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Prd V03 Outcome Definition

Community
mattgierhart
prd-v03-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.

Overview

Publishermattgierhart
RepositoryPRD-driven-context-engineering
Skill nameprd-v03-outcome-definition
Stars
179
Forks
11
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mattgierhart on GitHub. Read the source before you install it.

Installation

Install the Prd V03 Outcome Definition AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mattgierhart/PRD-driven-context-engineering.git /tmp/PRD-driven-context-engineering
mkdir -p .claude/skills
cp -r /tmp/PRD-driven-context-engineering/plugins/prd-ce/skills/prd-v03-outcome-definition .claude/skills/prd-v03-outcome-definition
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Prd V03 Outcome Definition in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Prd V03 Outcome Definition on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Prd V03 Outcome Definition is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

markdown
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:

TierMetric TypesWhy It Matters
Tier 1Revenue (MRR, first dollar, ACV), Churn (logo, NRR), LTV:CACRevenue validates market fit. "First dollar IS the proof."
Tier 2Conversion rates (trial→paid, lead→customer), Time to Value, ActivationLeading indicators that predict Tier 1 outcomes
Tier 3Engagement (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 TypePrimary MetricsAnti-Metrics (Avoid)
CloneFeature parity score, Price delta vs. leader, TTFV vs. leaderGeneric engagement (doesn't prove you beat leader)
UndercutPrice per [unit] vs. leader, Niche conversion rate, CAC in target segmentBroad market share (you're niche by design)
UnbundleCategory NPS vs. platform, Vertical retention, Feature depth usagePlatform-level metrics (irrelevant to your slice)
SliceMarketplace ranking, Install→activate rate, Platform retention liftTAM metrics (platform owns the market)
WrapperTime saved per workflow, API reliability, Integration adoptionStandalone usage (value is in connection)
InnovationEducation→activation conversion, Behavioral change rate, Reference customersUser 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:

ConsumerWhat It UsesExample
v0.5 Red TeamKill thresholds"If KPI-001 not hit by Day 21, pivot"
v0.7 Build ExecutionEPIC acceptance criteria"EPIC complete when KPI-002 validated"
v0.9 GTMLaunch dashboardTrack KPI-001, KPI-003 post-launch
BR- Business RulesDerived 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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Prd V03 Outcome Definition AI skill do?

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.

Why use Prd V03 Outcome Definition on TypingMind?

Because you install it once and use it with any model. Prd V03 Outcome Definition is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Prd V03 Outcome Definition in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mattgierhart/PRD-driven-context-engineering/tree/main/plugins/prd-ce/skills/prd-v03-outcome-definition. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Prd V03 Outcome Definition?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Prd V03 Outcome Definition?

As many as you like. As long as a model supports skills, you can use Prd V03 Outcome Definition with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Prd V03 Outcome Definition AI skill free?

Yes. It is published on GitHub by mattgierhart under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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