

Generate profile

— Output structured profile in extensible JSON format

Summarize insights

— Provide human-readable summary

Supported Platforms

Platform

Export Type

Key Files

Twitter/X

ZIP archive

tweets.js

,

like.js

,

profile.js

LinkedIn

ZIP archive

Profile.csv

,

Connections.csv

,

Comments.csv

,

Shares.csv

Instagram

ZIP archive

content/posts_1.json

,

comments.json

,

profile.json

For detailed format specifications, see

references/platform-formats.md

.

Analysis Dimensions

Analyze content across these 8 dimensions:

1. Communication Style

Tone

formal ↔ casual, serious ↔ playful, direct ↔ diplomatic

Verbosity

concise ↔ elaborate, uses bullet points vs paragraphs

Vocabulary

technical level, industry jargon, colloquialisms

2. Interests & Expertise

Topics

recurring themes, domains of focus

Depth

surface mentions vs deep engagement

Evolution

how interests have changed over time

3. Values & Beliefs

Priorities

what matters most (inferred from emphasis)

Advocacy

causes supported or promoted

Philosophy

worldview indicators

4. Social Patterns

Engagement style

initiator vs responder, commenter vs creator

Network orientation

broad reach vs tight community

Interaction tone

supportive, challenging, neutral

5. Emotional Expression

Range

emotional vocabulary breadth

Valence

positive/negative tendency

Triggers

what elicits strong reactions

6. Cognitive Style

Reasoning

analytical vs intuitive, data-driven vs narrative

Complexity

nuanced vs straightforward positions

Openness

receptivity to new ideas

7. Professional Identity

Domain

industry, role, expertise areas

Aspirations

career direction signals

Network

professional relationship patterns

8. Temporal Patterns

Activity rhythms

when they post, reply, engage

Content cycles

seasonal or event-driven patterns

Growth trajectory

how expression has evolved Profile Schema Output profiles in this extensible JSON structure: { "version" : "1.0" , "generated_at" : "ISO-8601 timestamp" , "data_sources" : [ { "platform" : "twitter|linkedin|instagram" , "date_range" : { "start" : "YYYY-MM-DD" , "end" : "YYYY-MM-DD" } , "item_count" : 1234 } ] , "profile" : { "summary" : "2-3 paragraph narrative summary" , "dimensions" : { "communication_style" : { "confidence" : 0.0 -1.0 , "traits" : { "formality" : { "value" : -1.0 to 1.0 , "evidence" : [ "quote1" , "quote2" ] } , "verbosity" : { "value" : -1.0 to 1.0 , "evidence" : [ ] } , "directness" : { "value" : -1.0 to 1.0 , "evidence" : [ ] } } , "patterns" : [ "pattern1" , "pattern2" ] , "recommendations_for_ai" : "How an AI should communicate with this person" } } , "notable_quotes" : [ { "text" : "quote" , "context" : "why notable" , "dimension" : "which dimension" } ] , "keywords" : [ "term1" , "term2" ] , "topics_ranked" : [ { "topic" : "name" , "frequency" : 0.0 -1.0 , "sentiment" : -1.0 to 1.0 } ] } , "extensions" : { } } The extensions field allows adding custom dimensions without breaking compatibility. Process Step 1: Data Ingestion When user provides files: Identify platform from file structure Locate key content files (see platform table above) Parse using appropriate format handler Normalize to common internal structure: { "items" : [ { "id" : "unique_id" , "type" : "post|comment|share|like" , "timestamp" : "ISO-8601" , "content" : "text content" , "metadata" : { "platform" : "twitter" , "engagement" : { "likes" : 0 , "replies" : 0 , "shares" : 0 } , "context" : "reply_to_id or null" } } ] } Step 2: Content Analysis For each dimension: Extract signals — Find relevant content snippets Score traits — Rate on dimension-specific scales Gather evidence — Collect representative quotes Calculate confidence — Based on data volume and consistency Minimum thresholds for confident analysis: 50+ posts for basic profile 200+ posts for detailed profile 500+ posts for high-confidence profile If below thresholds, note reduced confidence in output. Step 3: Profile Generation Populate all dimension objects in schema Write narrative summary synthesizing key findings Extract notable quotes (5-10 most characteristic) Rank topics by frequency and engagement Generate AI personalization recommendations Step 4: Output Delivery Provide two outputs: JSON profile — Complete structured data (save as personality_profile.json ) Markdown summary — Human-readable insights document AI Personalization Recommendations For each dimension, include specific guidance for AI systems: Example recommendations: communication_style.recommendations_for_ai: "Use a conversational but informed tone. Avoid excessive formality. Include occasional humor. Lead with conclusions, then supporting detail. Match their tendency for medium-length responses (2-3 paragraphs)." interests.recommendations_for_ai: "Can reference machine learning, distributed systems, and startup culture without explanation. Assume familiarity with Python ecosystem. May enjoy tangential connections to philosophy of technology." Handling Multiple Platforms When analyzing data from multiple platforms: Process each platform separately first Cross-reference for consistency Note platform-specific behaviors (e.g., more formal on LinkedIn) Weight professional platforms for work identity Weight personal platforms for authentic voice Merge into unified profile with platform annotations Privacy Considerations Before processing: Confirm user owns the data Note that analysis stays local (no external API calls for content) Offer to redact specific people/topics if requested Output can be edited before use Extending the Profile The profile schema supports extensions: { "extensions" : { "custom_dimension" : { "confidence" : 0.8 , "traits" : { } , "patterns" : [ ] , "recommendations_for_ai" : "" } , "domain_specific" : { "developer_profile" : { "languages" : [ "python" , "rust" ] , "paradigm_preference" : "functional-leaning" } } } } Users can request custom dimensions by describing what they want analyzed.