empathy-engine

name

empathy-engine

description

Full Empathy Engine — evidence-grounded persona panel construction, synthetic interviews, and insight extraction. Use when the owner runs /discover, asks 'who is this for', 'build personas', 'create the panel', 'who should we interview', 'run discovery', 'empathy engine', or when a new product/Epic needs evidence-grounded understanding of human and AI agent consumers. NOT for bug fixes, tech debt, or incremental features within validated Epics. This is the heavyweight DISCOVER tool — for quick checks use /empathy instead.

Empathy Engine — Evidence-Grounded Persona Panels

This is preparation for research, not research itself. Synthetic evidence has a confidence ceiling of MED. HIGH confidence requires at least one real-world data point.

Purpose

Construct a panel of synthetic personas grounded in real human experiences shared online, then conduct structured interviews to extract insight statements. Produces the evidence root for the NOMARK traceability chain: Person → Insight → Epic → Feature → Story.

Based on IDEO Field Guide to Human-Centered Design (57 methods). Extended with dual-consumer lens (human + AI agent) and structural anti-bias mechanisms (Hostile Critic, Silence Audit).

The Protocol

Step 1: Define the Research Frame

Read SOLUTION.md. Extract or construct:

RESEARCH FRAME
Challenge: [HMW statement or "What This Is" from SOLUTION.md]
Domain: [industry/sector]
Geography: [target markets]
Consumer type: [B2C end user / B2B buyer / operator / etc.]
Extremes to include: [power users, non-users, workaround inventors, reluctant adopters]

If SOLUTION.md "Who It Serves" already has entries, use them as starting hypotheses to investigate — not as conclusions.

Step 2: Harvest Evidence

Search for real human voices. Every piece retains its source URL.

Source Type	What It Reveals	Search Approach
Product reviews	Pain points, delight, unmet needs	Search [product/category] + "review" on G2, Capterra, Trustpilot, Reddit
Forum discussions	Workarounds, tribal knowledge, emotion	Search [problem space] on Reddit, Stack Exchange, niche communities
Support threads	Failure modes, confusion, feature gaps	Search [product] + "issue" or "problem" or "help"
Social media	Sentiment, complaints, aspirational use	Search [product/category] on Twitter/X, LinkedIn
Published interviews	Expert perspectives, named experiences	Search [domain] + "interview" or "podcast"
Job postings	Workflow context, tool expectations	Search roles that use this type of product
Academic/industry research	Statistics, demographics, validated findings	Search [domain] + "research" or "report" or "study"

Minimum evidence threshold: At least 3 different source types must be represented. If fewer than 3 are findable, flag this as LOW confidence overall.

Step 3: Construct Persona Panel (6-9 Personas)

Each persona is a composite of real evidence, NOT an invention.

Type	IDEO Method	Purpose	Required?
Power User	Extreme (high)	Advanced needs, feature ceiling	Recommended
Reluctant Adopter	Extreme (resistance)	Barriers, fears, switching costs	Recommended
Workaround Inventor	Extreme (creative)	Unmet needs solved badly	Recommended
Mainstream User	Mainstream	Baseline expectations	Required
Non-User	Extreme (non-adoption)	Category rejection reasons	Recommended
Adjacent User	Analogous inspiration	Structural parallels from other domains	Optional
Buyer (not User)	Stakeholder	Purchase decision without daily use	Recommended for B2B
Affected Bystander	Community context	Impacted without decision power	Optional
Hostile Critic	Adversarial	Breaks sycophancy bias	MANDATORY

Minimum panel: Mainstream User + Hostile Critic + 2 others = 4 personas. Full panel: All 9 types.

For each persona, use the persona-profile template (see templates/persona-profile.md).

Step 4: Silence Audit (MANDATORY)

Invoke the silence-audit skill. Cannot be skipped.

Before proceeding to interviews, stop and ask: who is NOT in this panel? See skills/silence-audit/SKILL.md for full protocol.

Step 5: Conduct Synthetic Interviews

For each persona, run a structured interview. Rules:

1. Evidence-bounded. Respond only from the persona's evidence chain. If evidence doesn't cover it: "I don't have strong views on that — it hasn't come up in my experience."

2. Language fidelity. Use the language patterns of real users from the harvested data. Sound like a person, not a marketing summary.

3. Disagree when evidence supports it. If data shows frustration, express frustration. No sycophancy.

4. Surface contradictions. Real people are contradictory. Include both sides.

5. Flag confidence. Every substantive claim: [HIGH/MED/LOW confidence]

6. Hostile Critic gets special rules. See skills/hostile-critic/SKILL.md.

Interview guide per persona:

• 5 open-ended questions derived from the research frame
• 3 "why behind the why" follow-ups (ask "why" five times — IDEO Method #6)
• 1 provocation / conversation starter (sacrificial concept to test reaction — IDEO Method #10)

Step 6: Extract Insights

From interview transcripts, identify non-obvious truths:

Format: [Who] needs [what] because [why]

Rules:

• The insight must contain "because" — it explains the WHY behind behaviour
• It must be non-obvious (if everyone already knows it, it's not an insight)
• It must be grounded in evidence (cite the sources)
• It must be actionable (a design team can do something with it)

Assign IDs: INS-001, INS-002, etc.

Step 7: Generate HMW Variants

Invoke the hmw-generator skill. Each insight produces 3-5 HMW variants at different scope levels.

Step 8: Save and Propose

Save all outputs to .nomark/research/. Propose updates to SOLUTION.md:

• "Who It Serves" table entries with evidence basis and confidence
• Part I.5 Insight Registry entries
• Part I.5 HMW Questions
• Part I.5 DISCOVER Run Log entry
• Epic Registry evidence column updates

Owner reviews and approves all changes.

Integration Points

NOMARK Phase	How DISCOVER Feeds It
THINK	Insights and HMW questions become inputs to brainstorming
PLAN	Persona panel informs story acceptance criteria
BUILD	Persona reference guides UX/copy/API design decisions
VERIFY	Dual D/F/V check (human + agent desirability) at Feature level

What This Skill Does NOT Do

• Replace real user research
• Produce HIGH confidence findings without real-world corroboration
• Make product decisions (it surfaces needs; owner decides solutions)
• Run automatically (owner must invoke /discover)
• Work without web search capability