Competitor teardown

Skill saurabhshuklagrowisto/saurabh-ai-systems/claude-skills/competitor-teardown

Turns a messy competitive landscape into a scored positioning matrix and a recommended wedge. Takes your brand plus 2-5 competitors and a set of buyer-decision dimensions, each scored 0-5 per player, and returns where you win, where you lose, the category whitespace nobody owns, and the sharpest wedge to position on. Use before writing positioning or messaging, before a launch, or when a rep keeps losing deals to the same competitor and you need to know why.From its SKILL.md

Install

npx -y skills add saurabhshuklagrowisto/saurabh-ai-systems --skill competitor-teardown

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

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SKILL.md

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Competitor Teardown Skill

Most competitive analysis is a wall of prose nobody acts on. This skill forces the landscape into a scored matrix and then answers the only three questions that matter: where do we win, where do we lose, and what is the wedge no one else can copy.

When to use

Before writing positioning or homepage messaging.
Before a launch, to pick the angle competitors cannot easily counter.
When deals keep slipping to one competitor and you need to see the pattern.

When NOT to use

For feature checklists to hand to product — this is about buyer-decision positioning, not a spec sheet.
With only your own opinion as input — score dimensions from real buyer evidence (reviews, sales-call notes, win/loss), not gut feel.

Method

Pick the dimensions a buyer actually decides on (not every feature — the 5-8 that move a deal).
Score every player 0-5 on each dimension, from evidence.
The engine returns:
- Wins — dimensions where you lead.
- Losses — dimensions where a competitor leads you.
- Whitespace — dimensions the whole category scores low on (own it before anyone does).
- Wedge — the single dimension where you are strong and the field is weak: your sharpest positioning.

Inputs

{
  "brand": "YourBrand",
  "competitors": ["Rival A", "Rival B"],
  "dimensions": [
    {"name": "Time to value", "weight": 3, "scores": {"YourBrand": 5, "Rival A": 2, "Rival B": 3}}
  ]
}

weight (1-3) reflects how much the dimension moves a buyer.

Output (JSON)

ranking[] (weighted total per player), wins[], losses[], whitespace[], and a single recommended wedge with the reason.

Run it

python scripts/teardown_score.py          # built-in sample
python scripts/teardown_score.py in.json  # your own landscape

Zero dependencies, no API keys. Deterministic scoring in scripts/teardown_score.py.

What ships with it: 4 files

8.9 KB alongside SKILL.md, 1 of them executable

scripts/

teardown_score.pyruns4.4 KB