# Unstuck Scaling

> Use when AI agents frequently hit dead ends, when reliability is the main constraint on scaling utility, or when general model improvements don't solve specific blockers

- Skill: `coowoolf/unstuck-scaling` (Agent Skill)
- Install (CLI): `npx skillmds@latest add coowoolf/unstuck-scaling`
- Raw SKILL.md: https://api.skillmd.com/api/skills/coowoolf/unstuck-scaling/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: Coowoolf (https://skillmd.com/u/coowoolf)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/coowoolf/unstuck-scaling

---


# The Unstuck Scaling Framework

## Overview

A systematic approach to improving AI reliability by treating **"getting stuck"** as the primary bottleneck. Instead of broad improvements, painstakingly identify specific failure modes and create tight feedback loops.

**Core principle:** Address specific bottlenecks, not general intelligence.

## The Cycle

```
┌─────────────────────────────────────────────────────────────────┐
│                                                                  │
│     ┌───────────────────┐                                       │
│     │  IDENTIFY         │                                       │
│     │  'Stuck' Points   │                                       │
│     │  (auth, payments) │                                       │
│     └─────────┬─────────┘                                       │
│               │                                                  │
│               ▼                                                  │
│     ┌───────────────────┐                                       │
│     │  ADDRESS          │                                       │
│     │  Specific         │                                       │
│     │  Bottlenecks      │                                       │
│     └─────────┬─────────┘                                       │
│               │                                                  │
│               ▼                                                  │
│     ┌───────────────────┐                                       │
│     │  QUANTITATIVELY   │                                       │
│     │  Tune System      │                                       │
│     │  (pass/fail rate) │                                       │
│     └─────────┬─────────┘                                       │
│               │                                                  │
│               ▼                                                  │
│     ┌───────────────────┐                                       │
│     │  FAST FEEDBACK    │─────────────────────────┐             │
│     │  Loop             │                         │             │
│     └───────────────────┘                         │             │
│               ▲                                   │             │
│               └───────────────────────────────────┘             │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘
```

## Key Principles

| Principle | Description |
|-----------|-------------|
| **Specific blockers** | Identify exact points where AI fails |
| **Quantitative tuning** | Measure stuck rates, not vibes |
| **Fast feedback** | Rapid iteration on fixes |
| **Bottleneck focus** | Specific roadblocks > general intelligence |

## Common Mistakes

- Focusing on general model improvements
- Failing to measure "stuck" rates quantitatively
- Slow feedback loops preventing rapid iteration

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*Source: Anton Osika (Lovable, GPT Engineer) via Lenny's Podcast*