MBTI Analyzer

---

name: mbti-analyzer description: Analyze a user's MBTI from authorized OpenClaw memory, session history, and workspace notes. Use when the user asks for MBTI analysis, personality inference without a questionnaire, an evidence-backed personality report, or a structured type hypothesis from historical conversations. version: 0.4.0 triggers: - "MBTI" - "personality analysis" - "type me" - "分析我的 MBTI" - "性格分析" - "人格报告" metadata: openclaw: commands: - command: mbti-report description: Generate an MBTI report from authorized historical data. clawdbot: emoji: "🧠" requires: bins: ["python3"]

---

MBTI

Generate an evidence-backed MBTI report from authorized OpenClaw history and workspace notes.

Quick Start

This package is a skill. The public handoff line for other agents lives in README.md.

Primary entry points:

trigger phrases: MBTI, personality analysis, type me

skill command: mbti-report

Minimal runtime requirement:

python3

Local install for development or manual setup:

ln -s /absolute/path/to/mbti "$CODEX_HOME/skills/mbti"

Start an analysis by invoking the skill in chat:

Analyze my MBTI using only my authorized memory and session history

For agents and maintainers:

read this page top to bottom before running any script

use the existing pipeline scripts below as implementation steps

do not skip the authorization step

do not infer MBTI directly from raw history

At A Glance

What this skill produces:

report.html: primary deliverable

report.md: compact summary

analysis_result.json: type hypothesis, confidence, follow-up questions

evidence_pool.json: scored and traceable evidence

What the first interaction should do:

1. Discover candidate source categories. 2. Show the user what is available. 3. Ask which categories are authorized. 4. Run the extraction → evidence → inference → report pipeline.

Core Rule

Always separate the workflow into two layers:

1. Full extraction from authorized sources into structured records and an evidence pool. 2. MBTI inference only from the evidence pool and source summary.

Do not infer MBTI directly from the full raw history.

When To Use

Use this skill when the user wants:

MBTI analysis from existing conversations or memory

personality inference without filling out a questionnaire

a professional-looking personality report with evidence

a structured summary of likely type, adjacent alternatives, and uncertainties

Do not use this skill for clinical diagnosis or mental-health assessment.

Authorization First

Before reading any source content:

1. Run source discovery. 2. Show the user which source categories are available. 3. Explain that the report may quote short excerpts unless quoting is disabled. 4. Ask the user to confirm which source categories are allowed.

Default candidate categories:

workspace long-term memory: MEMORY.md

workspace daily memory: memory/*.md

OpenClaw sessions: ~/.openclaw/agents/*/sessions/*.jsonl

OpenClaw memory index: ~/.openclaw/memory/main.sqlite

OpenClaw task metadata: ~/.openclaw/tasks/runs.sqlite

OpenClaw cron metadata: ~/.openclaw/cron/runs/*.jsonl

Default exclusions:

.env

credentials/*

identity/*

device files

approval files

generic config files

gateway and runtime logs

Execution Flow

If the user does not provide an output directory, write results to:

./.mbti-reports//

Recommended order:

1. Discover Candidate Sources

python3 {baseDir}/scripts/discover_sources.py \
  --workspace-root . \
  --openclaw-home ~/.openclaw \
  --output /tmp/mbti-source-manifest.json

Use the manifest to explain what can be analyzed. Do not read content yet.

2. Ingest Authorized Sources

python3 {baseDir}/scripts/ingest_all_content.py \
  --manifest /tmp/mbti-source-manifest.json \
  --approved-source-types workspace-long-memory,workspace-daily-memory,openclaw-sessions \
  --output-dir ./.mbti-reports/

This creates:

raw_records.jsonl

source_summary.json

3. Build Evidence Pool

python3 {baseDir}/scripts/build_evidence_pool.py \
  --raw-records ./.mbti-reports//raw_records.jsonl \
  --source-summary ./.mbti-reports//source_summary.json \
  --output ./.mbti-reports//evidence_pool.json

This stage should:

keep recall high

remove obvious tool noise

flag pseudo-signals

merge repeated facts

retain traceable evidence references

4. Infer MBTI From Evidence Pool

python3 {baseDir}/scripts/infer_mbti.py \
  --evidence-pool ./.mbti-reports//evidence_pool.json \
  --source-summary ./.mbti-reports//source_summary.json \
  --output ./.mbti-reports//analysis_result.json

Inference rules:

use four preferences as the primary decision layer

use type dynamics and cognitive functions only as a consistency check

weigh independent strong evidence above repeated weak signals

keep counterevidence visible

generate follow-up questions when margins are weak

If analysis_result.json contains needs_followup: true and the user is available to answer, ask the follow-up questions before finalizing the report.

5. Apply Follow-Up Answers And Rerun

After the user answers the low-confidence questions, rerun the pipeline with the answers incorporated as additional user evidence:

python3 {baseDir}/scripts/apply_followup_answers.py \
  --raw-records ./.mbti-reports//raw_records.jsonl \
  --source-summary ./.mbti-reports//source_summary.json \
  --analysis ./.mbti-reports//analysis_result.json \
  --output-dir ./.mbti-reports/ \
  --answer "S/N=" \
  --answer "J/P="

This updates:

raw_records.jsonl

source_summary.json

followup_answers.json

evidence_pool.json

analysis_result.json

report.md

report.html

If the user declines to answer, keep the current report and surface the uncertainty explicitly.

6. Render Final Reports

python3 {baseDir}/scripts/render_report.py \
  --analysis ./.mbti-reports//analysis_result.json \
  --evidence-pool ./.mbti-reports//evidence_pool.json \
  --output-dir ./.mbti-reports/ \
  --quote-mode summary \
  --open

Add --open to automatically open the HTML report in the default browser after rendering.

This creates:

report.md

report.html

7. Render A Standalone HTML Preview

When you only need to tune layout, CSS, spacing, or badge/theme behavior, use the built-in preview mode instead of rerunning discovery, ingestion, evidence construction, and inference:

python3 {baseDir}/scripts/render_report.py \
  --debug-preview \
  --debug-type INTP \
  --output-dir /tmp/mbti-preview

This creates a fully populated report.html and report.md from a bundled fixture so report debugging does not depend on prior pipeline artifacts.

Stage Testing

When you want to test one stage in isolation, prepare a synthetic fixture for that stage and then run the real stage script against those files.

Prepare fixture inputs:

python3 {baseDir}/scripts/prepare_stage_fixture.py \
  --stage infer \
  --output-dir /tmp/mbti-stage-infer

Then run the stage you actually want to inspect:

python3 {baseDir}/scripts/infer_mbti.py \
  --evidence-pool /tmp/mbti-stage-infer/evidence_pool.json \
  --source-summary /tmp/mbti-stage-infer/source_summary.json \
  --output /tmp/mbti-stage-infer/analysis_result.json

Supported fixture stages:

discover: generates synthetic workspace and OpenClaw source files

ingest: adds source_manifest.json

evidence: adds raw_records.jsonl and source_summary.json

infer: adds evidence_pool.json

render: adds analysis_result.json

followup: adds answers_input.json for apply_followup_answers.py

Smoke-test all stage entrypoints with:

python3 -m unittest tests.test_stage_smoke

Report Rules

The HTML report is the primary artifact. The chat reply should only provide:

the most likely type

confidence level

2-4 key observations

the output file paths

Do not freestyle the full report in chat if report.html already exists.

Evidence Rules

Treat the following as high-risk pseudo-signals:

requests about how the assistant should behave

formatting preferences

tool and workflow instructions without self-descriptive context

command output, logs, stack traces, or copied machine text

Treat the following as stronger evidence:

repeated self-descriptions

stable decision-making patterns

recurring work and reflection habits

conflict between desired structure and actual behavior

cross-source consistency

Read these references when needed:

analysis_framework.md

evidence_rubric.md

report_copy_contract.md

report_structure.md

Output Discipline

Keep tone rigorous and non-clinical.

Do not use emoji in the final report.

Present the result as a best-fit hypothesis, not a fixed truth.

Always include at least one "why not the adjacent type" section.

This package is a skill. The public handoff line for other agents lives in README.md.

Primary entry points:

trigger phrases: MBTI, personality analysis, type me

skill command: mbti-report

Minimal runtime requirement:

python3

Local install for development or manual setup:

ln -s /absolute/path/to/mbti "$CODEX_HOME/skills/mbti"

Start an analysis by invoking the skill in chat:

Analyze my MBTI using only my authorized memory and session history

For agents and maintainers:

read this page top to bottom before running any script

use the existing pipeline scripts below as implementation steps

do not skip the authorization step

do not infer MBTI directly from raw history