home / skills / coowoolf / insighthunt-skills / frontier-of-understanding
This skill helps teams move from outcome targets to learning and understanding frontier, ensuring goals match levers and reduce risk.
npx playbooks add skill coowoolf/insighthunt-skills --skill frontier-of-understandingReview the files below or copy the command above to add this skill to your agents.
---
name: Frontier of Understanding (NCTs)
description: Before setting outcome goals, identify your understanding level. If you don't know the levers, set a learning goal, not a revenue goal. Don't commit to outcomes you can't control.
---
# The Frontier of Understanding (NCTs)
> "If you don't understand how to move a particular metric, then the right goal is to set a goal to increase your understanding not to move that metric." — Ravi Mehta
## What It Is
Instead of blindly focusing on outcomes, teams should identify their **"Frontier of Understanding."** If the levers are unknown, the goal should be "Understanding Risk" (learning); if known, the goal can be "Execution Risk" (doing) or "Strategic Risk" (outcomes).
## When To Use
- Quarterly planning when leadership demands **metric increase**
- Team has **no clear hypothesis** on how to achieve
- Avoiding "throwing spaghetti at the wall"
- Setting realistic, achievable goals
## The Risk Levels
```
┌─────────────────────────────────────────────────────────┐
│ UNDERSTANDING RISK │
│ "We don't know the levers" │
│ → Goal = Insight / Learning │
├─────────────────────────────────────────────────────────┤
│ DEPENDENCY RISK │
│ "We know levers but lack tools/resources" │
│ → Goal = Unblock dependencies │
├─────────────────────────────────────────────────────────┤
│ EXECUTION RISK │
│ "We have the tools" │
│ → Goal = High velocity / Quality experiments │
├─────────────────────────────────────────────────────────┤
│ STRATEGIC RISK │
│ "We are executing well" │
│ → Goal = Verify hypothesis moves the metric │
└─────────────────────────────────────────────────────────┘
```
## How To Apply
```
STEP 1: Identify Your Frontier
└── Do we know what moves this metric?
└── Have we proven the levers work?
STEP 2: Match Goal to Frontier
└── Unknown levers → Learning goal
└── Known levers → Execution goal
└── Proven levers → Outcome goal
STEP 3: Don't Overcommit
└── If in Understanding phase, don't promise revenue
└── Promise insights instead
STEP 4: Move Along the Frontier
└── Each quarter, advance your understanding
└── Eventually you earn the right to set outcome goals
```
## Common Mistakes
❌ Setting an **outcome goal (Revenue)** when in "Understanding Risk" phase
❌ "Throwing spaghetti at the wall" to hit arbitrary targets
❌ Treating all goals as **equally achievable**
## Real-World Example
At Tinder, data showed high spending from a small group. Instead of blindly trying to grow revenue, they set a goal to understand WHY. They found these weren't rich people, but frequent travelers/salespeople. This insight led to "Tinder Platinum."
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*Source: Ravi Mehta, Former CPO of Tinder, Lenny's Podcast*
This skill teaches teams to diagnose their "Frontier of Understanding" before setting outcome goals. It reframes goal-setting: if you don't know the levers that move a metric, set a learning goal; only commit to outcome goals when levers are proven. The approach reduces wasted effort and prevents overcommitment to metrics you can't control.
You assess where your team sits on a risk spectrum: Understanding, Dependency, Execution, or Strategic. Based on that assessment you pick an appropriate goal type—insight, unblocking, execution, or outcome—and align quarterly plans to advance understanding one step at a time. The method emphasizes promising learnings when levers are unknown and reserving revenue or target commitments until levers are validated.
What counts as proof that a lever is known?
Repeated experimental evidence showing the lever moves the target metric reliably, plus instrumentation and a clear causal hypothesis.
How long should a team stay in an Understanding phase?
Long enough to generate reproducible signals—typically 1–3 quarters depending on experiment cadence and signal strength. Move on when you can predictably replicate effects.