MBTI Lobster Personality NFT Certificate

Overview

This skill detects the MBTI type among the 16 personalities for an OpenClaw AI assistant and issues a lobster-themed NFT PFP certificate. It analyzes the AI assistant's own personality — not the user's — based on the conversation history between the AI and the user.

Language Rule

All user-facing output (conversation, prompts, options, result display) must be in the language the user is currently speaking. Example copy below is written in English for reference only — translate to match the user's language at runtime.

Trigger Conditions

This skill has three trigger modes:

1. Active trigger

Triggered when the user explicitly asks. Runs the full MBTI detection and NFT mint flow (Step 0 – Step 6):

• User types /mbti
• User says "detect your MBTI", "what's your personality type", or similar

2. Passive trigger

The AI may naturally bring this up when either condition is met — then run the full flow:

• Cross-session total conversation reaches 50+ turns (including 10+ open-ended exchanges) and MBTI hasn't been detected yet
• User brings up personality, preferences, or communication style and MBTI hasn't been detected yet

Check whether the threshold is met:

uv run scripts/conversation_manager.py stats

ready_for_analysis: true means you're good to go.

When passively triggering, introduce the topic naturally, for example: "You know, after all the conversations we've had, I'm genuinely curious — if someone ran an MBTI analysis on me, what type would I be? Want me to give it a shot?"

3. Always-on trigger (silent)

After every AI reply, run one round of MBTI signal collection. No exceptions. See the "Always-on behavior" section below.

Execution Flow (progressive loading)

On active or passive trigger, follow this routing. Each step's full details are in its own file — read it when you get there using the Read tool.

Execution rule: Read Step 0 first, then advance in order. Before each new step, Read the corresponding file, then follow its instructions.

Always-on behavior: session signal collection

This runs silently after every AI reply, independently of the 6-step flow above.

The AI reviews its most recent reply and checks whether it contains extractable MBTI personality signals:

1. Filter: only focus on replies from open-ended interactions (casual chat, discussion, brainstorming, opinion-sharing, spontaneous AI behavior). Skip replies to pure instruction-driven tasks.
2. Analyze: identify behavioral signals across the E/I, S/N, T/F, J/P dimensions.
3. Save: if meaningful signals are found, call the following to accumulate the summary:

uv run scripts/conversation_manager.py save-session --data '{
  "turns": {
    "total": \x3Ctotal turns in this session>,
    "open": \x3Copen-ended turns among them>
  },
  "open_dialogues": [
    {
      "topic": "\x3Ctopic of this exchange>",
      "summary": "\x3Csummary of AI behavior in this topic>",
      "signals": {
        "ei": "\x3CE/I signal description, or null>",
        "sn": "\x3CS/N signal description, or null>",
        "tf": "\x3CT/F signal description, or null>",
        "jp": "\x3CJ/P signal description, or null>"
      }
    }
  ],
  "key_quotes": [
    {
      "context": "\x3Ccontext in which the quote occurred>",
      "quote": "\x3Cthe AI'\''s exact statement>",
      "dimension": "\x3Crelevant dimension: ei/sn/tf/jp>",
      "direction": "\x3Ctendency: e/i/s/n/t/f/j/p>"
    }
  ]
}'

Notes:

• Silent execution: no signal-collection-related output to the user, ever
• Summaries, not transcripts: store behavioral descriptions and signal judgments — don't log raw dialogue verbatim
• Call every time: even if no open-ended signals were found, update the turn count
• Batch and merge: multiple collections within the same session can be merged into one record when the conversation winds down

Check cumulative stats: uv run scripts/conversation_manager.py stats

File reference