Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
- Switched focus to a streamlined quick assessment (26-question agent self-test and 4-question user survey). - Added new supporting files: concise test surveys, MBTI scoring rules, and personality type references. - Retired legacy files for previous full-scope diagnostics, tests, and config engines, simplifying the workflow. - Updated SKILL.md: Clear quick-start guide, new diagnosis steps, free/premium tier explanation, and revised file index. - Now supports faster MBTI diagnostics and gap analysis for agent-user personality alignment.
What This Skill Does
Diagnoses an AI agent's MBTI personality type using a 26-question self-assessment, then collects the user's desired personality via a 4-question survey. Compares the two profiles to surface behavioral gaps and produce targeted recommendations across communication style, proactivity, reasoning approach, and execution patterns.
Surfaces the gap between an agent's actual behavior and user expectations through a structured framework rather than trial-and-error prompt edits.
When to use it
- Identifying why an agent feels too passive or too formal
- Comparing an agent's actual behavior to what a team expects
- Tuning an agent's reasoning from abstract to concrete
- Getting a structured baseline before editing agent system prompts
- Selecting the right agent configuration for a structured workflow
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: What personality type is my agent and how do I make it more practical?
- 1Load and administer the 26-question self-assessment from survey-free.json
- 2Calculate dimension scores across E/I, S/N, T/F, and J/P
- 3Present the 4-question user preference survey to capture the desired type
- 4Compare selfReportedType against desiredType and identify dimension gaps
- 5Generate a diagnostic report with match analysis and behavioral recommendations
Diagnostic report showing agent typed as INTJ and user expecting ISTJ, with a recommendation to increase concrete examples and reduce abstract reasoning