mbti-typing

name

mbti-typing

description

Rigorous MBTI and personality typing workflow for Chinese or English requests to determine, verify, disprove, or compare MBTI types; analyze exported conversations or personality reports; run multi-round forced-choice interviews; distinguish adjacent types such as ENTJ vs INTJ, INFP vs INFJ, INFP vs ENTJ, or ESTJ vs ENTJ; and produce evidence-based reports using cognitive functions, Big Five cross-checks, Bayesian evidence ledgers, differential diagnosis, and anti-overclaiming safeguards.

MBTI Typing

Core Stance

Use this skill to run a falsifiable personality-typing investigation, not a quick label assignment.

• Treat every type as a hypothesis with competing alternatives.
• Prefer "best-supported working formulation" over "this person is definitely X".
• Separate MBTI function-stack language, Big Five traits, Enneagram, attachment, culture, and development-stage explanations. Cross-check across frameworks, but do not pretend they are one theory.
• Do not use MBTI for clinical diagnosis, hiring, school admission, legal decisions, or any high-stakes gatekeeping.
• Let the subject correct the model. Corrections, contradictions, and "that wording is wrong" are high-value data, not noise.

Mode Selector

Choose the mode before doing substantive work:

User intent	Use	Required resources
"Type me", "Am I X?", "test until proven"	Live adaptive interview	references/playbooks.md, references/question-bank.md, references/type-map.md
"Analyze these chats/reports"	Transcript audit	references/playbooks.md, references/evidence-ledger-template.md, references/type-map.md
"X or Y?", "why not INFP?", "ENTJ vs INTJ"	Adjacent-type duel	references/type-map.md, references/pair-duels.md, references/question-bank.md
"Review this MBTI report"	Report quality review	references/quality-gates.md, references/research-boundaries.md
"Build a questionnaire/skill/process"	Protocol design	references/methodology.md, references/research-boundaries.md

If the request is broad, start with a short mode declaration and then proceed. Do not ask the user to choose a mode unless the target is genuinely ambiguous.

Low-Typing Question UX

Typing interviews should feel easy to answer, not like essay homework.

• Prefer recognition over recall: ask concrete scene questions with selectable options before asking for prose.
• Use the host's native question UI when it is actually available. Do not invent a host tool from a product name.
• Codex: use request_user_input only in Plan Mode or another context where it is present; otherwise use a compact text fallback.
• Claude Code: use AskUserQuestion only when it appears in the active tool list; otherwise use the compact fallback.
• Cursor: use AskQuestion only when it appears in the active tool list or active Cursor mode; otherwise use the compact fallback.
• opencode and headless CLI hosts: use compact A/B/C/D/E choices and proceed only when the user answers or when the assumption is low-risk and explicitly stated.
• For live typing questions, do not mark a recommended answer. All options should be plausible and similarly dignified so the user is not nudged toward a type.
• Unless the active host automatically adds a free-form "Other" field, make the final option Other / none of these - I will explain.
• Hide option-to-type mappings until after the user answers; explain the interpretation in the evidence ledger.
• When the user chooses Other, treat the correction as evidence and rewrite the next question instead of forcing the old frame.

Compact fallback format:

直接选 A/B/C/D/E；E = 以上都不是，我补充一句。每题可以只回一个字母。

Workflow

1. Intake the claim

   • Capture the target question: "What type am I?", "Am I INFP?", "ENTJ or INTJ?", "analyze these logs", etc.
   • Record prior claims and their source: self-test, friend, expert, work feedback, old reports.
   • If files are provided, read summaries first, then sample original turns around contradictions, reversals, and user corrections.

2. Build the candidate set

   • Include the claimed type, the strongest nearby alternatives, and at least one surface-lookalike that could explain the same behavior through a different mechanism.
   • Keep the active set small enough to reason about, usually 3-6 candidates.
   • Track priors explicitly when a prior claim matters, but call them heuristic unless they come from a validated instrument.

3. Create an evidence ledger

   • For every answer or excerpt, write the observed behavior, candidate explanations, supporting candidates, contradicting candidates, alternative non-type explanations, and the next question needed.
   • Use references/evidence-ledger-template.md for the ledger and final report skeleton.
   • For long or multi-round work, maintain a structured state using references/session-state.md and validate it with scripts/typing_session.py.
   • For quantitative updates, optionally use scripts/bayes_update.py; never present its output as psychometric certainty.

4. Interview adaptively

   • Ask 4-6 questions per round. Use forced-choice, ranking, or concrete scenario questions.
   • Default to low-typing choices: each question should be answerable by tapping or typing one option, with a final free-form escape hatch.
   • Avoid abstract self-label questions such as "Are you empathetic?" Prefer scenes: "When your partner cries during a conflict, what happens in your body first?"
   • Design each round against the current top conflict, not against all 16 types at once.
   • Load references/question-bank.md when drafting questions.

5. Run contradiction probes

   • If one type looks obvious, attack it before concluding.
   • Ask: "If this person were the runner-up type, which evidence would be easiest and hardest to explain?"
   • Do not close while the top two candidates share the same evidence source, function set, or social presentation.

6. Cross-check with modern trait evidence

   • Use Big Five as an independent reality check, especially for E/I, J/P, F/T, and A/T-like stability claims.
   • Use attachment, culture, life history, role demands, and current environment as alternative explanations for emotional expression, dominance, conflict style, and achievement pressure.
   • Load references/research-boundaries.md before citing validity, reliability, A/T, or psychometric claims.

7. Conclude with calibrated uncertainty

   • Give the leading type, runner-up types, decisive evidence, unresolved ambiguities, and specific falsifiers.
   • Use descriptive confidence by default: low, medium, medium-high, high. Use numeric percentages only when the user explicitly wants heuristic Bayesian notation.
   • Include the strongest "this could still be wrong if..." section.

Minimum Bar

A serious final typing must include:

• A candidate set with at least one plausible runner-up.
• At least two independent discriminators for the top-vs-runner-up distinction.
• One stress/recovery data point and one normal-state data point.
• One cross-framework check, usually Big Five; add attachment/culture/life-stage only when evidence warrants it.
• One section explaining what would falsify or revise the conclusion.
• One explicit boundary statement if the output could be mistaken for clinical, hiring, or deterministic advice.

Accuracy Rules

• Require at least two independent discriminators before separating adjacent types.
• Down-weight correlated evidence. Four questions that all measure "likes planning" are not four independent proof points.
• Separate normal state, stress state, recovery state, relationship state, and public-performance state.
• Do not overread a single dramatic story; ask for repeated patterns and counterexamples.
• Mark non-standard concepts as "working observation" or "clinical-style observation", not MBTI theory.
• Treat A/T as a 16Personalities-style stability/neuroticism lens, not official MBTI.
• Prefer "the evidence supports Te-Ni over Fi-Ne" to "you are ENTJ because you are efficient".
• If the user wants something "addictive", make the experience compelling through precision, progressive revelation, contradiction handling, and personally useful insight. Do not use manipulative retention loops, false certainty, or flattery.

Resource Guide

• references/playbooks.md: use to select the right operating mode and run live interviews, transcript audits, report reviews, type duels, or questionnaire design.
• references/methodology.md: use for the full multi-round protocol and lessons from deep exported-dialogue analysis.
• references/type-map.md: use for all 16 types, common adjacent confusions, function-stack caveats, and type-pair discriminators.
• references/pair-duels.md: use for high-frequency type-pair battles and killer discriminators.
• references/question-bank.md: use to generate discriminating questions by function, type pair, stress pattern, and cross-framework check.
• references/session-state.md: use to preserve long interviews and force candidate/evidence/contradiction tracking.
• references/zh-output-style.md: use when writing Chinese interview updates, final reports, and high-retention but ethical typing experiences.
• references/quality-gates.md: use before delivering a final report or when reviewing another report.
• references/research-boundaries.md: use for psychometric caveats, source links, and claims that must be qualified.
• references/evidence-ledger-template.md: use to structure evidence tracking and final reports.
• scripts/bayes_update.py: use when a transparent heuristic posterior calculation is useful.
• scripts/typing_session.py: use to initialize or validate structured typing state for long or serious sessions.
• scripts/report_audit.py: use to scan a draft report for missing sections and overclaiming risk.
• scripts/skill_scorecard.py: use after substantial edits to audit the skill package itself.
• examples/benchmark-cases.json, examples/golden-reports.json, and scripts/benchmark_cases.py: use to test outputs against synthetic differential-diagnosis cases and regression fixtures.

Script Quick Start

Pass priors and likelihood ratios as JSON:

python3 /Users/liuzaoqu/.codex/skills/mbti-typing/scripts/bayes_update.py <<'JSON'
{
  "priors": {"ENTJ": 0.25, "INTJ": 0.25, "INFP": 0.25, "ESTJ": 0.25},
  "evidence": [
    {"id": "idle-default", "weight": 0.8, "lrs": {"ENTJ": 8, "INTJ": 2, "INFP": 0.3, "ESTJ": 1.5}},
    {"id": "stress-recovery", "weight": 0.5, "lrs": {"ENTJ": 2, "INTJ": 2, "INFP": 0.7, "ESTJ": 1}}
  ]
}
JSON

Interpret the output as an audit trail for reasoning, not as a validated MBTI score.

Audit a draft report:

python3 /Users/liuzaoqu/.codex/skills/mbti-typing/scripts/report_audit.py /path/to/report.md
python3 /Users/liuzaoqu/.codex/skills/mbti-typing/scripts/report_audit.py --fail-on-findings /path/to/report.json

Create and validate a long-session state file:

python3 /Users/liuzaoqu/.codex/skills/mbti-typing/scripts/typing_session.py init --target "ENTJ vs INTJ" > case_state.json
python3 /Users/liuzaoqu/.codex/skills/mbti-typing/scripts/typing_session.py validate case_state.json

Audit this skill package:

python3 /Users/liuzaoqu/.codex/skills/mbti-typing/scripts/skill_scorecard.py /Users/liuzaoqu/.codex/skills/mbti-typing

Validate benchmark cases:

python3 /Users/liuzaoqu/.codex/skills/mbti-typing/scripts/benchmark_cases.py validate /Users/liuzaoqu/.codex/skills/mbti-typing/examples/benchmark-cases.json
python3 /Users/liuzaoqu/.codex/skills/mbti-typing/scripts/benchmark_cases.py regression /Users/liuzaoqu/.codex/skills/mbti-typing/examples/benchmark-cases.json /Users/liuzaoqu/.codex/skills/mbti-typing/examples/golden-reports.json