nova: Skill for Claude Code

.claude/skills/competitive-ads-extractor/SKILL.md

competitive-ads-extractor is a skill for Claude Code from djbelieny/nova. It costs 46 tokens per session (1,805 once invoked), scanned A, a copy of competitive-ads-extractor, MIT.

An ad research tool that collects competitors’ advertisements from public ad libraries such as Facebook Ad Library and LinkedIn.

In plain words
What is it for?
Finding campaign inspiration, comparing market positioning, spotting recurring customer problems, and analyzing ad formats and messages.
Why use it?
It reduces the time spent manually studying competitor campaigns and organizing their messages, audiences, and creative ideas.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is djbelieny/nova's own configuration. It tells Claude Code how to work on nova itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything nova configures →

Reuse

Borrowing it

Nothing to install: this file belongs to djbelieny/nova. 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/djbelieny/nova/main/.claude/skills/competitive-ads-extractor/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/djbelieny/nova

Made for: Claude Code.

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README.md
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Per session 46 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,805 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 100% copy Near-identical to another mod 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00046 $0.01805
Opus 5 $0.00023 $0.00903
Sonnet 5 $0.00009 $0.00361
Haiku 4.5 $0.00005 $0.00180

Measured 8d ago against content hash c9bc0f711936, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

competitive-ads-extractor 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 8d 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.

Origin

This is a copy

100% identical to competitive-ads-extractor — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.claude/skills/competitive-ads-extractor/SKILL.md · 294 lines

How it starts

The opening of the file, as written. The whole thing — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Competitive Ads Extractor

This skill extracts your competitors' ads from ad libraries and analyzes what's working—the problems they're highlighting, use cases they're targeting, and copy/creative that's resonating.

When to Use This Skill

  • Researching competitor ad strategies
  • Finding inspiration for your own ads
  • Understanding market positioning
  • Identifying successful ad patterns
  • Analyzing messaging that works
  • Discovering new use cases or pain points
  • Planning ad campaigns with proven concepts

What This Skill Does

  1. Extracts Ads: Scrapes ads from Facebook Ad Library, LinkedIn, etc.
  2. Captures Screenshots: Saves visual copies of all ads
  3. Analyzes Messaging: Identifies problems, use cases, and value props
  4. Categorizes Ads: Groups by theme, audience, or format
  5. Identifies Patterns: Finds common successful approaches
  6. Provides Insights: Explains why certain ads likely perform well

How to Use

Basic Extraction

Extract all current ads from [Competitor Name] on Facebook Ad Library
Scrape ads from [Company] and analyze their messaging

Specific Analysis

Get all ads from [Competitor] focusing on their messaging 
about [specific problem]. What pain points are they highlighting?

Competitive Set

Extract ads from these 5 competitors: [list]. 
Compare their approaches and tell me what's working.

Specific Platform

Get LinkedIn ads from [Competitor] and analyze their 
B2B positioning strategy

Example

User: "Extract ads from Notion on Facebook Ad Library and tell me what messaging is working for them."

Process:

Accessing Facebook Ad Library...
Searching for: Notion
Found: 23 active ads

Extracting screenshots...
[████████████████████] 100%

Saved to: competitor-ads/notion/
- ad-001-collaboration.png
- ad-002-productivity.png
- ad-003-templates.png
...

Analyzing messaging...

Output:

# Notion Ad Analysis

## Overview
- Total Ads: 23 active
- Primary Themes: Productivity (35%), Collaboration (30%), 
  Templates (20%), AI Features (15%)
- Ad Formats: Static images (60%), Video (40%)
- CTA Patterns: "Try for free", "Get started"

## Key Problems They're Highlighting

1. **Scattered Information** (8 ads)
   Copy: "Stop switching between 10 different tools"
   Why it works: Direct pain point many teams face

2. **Meeting Overload** (5 ads)
   Copy: "Replace unnecessary meetings with async updates"
   Why it works: Post-COVID remote work pain point

3. **Lost Documentation** (4 ads)
   Copy: "Never ask 'where is that doc?' again"
   Why it works: Universal workplace frustration

## Successful Creative Patterns

### Pattern 1: Before/After Split
- Shows chaotic tool landscape → Clean Notion workspace
- Used in 6 high-performing ads
- Visual metaphor is immediately clear

### Pattern 2: Feature Showcase
- GIF of actual product usage
- Shows specific feature in 5 seconds
- Used for new features (AI, templates)

### Pattern 3: Social Proof
- "Join 20M users" messaging
- Customer logos
- Used in 4 ads targeting enterprise

## Copy That's Working

Best Headlines:
1. "Your team's knowledge, finally in one place"
   → Benefit-focused, addresses pain directly
   
2. "The all-in-one workspace"
   → Clear positioning, broad appeal
   
3. "AI that actually helps you work"
   → Addresses AI skepticism, practical angle

Best Body Copy Patterns:
- Short sentences (under 10 words)
- Focus on outcomes not features
- Include specific numbers ("Cut meetings by 50%")

## Audience Targeting Insights

Based on ad variations:
- Startup founders: Solo productivity angle
- Team leads: Collaboration and alignment
- Enterprise: Security and compliance mentions
- Students: Free plan, templates, organization

## Recommendations for Your Ads

1. **Test the "tool sprawl" pain point**
   → Strong resonance based on their ad frequency

2. **Use product screenshots over abstract visuals**
   → All their top ads show actual UI

3. **Lead with the problem, not the solution**
   → "Tired of X?" performs better than "Introducing Y"

4. **Keep copy under 100 characters**
   → Their shortest ads seem most frequent

5. **Test before/after visual formats**
   → Proven pattern in their creative

## Files Saved
- All ads: ~/competitor-ads/notion/
- Analysis: ~/competitor-ads/notion/analysis.md
- Best performers: ~/competitor-ads/notion/top-10/

Read the full file on GitHub · 294 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.

  1. 8d ago First seen · 294 lines · 46 tokens per session scan A c9bc0f711936

Subscribe to this mod's changes

competitive-ads-extractor is a skill published in the GitHub repository djbelieny/nova (5 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 1,805 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to competitive-ads-extractor, differing in 0 lines, and is treated as a copy.

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