Conversion Ops
Optimize marketing funnels and conversion rates with AI-powered analysis.
Installation
- Make sure Claude is on your device and in your terminal.
Skills load from
~/.claude/skills/when Claude Code starts up β so you need it on your machine first. If you don't have it yet, install it once with the command below, then runclaudein any terminal to verify.One-time setupnpm i -g @anthropic-ai/claude-codeAlready have it? Skip ahead.
- Paste into Claude Code or into your terminal.
This copies the whole skill folder into
~/.claude/skills/conversion-ops-ericosiu/β the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.Faster alternative (instruction-only skills)
Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates β they won't be downloaded and the skill will fail when it tries to load them.
Quick install (SKILL.md only)Sign up to copy - Restart Claude Code.
Quit and reopen Claude Code (or any other agent that loads from
~/.claude/skills/). New skills are picked up on startup. - Just ask Claude.
Skills auto-activate when your request matches the skill's description β no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the βWhat this skill doesβ section above.
Prefer to read the source first? Open on GitHub.
When Claude uses it
Claude activates this skill based on the context of your request.
What this skill does
AI Conversion Ops
Preamble (runs on skill start)
# Version check (silent if up to date)
python3 telemetry/version_check.py 2>/dev/null || true
# Telemetry opt-in (first run only, then remembers your choice)
python3 telemetry/telemetry_init.py 2>/dev/null || true
Privacy: This skill logs usage locally to
~/.ai-marketing-skills/analytics/. Remote telemetry is opt-in only. No code, file paths, or repo content is ever collected. Seetelemetry/README.md.
AI-powered conversion rate optimization: landing page audits, CRO scoring, survey segmentation, and lead magnet generation.
When to Use
- User asks for a landing page audit or CRO analysis
- User wants to score a page across conversion dimensions
- User needs to identify conversion bottlenecks on a URL
- User has survey data and wants to segment respondents by pain point
- User wants lead magnet ideas generated from survey responses
- User needs batch CRO analysis across multiple URLs
Tools
CRO Audit (cro_audit.py)
Fetches a landing page and scores it across 8 conversion dimensions. No headless browser needed.
# Single URL audit
python cro_audit.py --url https://example.com/landing-page
# Batch mode β multiple URLs
python cro_audit.py --urls https://example.com/page1 https://example.com/page2
# URLs from a file (one per line)
python cro_audit.py --file urls.txt
# Specify industry for benchmark comparison
python cro_audit.py --url https://example.com --industry saas
# JSON output
python cro_audit.py --url https://example.com --json
# Save report to file
python cro_audit.py --url https://example.com --output report.json
Scoring dimensions (each 0β100):
- Headline Clarity β Is the value prop obvious in <5 seconds?
- CTA Visibility β Are CTAs prominent, contrasting, above the fold?
- Social Proof β Testimonials, logos, case studies, numbers?
- Urgency β Scarcity, deadlines, limited offers?
- Trust Signals β Security badges, guarantees, privacy, certifications?
- Form Friction β How many fields? Is the form intimidating?
- Mobile Responsiveness β Viewport meta, responsive patterns, touch targets?
- Page Speed Indicators β Image optimization, script count, resource size?
Overall CRO Score = Weighted average across all 8 dimensions.
Output includes:
- Per-dimension score with specific findings
- Priority fixes ranked by impact
- Before/after suggestions for each issue
- Industry benchmark comparison
- Overall letter grade (A+ through F)
Supported industries: saas, ecommerce, agency, finance, healthcare, education, b2b, general
Survey-to-Lead-Magnet Engine (survey_lead_magnet.py)
Ingests survey CSV data, clusters respondents by pain point, and generates lead magnet briefs for each segment.
# Basic usage β analyze survey CSV
python survey_lead_magnet.py --csv survey_responses.csv
# Specify which columns contain pain points / challenges
python survey_lead_magnet.py --csv survey.csv --pain-columns "biggest_challenge" "top_frustration"
# Limit number of segments
python survey_lead_magnet.py --csv survey.csv --top-segments 5
# JSON output
python survey_lead_magnet.py --csv survey.csv --json
# Save output
python survey_lead_magnet.py --csv survey.csv --output lead_magnets.json
What it produces:
- Pain point clusters with respondent counts
- Segments ranked by size and commercial potential
- For each top segment, a lead magnet brief:
- Title, format (guide/checklist/template/calculator), hook
- Content outline (5β7 sections)
- Target CTA and distribution channel
- Viral potential score + conversion potential score
- Prioritized implementation roadmap
CSV format: Questions as column headers, one respondent per row. Works with any survey tool export (Typeform, Google Forms, SurveyMonkey, etc.)
Configuration
No API keys required. Both tools work with local analysis only.
Optional environment variables:
| Variable | Required | Description |
|---|---|---|
USER_AGENT | No | Custom user agent for page fetching (default provided) |
REQUEST_TIMEOUT | No | HTTP timeout in seconds (default: 15) |
Recommended Workflow
- Weekly: Run
cro_audit.pyon your top landing pages to track CRO scores over time - Post-survey: Run
survey_lead_magnet.pyto turn survey data into content strategy - Pre-launch: Audit new landing pages before driving paid traffic
- Monthly: Batch audit competitor landing pages to benchmark against
Dependencies
pip install -r requirements.txt
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