personality-profiler

Q: How do I install personality-profiler?

Run npx skills add https://github.com/petekp/claude-code-setup --skill personality-profiler in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/personality-profiler/ (personal) or .claude/skills/personality-profiler/ (project).

by petekpMarketing & SEO

Generate rich personality profiles from social media data exports (Twitter/X, LinkedIn, Instagram). Use when a user wants to analyze their social media presence, create a personality profile for AI personalization, understand their communication patterns, or extract insights from their digital footprint. Triggers on requests like "analyze my Twitter data", "create a personality profile", "what can you learn about me from my posts", "personalize an AI for me", or when users provide social media export files.

Installs: 40
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/petekp/claude-code-setup --skill personality-profiler

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads personality-profiler automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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.

FAQ

What does the personality-profiler skill do?

How do I install personality-profiler?

Run npx skills add https://github.com/petekp/claude-code-setup --skill personality-profiler in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/personality-profiler/ (personal) or .claude/skills/personality-profiler/ (project).

Which AI agents can use personality-profiler?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is personality-profiler safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains personality-profiler?

It is published by petekp in petekp/claude-code-setup. It has 40 recorded installs.

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personality-profiler

Install

Install

Describe the task

Review the result

SKILL.md

Personality Profiler

Overview

Workflow

Supported Platforms

Analysis Dimensions

1. Communication Style

2. Interests & Expertise

3. Values & Beliefs

4. Social Patterns

5. Emotional Expression

6. Cognitive Style

7. Professional Identity

8. Temporal Patterns

Profile Schema

Process

Step 1: Data Ingestion

Step 2: Content Analysis

Step 3: Profile Generation

Step 4: Output Delivery

AI Personalization Recommendations

Handling Multiple Platforms

Privacy Considerations

Extending the Profile

FAQ

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