Ad Angle Miner

Dig through customer voice data — reviews, Reddit, support tickets, competitor ads — to extract the specific language, pain points, and outcome desires that make ads convert. The output is an angle bank your team can pull from for any campaign.

Core principle: The best ad angles aren't invented in a brainstorm. They're extracted from what real people are already saying. This skill finds those angles and ranks them by strength of evidence.

When to Use

"What angles should we run in our ads?"
"Find pain points we can use in ad copy"
"What are people complaining about with [competitors]?"
"Mine reviews for ad messaging"
"I need fresh ad angles — not the same tired stuff"

Phase 0: Intake

Your product — Name + what it does in one sentence
Competitors — 2-5 competitor names (for review mining)
ICP — Who are you targeting? (role, company stage, pain)
Data sources to mine (pick all that apply):
- G2/Capterra/Trustpilot reviews (yours + competitors)
- Reddit threads in relevant subreddits
- Twitter/X complaints or praise
- Support tickets or NPS comments (paste or file)
- Competitor ads (Meta + Google)
Any angles you've already tested? — So we can skip those

Phase 1: Source Collection

1A: Review Mining

Run

review-scraper

for your product and each competitor:

bash

python3 skills/review-scraper/scripts/scrape_reviews.py \
  --product "<product_name>" \
  --platforms g2,capterra \
  --output json

Focus on:

1-2 star reviews of competitors — Pain they're failing to solve
4-5 star reviews of you — Outcomes that delight buyers
4-5 star reviews of competitors — Strengths you need to counter or match
Review language patterns — Exact phrases buyers use

1B: Reddit/Community Mining

Run

reddit-scraper

for relevant subreddits:

bash

python3 skills/reddit-scraper/scripts/scrape_reddit.py \
  --query "<product category> OR <competitor> OR <pain keyword>" \
  --subreddits "<relevant_subreddits>" \
  --sort relevance \
  --time month \
  --limit 50

Extract:

Questions people ask before buying
Complaints about current solutions
"I wish [product] would..." statements
Comparison threads (vs discussions)

1C: Twitter/X Mining

Run

twitter-scraper

bash

python3 skills/twitter-scraper/scripts/scrape_twitter.py \
  --query "<competitor> (frustrating OR broken OR hate OR love OR switched)" \
  --max-results 50

1D: Competitor Ad Mining (Optional)

Run

ad-creative-intelligence

to see what angles competitors are currently using. This reveals:

Angles they've validated (long-running ads = working)
Angles they're testing (new ads)
Angles nobody is running (white space)

1E: Internal Data (Optional)

If the user provides support tickets, NPS comments, or sales call transcripts — ingest and tag with the same framework below.

Phase 2: Angle Extraction

Process all collected data through this extraction framework:

Angle Categories

Category	What to Look For	Ad Power
Pain angles	Specific frustrations with status quo or competitors	High — pain motivates action
Outcome angles	Desired results buyers describe in their own words	High — positive aspiration
Identity angles	How buyers describe themselves or want to be seen	Medium — emotional resonance
Fear angles	Risks of NOT switching or acting	Medium — loss aversion
Competitive displacement	Specific reasons people switched from a competitor	Very high — direct comparison
Social proof angles	Outcomes or metrics buyers cite in reviews	High — credibility
Contrast angles	Before/after or old way/new way framings	High — clear value prop

For Each Angle, Extract:

The angle — One-sentence framing
Proof quotes — 2-5 verbatim quotes from sources
Source count — How many independent sources mention this?
Competitor weakness? — Does this exploit a specific competitor's gap?
Emotional register — Frustration / Aspiration / Fear / Relief / Pride
Recommended format — Search ad / Meta static / Meta video / LinkedIn / Twitter

Phase 3: Scoring & Ranking

Score each angle on:

Factor	Weight	Description
Evidence strength	30%	Number of independent sources mentioning it
Emotional intensity	25%	How strongly people feel about this (language intensity)
Competitive differentiation	20%	Does this set you apart, or could any competitor claim it?
ICP relevance	15%	How closely does this match the target buyer's world?
Freshness	10%	Is this angle already overused in competitor ads?

Total score out of 100. Rank all angles.

Phase 4: Output Format

markdown

# Ad Angle Bank — [Product Name] — [DATE]

Sources mined: [list]
Total angles extracted: [N]
Top-tier angles (score 70+): [N]

---

## Tier 1: Highest-Conviction Angles (Score 70+)

### Angle 1: [One-sentence angle]
- **Category:** [Pain / Outcome / Identity / Fear / Displacement / Proof / Contrast]
- **Score:** [X/100]
- **Emotional register:** [Frustration / Aspiration / etc.]
- **Proof quotes:**
  > "[Verbatim quote 1]" — [Source: G2 review / Reddit / etc.]
  > "[Verbatim quote 2]" — [Source]
  > "[Verbatim quote 3]" — [Source]
- **Source count:** [N] independent mentions
- **Competitor weakness exploited:** [Competitor name + specific gap, or "N/A"]
- **Recommended formats:** [Search ad headline / Meta static / Video hook / etc.]
- **Sample headline:** "[Draft headline using this angle]"
- **Sample body copy:** "[Draft 1-2 sentence body]"

### Angle 2: ...

---

## Tier 2: Worth Testing (Score 50-69)

[Same format, briefer]

---

## Tier 3: Emerging / Low-Evidence (Score < 50)

[Brief list — angles with potential but insufficient evidence]

---

## Competitive Angle Map

| Angle | Your Product | [Comp A] | [Comp B] | [Comp C] |
|-------|-------------|----------|----------|----------|
| [Angle 1] | Can claim ✓ | Weak here ✗ | Also claims | Not relevant |
| [Angle 2] | Strong ✓ | Strong | Weak ✗ | Not relevant |
...

---

## Recommended Test Plan

### Week 1-2: Test Tier 1 Angles
- [Angle] → [Format] → [Platform]
- [Angle] → [Format] → [Platform]

### Week 3-4: Test Tier 2 Angles
- [Angle] → [Format] → [Platform]

Save to

clients/<client-name>/ads/angle-bank-[YYYY-MM-DD].md

Cost

Component	Cost
Review scraper (per product)	~$0.10-0.30 (Apify)
Reddit scraper	~$0.05-0.10 (Apify)
Twitter scraper	~$0.10-0.20 (Apify)
Ad scraper (optional)	~$0.40-1.00 (Apify)
Analysis	Free (LLM reasoning)
Total	~$0.25-1.60

Tools Required

Apify API token —
```
APIFY_API_TOKEN
```
env var

Upstream skills:

review-scraper

reddit-scraper

twitter-scraper

Optional:
```
ad-creative-intelligence
```
(for competitor ad angles)

Trigger Phrases

"Mine ad angles from reviews"
"What angles should we run?"
"Find pain language for our ads"
"Build an ad angle bank for [client]"
"What are people complaining about with [competitor]?"

ad-angle-miner

NPX Install

Tags

SKILL.md Content

Ad Angle Miner

When to Use

Phase 0: Intake

Phase 1: Source Collection

1A: Review Mining

1B: Reddit/Community Mining

1C: Twitter/X Mining

1D: Competitor Ad Mining (Optional)

1E: Internal Data (Optional)

Phase 2: Angle Extraction

Angle Categories

For Each Angle, Extract:

Phase 3: Scoring & Ranking

Phase 4: Output Format

Cost

Tools Required

Trigger Phrases