smithery/coowoolf

shortening-feedback-loops

Use when decisions seem to require years to evaluate, when hiding behind long-term vision to avoid accountability, or when struggling to learn from venture/product bets in real-time

Installation

$ npx skills add smithery/coowoolf --skill shortening-feedback-loops

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Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Package contents

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  • skill md SKILL.md 3,079 B
  • docs SUMMARY.md 214 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Shortening the Feedback Loop

Overview

A mental model to debunk the idea that some decisions require years to evaluate. It involves identifying short-term proxies that correlate with long-term success.

Core principle: There is no such thing as a long feedback loop—it's a choice to wait.

The Framework

┌─────────────────────────────────────────────────────────────────┐
│                                                                  │
│   ULTIMATE OUTCOME (e.g., IPO / $1B Exit)                       │
│                     ▲                                           │
│                     │                                           │
│   ┌─────────────────┴─────────────────┐                         │
│   │   INTERMEDIATE PROXY              │                         │
│   │   (Necessary Condition)           │                         │
│   │   e.g., Series A Funding (18 mo)  │                         │
│   └─────────────────┬─────────────────┘                         │
│                     │                                           │
│   ┌─────────────────┴─────────────────┐                         │
│   │   SHORT-TERM SIGNAL               │                         │
│   │   (Correlated Metric)             │                         │
│   │   e.g., Net New ARR, Retention    │                         │
│   └───────────────────────────────────┘                         │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Key Principles

Principle Description
Necessary conditions Find what MUST happen for ultimate success
Correlated signals Look for metrics that predict the outcome
Proactive measurement Design experiments to surface signals early
Trade safety for learning Knowing early is better than comfortable ignorance

Common Mistakes

  • Waiting for final exit/outcome to judge decision quality
  • Ignoring interim signals like funding or retention
  • Hiding behind "it's too early to tell"

Source: Annie Duke (First Round Capital) via Lenny's Podcast