/prd-v10-continuous-discovery-torres

name: prd-v10-continuous-discovery-torres
description: >
  Establish weekly customer-discovery cadence and Opportunity Solution Tree practice using Teresa
  Torres's Continuous Discovery Habits framework during PRD v1.0 Market Adoption. Triggers on
  requests to set up discovery cadence, build opportunity solution tree, run weekly customer
  interviews, or when user asks "Torres", "continuous discovery", "opportunity solution tree",
  "outcomes vs outputs", "weekly interviews", "assumption mapping". Outputs CFD-* discovery
  entries and updates to ADO-STAGE-* and PER-* with new evidence.
context: fork
allowed-tools:
  - Read
  - Write
  - Edit
  - Glob
  - Grep

execution_modes:
  default: standard
  supports: [quick, standard, deep]

Continuous Discovery (Torres)

Position in workflow: v1.0 Crossing the Chasm (Moore) → **v1.0 Continuous Discovery (Torres)** → v1.0 Mom Test, Case Study Builder

Execution Mode

Default is **standard**. See [`.claude/rules/08-skill-execution-modes.md`](../../rules/08-skill-execution-modes.md) for selection logic.

| Mode | What this skill produces | |------|--------------------------| | **quick** | One outcome + 3–5 opportunities + interview cadence proposal | | **standard** | Full Opportunity Solution Tree (outcome → opportunities → solutions → assumption tests); weekly 3-interview cadence; assumption-mapping for top solution | | **deep** | Multi-outcome tree; per-opportunity confidence scoring; full assumption tests with experiment plans; cross-discipline trio (PM/design/eng) participation rules |

What This Does

Establishes **continuous discovery** as a weekly habit, not a one-time research phase. The shift from "we do research before building" to "we talk to customers every week" is what separates teams that find PMF from teams that drift.

The work product is the **Opportunity Solution Tree** — a structured artifact that connects a measurable business outcome to opportunities (customer needs), to candidate solutions, to assumption tests. The tree is *living*: it grows and prunes as interviews accumulate.

This skill assumes [prd-v10-mom-test-interview](../prd-v10-mom-test-interview/SKILL.md) is the discipline for *how* to interview; this skill is the discipline for *what to do with* the interviews.

How It Works

1. **Define one measurable outcome** — Not an output ("ship feature X"), but an outcome ("activated users in beachhead segment grow 20% MoM"). Anchor in [ADO-STAGE-*](../prd-v10-chasm-adoption-moore/SKILL.md) and KPI-*. 2. **Set up weekly cadence** — 3+ customer interviews per week, ongoing. Not "until we feel done." Continuous. 3. **Map opportunities under the outcome** — Each opportunity is a customer pain or need (not a feature). Phrased in customer words. Grouped under the outcome. Sourced from interviews. 4. **Pick top opportunity** — Score by outcome-impact × evidence-strength × addressability. Focus on one at a time. 5. **Brainstorm solutions** — Multiple candidate solutions per opportunity. Not "the obvious one." Force divergent options. 6. **Assumption-map the top solution** — What must be true for this solution to work? Three categories: desirability (do they want it?), viability (will it grow our outcome?), feasibility (can we build it?). 7. **Test the riskiest assumption first** — Smallest experiment that disproves the assumption if it's wrong. Update tree.

Example

**Outcome**: "Activated users in beachhead segment grow 20% MoM" (anchored in KPI-103 + ADO-BEACHHEAD-001).

**Opportunities** (from 8 weekly interviews):

• O1: "I don't know what to do first when I sign up" (4 mentions)
• O2: "Integration with [our stack tool] is missing" (3 mentions, all beachhead)
• O3: "Pricing is confusing — I don't know which tier I need" (5 mentions)
• O4: "I'd recommend it but I'm afraid teammates won't get the value" (2 mentions, low confidence)

Pick top: **O3** (highest mentions, blocks revenue conversion, addressable in product).

**Candidate solutions** (force divergence):

• S1: Simplify to 1 tier
• S2: Pricing wizard (3 questions → recommendation)
• S3: Annotated comparison page with "most popular" anchor
• S4: Self-serve trial extended to all features

**Top solution**: S2 (pricing wizard).

**Assumptions** for S2:

• D1 (desirability): Users will engage with a wizard before signing up
• D2: The wizard's recommendation will feel right (no "this isn't me")
• V1 (viability): Self-selected tier through wizard → fewer downgrades
• V2: Doesn't tank conversion overall
• F1 (feasibility): Engineering can ship 3-question wizard in 2 weeks

**Riskiest**: D1 — without engagement, nothing else matters.

**Test for D1**: Add wizard to /pricing for 50% of traffic. Measure engagement rate. Threshold: ≥30% engage = D1 valid. If <15%, drop S2.

What You Get Back

• **Opportunity Solution Tree** in `temp/<epic>_discovery-tree.md` (or harvested to UJ-/CFD- when stable) — Living structured artifact
• **CFD-\* discovery insights** (one per interview) with confidence ≥ 3/5 per the Mom Test discipline
• **CFD-\* opportunity entries** with frequency + evidence + outcome-link
• **CFD-\* assumption-test results** as experiments run
• **PER-\* / ADO-STAGE-\* / ADO-BEACHHEAD-\* updates** when discovery accumulates contradicting evidence

When to Use It

| Trigger | Mode | |---------|------| | Post-launch standard practice | standard (ongoing) | | Pre-chasm crossing research push | deep | | Investigating a specific stalled metric | quick (focused on one outcome) | | New team member onboarding to discovery | standard (with mentorship) | | Outcome target is unclear | **stop** — go fix the outcome definition first |

Consumes

• **ADO-STAGE-\* and ADO-BEACHHEAD-\*** (from prd-v10-chasm-adoption-moore) — Defines the segment to interview
• **KPI-\* outcome targets** (from v0.3 + v0.9) — Anchors the outcome at the top of the tree
• **PER-\* personas** (from v0.4 + v0.9) — Interview pool definition
• **CFD-\* existing evidence** (all prior