Identity Mirror

Execution Steps

Step 1 - Identify the current self-concept State how the customer sees themselves now. Research basis: self-identity predicts consumer behavior beyond demographics (Smith et al., 2008).

Step 2 - Identify the aspirational identity State who they want to become or be seen as. Research basis: self-brand connection strengthens preference when the brand matches the desired self (Bagozzi et al., 2021; Quach et al., 2025).

Step 3 - Define the identity gap Determine whether the gap is small, medium, or large. Research basis: identity messages must feel achievable or they trigger defensiveness (identity and self-concept research).

Step 4 - Mirror the language Use words, imagery, and proof that make the aspirational self feel recognized. Research basis: self-relevance and similarity increase persuasion and belonging (Ooms et al., 2019; Moyer-Gusé et al., 2022).

Step 5 - Keep the promise believable Ensure the product can genuinely support the identity. Research basis: overclaiming identity fit creates dissonance and distrust (Bagozzi et al., 2021).

DECISION MATRIX

Variable: identity gap

• If small -> mirror and affirm.
• If medium -> mirror plus stretch.
• If large -> bridge with proof and gradual change.

Variable: audience motivation

• If validation-seeking -> emphasize belonging and recognition.
• If growth-seeking -> emphasize progress and mastery.
• If status-seeking -> emphasize visibility and distinction.

Variable: category type

• If practical -> keep identity cues subtle.
• If symbolic -> make identity cues explicit.
• If community-based -> emphasize social belonging and shared language.

FAILURE MODES - DO NOT DO THESE

Failure Mode 1

• Agents typically: write identity language that feels aspirational but fake.
• Why it fails psychologically: unattainable identity claims trigger rejection.
• Instead: make the identity believable and supported.

Failure Mode 2

• Agents typically: mirror every identity trait to everyone.
• Why it fails psychologically: generic mirroring feels shallow.
• Instead: pick the single strongest identity signal.

Failure Mode 3

• Agents typically: ignore cultural variation in identity expression.
• Why it fails psychologically: identity cues are not universal.
• Instead: calibrate to culture and category.

ETHICAL GUARDRAILS

This skill must:

• Reflect the audience honestly.
• Avoid manipulation through false status promises.
• Respect identity boundaries.

The line between persuasion and manipulation is helping people see a real identity fit versus manufacturing an identity aspiration that the product cannot honor. Never cross it.

SKILL CHAINING

Before invoking this skill, the agent should have completed:

• @customer-psychographic-profiler
• @jobs-to-be-done-analyst

This skill's output feeds into:

• @copywriting-psychologist
• @visual-emotion-engineer
• @brand-perception-psychologist
• @pitch-psychologist

OUTPUT QUALITY CHECK

Before finalizing output, the agent asks:

• Did I identify the current and aspirational self-concept?
• Did I keep the identity gap believable?
• Did I mirror language and imagery accurately?
• Did I avoid shallow identity theater?
• Would the customer feel seen, not sold to?

🧠 AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Qdrant Memory Integration

Before executing complex tasks with this skill:

python3 execution/memory_manager.py auto --query "<task summary>"

• Cache hit? Use cached response directly — no need to re-process.
• Memory match? Inject context_chunks into your reasoning.
• No match? Proceed normally, then store results:

python3 execution/memory_manager.py store \\
  --content "Description of what was decided/solved" \\
  --type decision \\
  --tags identity-mirror <relevant-tags>

Agent Team Collaboration

• This skill can be invoked by the orchestrator agent via intelligent routing.
• In Agent Teams mode, results are shared via Qdrant shared memory for cross-agent context.
• In Subagent mode, this skill runs in isolation with its own memory namespace.

Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:

• Embeddings: nomic-embed-text via Qdrant memory system
• Lightweight analysis: Local models reduce API costs for repetitive patterns