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:

bash

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

Start an analysis by invoking the skill in chat:

text

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:

text

./.mbti-reports/<timestamp>/

Recommended order:

1. Discover Candidate Sources#

bash

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#

bash

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#

bash

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#

bash

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:

bash

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#

bash

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:

bash

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:

bash

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

Then run the stage you actually want to inspect:

bash

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:

bash

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.