Conversion Audit
Skill5 starsChanged 3 months agoMITView on GitHub
Score landing pages across 8 CRO dimensions from a URL — plus survey-to-lead-magnet pipeline from CSV data
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The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 75
Check viewport meta tag, responsive patterns in the HTML
- Distinctive: line 79
Check: image count/sizes, script count, inline CSS vs external, lazy loading
- Distinctive: line 177
Always fetch the actual page — don't score from description alone
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
<!-- openlabor-connector: research, api_key={{INTERNAL_API_KEY}}, employee={{EMPLOYEE_ID}}, base={{API_BASE_URL}} -->Overview
Two tools in one skill. Tool 1: give it a URL, get an 8-dimension CRO score. Tool 2: give it survey CSV data, get lead magnet strategies per segment. Zero external API cost — uses JINA_READ to fetch pages.
Tool 1: CRO Page Audit
Step 1: Fetch the Page
use research JINA_READ '{"url":"TARGET_URL"}'This returns the page content as clean markdown. Parse it for: headlines, CTAs, form fields, testimonials, trust signals, images, meta tags.
Step 2: Score 8 Dimensions (each 0-100)
1. Headline Clarity (0-100)
- 90-100: Value prop obvious in <5 seconds, specific benefit, customer language
- 70-89: Clear but generic, could be any company
- 50-69: Vague or feature-focused instead of benefit-focused
- 0-49: Missing, confusing, or purely clever (no meaning)
2. CTA Visibility (0-100)
- 90-100: Above fold, contrasting color, specific text ("Start Free Trial"), low-commitment
- 70-89: Present but generic ("Sign Up", "Learn More")
- 50-69: Below fold only, or blends with design
- 0-49: Missing or buried
3. Social Proof (0-100)
- 90-100: Named testimonials with photos + results, recognizable logos, specific numbers ("14,000 teams")
- 70-89: Testimonials present but generic ("Great product!")
- 50-69: Logos only, or vague claims ("Trusted by thousands")
- 0-49: No social proof at all
4. Urgency/Scarcity (0-100)
- 90-100: Genuine scarcity with deadline or limited availability
- 70-89: Soft urgency ("Start today", "Don't miss out")
- 50-69: No urgency but has clear next step
- 0-49: No reason to act now
5. Trust Signals (0-100)
- 90-100: Money-back guarantee + security badges + privacy policy + recognizable certifications
- 70-89: Some trust elements present
- 50-69: Only basic footer links
- 0-49: No trust indicators
6. Form Friction (0-100)
- 90-100: 1-3 fields, no credit card, instant value
- 70-89: 4-5 fields, reasonable
- 50-69: 6+ fields or requires credit card upfront
- 0-49: Long forms, mandatory phone, complex multi-step
7. Mobile Responsiveness (0-100)
- Check viewport meta tag, responsive patterns in the HTML
- Score based on: readable font sizes, tap targets, no horizontal scroll indicators
8. Page Speed Indicators (0-100)
- Check: image count/sizes, script count, inline CSS vs external, lazy loading
- Score based on resource weight signals in the HTML
Overall CRO Score = weighted average:
- Headline Clarity: 20%
- CTA Visibility: 20%
- Social Proof: 15%
- Trust Signals: 15%
- Form Friction: 10%
- Urgency: 10%
- Mobile: 5%
- Speed: 5%
Letter grades: A+ (95-100), A (90-94), B+ (85-89), B (80-84), C+ (75-79), C (70-74), D (60-69), F (<60)
Step 3: Priority Fixes
For each dimension scoring below 70, generate:
- Issue: what's wrong specifically
- Fix: concrete copy/design change
- Impact: estimated lift (Low/Medium/High)
- Effort: implementation difficulty (Low/Medium/High)
Rank fixes by Impact/Effort ratio — highest impact, lowest effort first.
Step 4: Industry Benchmarks
Compare scores against industry averages:
| Industry | Avg CRO Score | Top Quartile |
| SaaS | 62 | 78+ |
| Ecommerce | 58 | 74+ |
| Agency | 55 | 72+ |
| B2B | 52 | 68+ |
| Finance | 60 | 76+ |
| Healthcare | 48 | 64+ |
Step 5: Output Format
## CRO Audit: [URL]
**Overall Score: [XX]/100 — Grade: [Letter]**
**Industry: [detected or specified] — Benchmark: [avg] — Your position: [above/below/at]**
### Dimension Scores
| Dimension | Score | Status | Key Finding |
(table with all 8 dimensions, green/yellow/red status)
### Priority Fixes (ranked by impact/effort)
1. [Highest priority fix with specific copy suggestion]
2. ...
3. ...
### What's Working Well
- [Positive findings]
### Competitor Comparison (if URLs provided)
(side-by-side scores)Tool 2: Survey-to-Lead-Magnet
Step 1: Ingest CSV
User provides survey CSV (from Typeform, Google Forms, SurveyMonkey, etc.) Parse: identify pain point columns, demographic columns, response counts.
Step 2: Cluster Pain Points
Group responses by similar themes. For each cluster:
- Cluster name
- Response count (% of total)
- Representative quotes
- Commercial potential (High/Medium/Low based on buying intent signals)
Step 3: Generate Lead Magnets
For each top cluster (max 5), generate:
- Title: specific, benefit-driven
- Format: guide / checklist / template / calculator / quiz (pick best for the pain point)
- Hook: one sentence that would make this segment click
- Outline: 5-7 sections
- CTA: what happens after they download
- Distribution: best channel to reach this segment
- Viral score: 1-10 likelihood of sharing
- Conversion score: 1-10 likelihood of converting to customer
Step 4: Implementation Roadmap
Prioritize lead magnets by: segment size × commercial potential × ease of creation
Guidelines
- Always fetch the actual page — don't score from description alone
- Be brutally honest in scoring — inflated scores help nobody
- Provide SPECIFIC copy suggestions, not generic advice
- Compare to competitors when URLs are provided
- Survey clustering should use actual response text, not just column headers
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