```bash python3 telemetry/version_check.py 2>/dev/null || true
Scanned 9/8/2026
Install to Claude Code
npx -y skills add infometa/workbuddyskills --skill conversion-ops --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: conversion-ops
version: 1.0.1
display_name: "conversion-ops"
display_name_en: "Conversion Ops"
description_zh: "CRO 审计与落地页转化优化,调查问卷转化为引流磁铁"
description_en: "CRO audit, landing page optimization, and survey-to-lead-magnet conversion"
visibility: "public"
---
# AI Conversion Ops
## Preamble (runs on skill start)
```bash
# 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. See `telemetry/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.
```bash
# Single URL audit
python cro_audit.py --url https://example.com/landing-page
# Batch mode — 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 0–100):**
1. **Headline Clarity** — Is the value prop obvious in <5 seconds?
2. **CTA Visibility** — Are CTAs prominent, contrasting, above the fold?
3. **Social Proof** — Testimonials, logos, case studies, numbers?
4. **Urgency** — Scarcity, deadlines, limited offers?
5. **Trust Signals** — Security badges, guarantees, privacy, certifications?
6. **Form Friction** — How many fields? Is the form intimidating?
7. **Mobile Responsiveness** — Viewport meta, responsive patterns, touch targets?
8. **Page Speed Indicators** — 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.
```bash
# Basic usage — 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 (5–7 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
1. **Weekly:** Run `cro_audit.py` on your top landing pages to track CRO scores over time
2. **Post-survey:** Run `survey_lead_magnet.py` to turn survey data into content strategy
3. **Pre-launch:** Audit new landing pages before driving paid traffic
4. **Monthly:** Batch audit competitor landing pages to benchmark against
## Dependencies
```bash
pip install -r requirements.txt
```
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