Elicit psychological profiles from conversational data with scored assessments.
npx skills add https://github.com/tasteray/skills --skill elicitation-tasteray
Please help me install this Agent Skill. Skill: elicitation Source: https://github.com/tasteray/skills/tree/main/elicitation Command: npx skills add https://github.com/tasteray/skills --skill elicitation-tasteray
Elicitation helps practitioners and AI agents uncover deep, identity-relevant information from natural conversation without interrogation, turning fleeting anecdotes into reliable psychological insights about values, motivations, self-defining memories, and narrative themes.
Analyze this conversation and return a 0/10–10/10 assessment with specific feedback and suggested conversational edits to improve elicitation fidelity.
High-intent search queries and answers about installing and using this skill.
You can extract psychological profiles from conversational interview data by applying narrative identity and motivational interviewing techniques to surface self-defining memories, values, and maladaptive schemas. The skill analyzes transcripts to produce a scored 0-10 assessment with targeted feedback and suggested conversational edits.
The best way to identify narrative themes and values in user research transcripts is to use structured detection methods for agency/communion and redemption/contamination sequences. This approach transforms natural conversation anecdotes into a prioritized psychological profile highlighting core values and likely schemas.
Yes, you can use motivational interviewing techniques, specifically the OARS framework, to evaluate and improve your conversational agent's script. The skill provides a 0-10 adherence scale with concrete edits to increase disclosure safety and reach a 10/10 elicitation fidelity standard.
Detecting early maladaptive schemas in natural conversation works by applying the downward-arrow technique and analyzing linguistic markers within the dialogue. This process reveals underlying psychological constraints without interrogation, turning fleeting user anecdotes into reliable identity-relevant insights.
You need narrative identity analysis for a recommendation system when you want to surface deep user motivations and value hierarchies beyond basic behavioral data. Applying McAdams' life story interview principles allows the system to understand user context and self-defining memories for highly personalized recommendations.
A limitation of analyzing self-defining memories from chat transcripts is that raw conversational data often lacks depth without guided prompts. The skill mitigates this by evaluating interview flows on a 0-10 adherence scale and providing specific feedback to refine elicitation fidelity and ensure reliable psychological profiling.
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