Agent Skills: Outcome Definition

Define measurable success metrics (KPIs) tied to product type during PRD v0.3 Commercial Model. Triggers on requests to define success metrics, set KPI targets, determine what to measure, establish go/no-go thresholds, or when user asks "how do we measure success?", "what metrics matter?", "what's our target?", "how do we know if this works?", "define KPIs", "success criteria". Consumes Product Type Classification (BR-) from v0.2. Outputs KPI- entries with thresholds, evidence sources, and downstream gate linkages.

UncategorizedID: mattgierhart/PRD-driven-context-engineering/prd-v03-outcome-definition

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Skill Metadata

Name
prd-v03-outcome-definition
Description
Define measurable success metrics (KPIs) tied to product type during PRD v0.3 Commercial Model. Triggers on requests to define success metrics, set KPI targets, determine what to measure, establish go/no-go thresholds, or when user asks "how do we measure success?", "what metrics matter?", "what's our target?", "how do we know if this works?", "define KPIs", "success criteria". Consumes Product Type Classification (BR-) from v0.2. Outputs KPI- entries with thresholds, evidence sources, and downstream gate linkages.

Outcome Definition

Position in HORIZON workflow: v0.2 Product Type Classification → v0.3 Outcome Definition → v0.3 Pricing Model Selection

Consumes

This skill requires prior work from v0.2:

  • BR-* product type entry (from Product Type Classification) — Classification determines which metrics are relevant
  • CFD-* entries (from Problem Framing and Competitive Landscape) — Customer evidence about desired outcomes
  • Market benchmarks and competitor metrics — Reference data for Tier 1/2 targets

This skill assumes v0.2 classification is complete.

Produces

This skill creates/updates:

  • KPI-* entries (outcome definitions) — Measurable success metrics tied to product type
  • BR-* outcome rules (optional) — Constraints derived from KPI thresholds (e.g., "Launch blocked if LTV:CAC < 3:1")
  • Success criteria artifact — Dashboard of leading + lagging indicators that define product-market fit

All KPI entries should include:

  • confidence: 2-3/5 (based on benchmark evidence, not just assumptions)
  • Evidence source (competitor benchmarks, CFD validation, industry reports)
  • Forward target: "Would move to 4/5 if we observe real customer data"

Example KPI entry with confidence:

KPI-001: Time to First Revenue

Type: Tier 1 (Revenue)
Category: Lagging
Definition: Days from market signal identification to first paying customer
Target: ≤14 days
Confidence: 2/5 (source: GearHeart-methodology + 0-customer-validation)
Evidence: BR-001 (GearHeart standard); No pre-customer validation yet
Next Target: "Would move to 4/5 if actual customer reaches paying status in ≤14 days"
Downstream Gate: v0.5 Red Team — if not hit by Day 21, evaluate pivot

---

KPI-002: Conversion Rate (Trial → Paid)

Type: Tier 2 (Leading Indicator)
Category: Leading
Definition: (Paid customers / Trial signups) × 100, measured over 60-day trial period
Target: ≥15% (benchmark: SaaS median 10-15%)
Confidence: 3/5 (source: SaaS-benchmarks + 1-SMB-validation-conversation)
Evidence: CFD-042 (competitive landscape shows SMB conversion patterns)
Next Target: "Would move to 4/5 if we see actual cohort conversion in our product"
Downstream Gate: v0.7 Build Execution — EPIC complete when KPI-002 validated

Metric Quality Hierarchy

Not all metrics are equal. Use this tier system:

| Tier | Metric Types | Why It Matters | |------|--------------|----------------| | Tier 1 | Revenue (MRR, first dollar, ACV), Churn (logo, NRR), LTV:CAC | Revenue validates market fit. "First dollar IS the proof." | | Tier 2 | Conversion rates (trial→paid, lead→customer), Time to Value, Activation | Leading indicators that predict Tier 1 outcomes | | Tier 3 | Engagement (DAU, sessions), Feature adoption, NPS | "Nice to know" — only track if tied to Tier 1/2 |

Rule: Every product needs at least one Tier 1 metric. Tier 3 metrics without Tier 1/2 correlation are vanity metrics.

Product Type × Metric Selection

Metrics must align with product type from v0.2 classification:

| Product Type | Primary Metrics | Anti-Metrics (Avoid) | |--------------|-----------------|----------------------| | Clone | Feature parity score, Price delta vs. leader, TTFV vs. leader | Generic engagement (doesn't prove you beat leader) | | Undercut | Price per [unit] vs. leader, Niche conversion rate, CAC in target segment | Broad market share (you're niche by design) | | Unbundle | Category NPS vs. platform, Vertical retention, Feature depth usage | Platform-level metrics (irrelevant to your slice) | | Slice | Marketplace ranking, Install→activate rate, Platform retention lift | TAM metrics (platform owns the market) | | Wrapper | Time saved per workflow, API reliability, Integration adoption | Standalone usage (value is in connection) | | Innovation | Education→activation conversion, Behavioral change rate, Reference customers | User counts without activation (people try, don't convert) |

Leading vs. Lagging Framework

Every product needs BOTH:

Leading Indicators (actionable now, predict outcomes):

  • Sequences sent, open rates, trial starts
  • Time to first value, activation rate
  • Feature adoption in first 7 days

Lagging Indicators (confirm strategy worked):

  • MRR, churn rate, LTV:CAC
  • Net Revenue Retention (NRR)
  • Customer count, logo churn

Pattern: Track leading weekly, lagging monthly. If leading indicators fail, you can pivot before lagging indicators confirm disaster.

Target-Setting Rules

Targets must be evidence-based, never arbitrary:

Good targets (use these approaches):

  • Competitor benchmark × safety margin: "SMB churn benchmark 3-5% → use 5%"
  • Revenue gates: "First dollar by Day 14" (Signal → $1: 14 days)
  • Ratio thresholds: "LTV:CAC ≥ 3:1"
  • Time bounds: "TTFV < 5 minutes for self-serve"

Bad targets (anti-patterns):

  • Round numbers without evidence: "10% improvement"
  • Engagement without revenue tie: "1000 DAU"
  • Aspirational without baseline: "Best in class retention"

Output Template

Create KPI- entries in this format:

KPI-XXX: [Metric Name]
Type: [Tier 1 | Tier 2 | Tier 3]
Category: [Leading | Lagging]
Definition: [Exact calculation formula]
Target: [Specific threshold with evidence source]
Evidence: [CFD-XXX or benchmark source]
Downstream Gate: [Which decision uses this — e.g., "v0.5 Red Team kill criteria"]
Measurement: [How/when measured — e.g., "Weekly via Mixpanel"]

Example KPI- entry:

KPI-001: Time to First Revenue
Type: Tier 1
Category: Lagging
Definition: Days from market signal identification to first paying customer
Target: ≤14 days (GearHeart standard: Signal → $1: 14 days)
Evidence: BR-001 (GearHeart methodology)
Downstream Gate: v0.5 Red Team — if not hit by Day 21, evaluate pivot
Measurement: Manual tracking in PRD changelog

Anti-Patterns to Avoid

  1. Vanity metrics as primary: "50K users" means nothing if only 500 pay
  2. Traffic without quality: High volume + low engagement = quality problem
  3. Arbitrary targets: "10% improvement" without baseline or benchmark
  4. All lagging, no leading: Can't course-correct if you only see outcomes monthly
  5. Ignoring product type: Clone metrics ≠ Innovation metrics
  6. Unmeasurable outcomes: "Better experience" — how do you know?

Downstream Connections

KPI- entries feed into:

| Consumer | What It Uses | Example | |----------|--------------|---------| | v0.5 Red Team | Kill thresholds | "If KPI-001 not hit by Day 21, pivot" | | v0.7 Build Execution | EPIC acceptance criteria | "EPIC complete when KPI-002 validated" | | v0.9 GTM | Launch dashboard | Track KPI-001, KPI-003 post-launch | | BR- Business Rules | Derived constraints | "BR-XXX: No launch if LTV:CAC <3:1" |

Detailed References

  • Good/bad examples: See references/examples.md
  • Benchmark sources: See references/benchmarks.md
  • KPI template worksheet: See assets/kpi.md