What is competitor-teardown?

A structured review of a competitor's landing page, the web page designed to turn visitors from an advertisement or campaign into customers or leads.

Fetching and analysing a competitor URL for Google Ads, Meta, or TikTok campaigns, then producing scored findings and account-specific positioning recommendations.

It identifies how the competitor presents its offer, handles objections, builds trust, uses calls to action, and shows pricing, so another account can choose how to differ.

How Pauesome/Paid-Media-MCP configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to Pauesome/Paid-Media-MCP. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/Pauesome/Paid-Media-MCP/main/.claude/skills/competitor-teardown/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/Pauesome/Paid-Media-MCP

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/pauesome/paid-media-mcp/competitor-teardown/github.svg)](https://agentmods.dev/skills/pauesome/paid-media-mcp/competitor-teardown)

Your own site

<a href="https://agentmods.dev/skills/pauesome/paid-media-mcp/competitor-teardown"><img src="https://agentmods.dev/badge/skills/pauesome/paid-media-mcp/competitor-teardown/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/pauesome/paid-media-mcp/competitor-teardown"><img src="https://agentmods.dev/badge/skills/pauesome/paid-media-mcp/competitor-teardown.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 96 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,677 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00096	$0.01677
Opus 5.5	$0.00038	$0.00671
Sonnet 5.5	$0.00019	$0.00335
Haiku 4.5	$0.00010	$0.00168

Measured 2mo ago against content hash 9e0fe41b5b93, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

competitor-teardown scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/competitor-teardown/SKILL.md · 176 lines

How it starts

The opening of the file, as written. The whole thing — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Competitor Teardown — Spain

Fetches and analyses a competitor landing page to surface positioning choices, persuasion tactics, and differentiation opportunities for your account. Cross-channel — applies to Google Ads, Meta, and TikTok landing pages equally.

Required Inputs

client_id (required) — so the recommendation section can contrast the competitor against this account's positioning
competitor_url (required) — fully qualified URL to the competitor landing or home page
page_context (optional) — what role the page plays (Google Ads brand landing, Meta lead-gen LP, homepage, product page). If omitted, inferred from URL structure.

Fail fast on missing URL or client_id.

Account Context

If you maintain notes for the account, extract your account's:

Value proposition and differentiation angles
Sector compliance constraints (what the account CAN'T say — e.g. Special Ad Category restrictions, Ley 16/2011 disclosures for credit products)
Target audience and objections
Existing positioning language

Recommendations should contrast the competitor's positioning with this account's positioning — not produce generic "how to market" advice.

Data Collection

WebFetch the competitor_url — the only data source required. Extract:
- Full visible copy (headline, sub-headline, body, bullets)
- CTA text and placement (above-fold, mid-page, sticky)
- Trust signals (logos, testimonials, case studies, certifications, ratings, counts)
- Pricing (if shown) and pricing framing (monthly vs annual, free trial, money-back)
- Form fields (for lead-gen pages)
- Navigation structure and footer links (to understand product portfolio)
Optional: WebFetch one or two sub-pages if linked from the main page and relevant (e.g. pricing page, product page, about page). Don't cascade; keep fetch count low.

Teardown Framework

1. Value Proposition Decomposition

Question	What to extract
What problem do they solve?	Pain language, problem agitation
Who is their ICP?	Explicit callouts, industry/role signals
What transformation do they promise?	Before/after, outcomes, results
What's their unique mechanism?	Proprietary method, tech, process

Read the full file on GitHub · 176 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 176 lines · 96 tokens per session scan A 9e0fe41b5b93

Subscribe to this mod's changes

competitor-teardown is a skill published in the GitHub repository Pauesome/Paid-Media-MCP (2 stars, last pushed 4mo ago), licensed MIT. It adds 96 tokens to every session and 1,677 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Marketing and SEO

competitor-teardown next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
competitor-teardownPauesome/Paid-Media-MCP	Skill	2	4mo ago	MIT	A	96	Claude Code
imagegenopenai/codex	Skill	133k	yesterday	Apache-2.0	A	113	Codex
playwright-climicrosoft/playwright	Skill	99k	yesterday	Apache-2.0	A	19	Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	4d ago	MIT	A	200	Claude Code, Codex
systematic-debuggingobra/superpowers	Skill	313k	3d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	95	Claude Code
next-partial-prefetching-adoptionvercel/next.js	Skill	143k	yesterday	MIT	A	103	Claude Code

Paid-Media-MCP: Skill for Claude Code