synthetic-users

Build and interview synthetic user panels to surface objections and hypotheses before build.

1|Updated Jun 30, 2026
One-click install
npx skills add https://github.com/Lia-Creative/lia-plugins --skill synthetic-users-lia-creative
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: synthetic-users
Source: https://github.com/Lia-Creative/lia-plugins/tree/main/lia-tools/skills/synthetic-users
Command: npx skills add https://github.com/Lia-Creative/lia-plugins --skill synthetic-users-lia-creative

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Teams often commit design and build budget to a brief that has never been tested against a critical reader, and asking an LLM to play a customer normally produces flattery rather than useful pushback. This Skill turns research artefacts or a thin brief into a panel of psychologically coherent, deliberately critical synthetic users that surface objections and hypotheses fast. ## Core Features & Use Cases - Four-stage panel pipeline: build persona profiles from jobs, pains, and gains; encode behaviourally-structured journey maps as CSV; assemble an anti-idealism system-prompt config; then run facilitated panel sessions. - Anti-Idealism Protocol: forces personas to lead with objections, disagree with each other, speak from lived experience, and carry emotional residue and bias triggers across journey stages. - Assumption Trace and Facilitator Summary: every response is tagged GROUNDED, INFERENCE, ASSUMPTION, or BIAS-RISK, and sessions close with themes, deal-breakers, and validation priorities for real research. - Cold-start mode: a proto-profiles prompt generates differentiated archetypes from a thin business brief, flagging what is grounded, inferred, or speculative. - Use Case: Before building a new onboarding flow, run the panel against the epic to collect the top objections and unmet-need hypotheses, then convert the strongest into questions for real user interviews. ## Quick Start Ask the agent to run a synthetic user panel on your problem brief or epic to surface objections and hypotheses before build.

Frequently Asked Questions about synthetic-users

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create synthetic user personas for product discovery?▼

Build one markdown profile per persona covering overview, goals, functional/emotional/social jobs, grouped pains with intensity, gains, and behavioural motivations. Pair each profile with a behaviourally encoded journey map CSV, then assemble the facilitator config and run the panel.

How do I pressure-test a product brief before building?▼

Run a synthetic user panel against the brief: pose questions to 3-6 personas, collect in-character responses that lead with objections, and read the facilitator summary for deal-breakers, conflicts, and validation priorities to take into real user research.

Can synthetic users replace real user research or validation?▼

No. Synthetic users produce observations, patterns, and hypotheses only, never insight or validation. Output must be labelled synthetic, kept out of insight ledgers, and used to aim real interviews and research where they matter most.

What inputs does a synthetic user panel need?▼

Each persona needs a profile markdown file and a journey map CSV encoding stage metadata, active jobs, pain intensity, emotional residue, identity tension, bias triggers, and decision thresholds. With thin data, a proto-profiles prompt generates starter archetypes flagged as grounded, inferred, or speculative.

Why do AI-generated personas give unrealistic positive feedback?▼

LLMs default to sycophancy and idealism when roleplaying customers. The Anti-Idealism Protocol counters this by requiring 2-3 specific objections per response, banning filler praise, enforcing bounded rationality, and tagging speculative reasoning in an assumption trace.

How many personas should a synthetic user panel have?▼

Use 3 to 6 personas per panel. Fewer than 3 loses the disagreement that makes a panel useful, and more than 6 blurs the focus group into noise. Differentiate personas behaviourally rather than demographically.