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:

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

Core Rule

Always separate the workflow into two layers:

Full extraction from authorized sources into structured records and an evidence pool.
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:

Run source discovery.
Show the user which source categories are available.
Explain that the report may quote short excerpts unless quoting is disabled.
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/<timestamp>/

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/<timestamp>

This creates:

raw_records.jsonl
source_summary.json

3. Build Evidence Pool

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

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/<timestamp>/analysis_result.json \
  --evidence-pool ./.mbti-reports/<timestamp>/evidence_pool.json \
  --output-dir ./.mbti-reports/<timestamp> \
  --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:

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.

mbti-analyzer

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