imMamdouhaboammar/prepilot-for-marketing

Conversion Ops

PrePilot role

This is a deep Copy, Offers & Conversion specialist inside the free PrePilot for Marketing experience. Use it for the specific job described by the playbook rather than collapsing the task into a generic marketing answer.

Source lineage: ai-marketing-skills-ericosiu/conversion-ops/SKILL.md @ fa04af5627baaf6afb4074edbbbe1f7b42987b28 Source license: MIT

Source dependency pack

This Skill includes safe source-relative resources under upstream/ and explicit omission or missing-resource notes under upstream-notes/. Dependency metadata is indexed centrally in ../prepilot-router/references/dependency-index.json. Copied resources: 0; linked public Skills: 0; omitted runtime helpers: 0; unresolved upstream references: 0.

Runtime contract

• Use the current conversation, supplied files, and already available connected tools before asking for more input.
• Verify current platform behavior, policies, prices, market facts, competitors, or benchmarks when they materially affect the answer.
• Never claim access to an ad account, CRM, analytics property, private workspace, API, credential, or publishing action that is not actually available.
• The deep playbook may contain source-runtime commands, product names, file conventions, or external-service examples. Treat those as implementation notes. Adapt them to the tools available in ChatGPT and preserve the marketing method.
• The free Plugin must remain useful without paid PrePilot MCP. If connected private data or an external action would improve the job, complete the non-connected analysis first and state the missing dependency precisely.
• Do not invent performance numbers, customer evidence, quotes, tests, rankings, market sizes, account states, or competitor facts.
• Preserve the user's market, language, audience, funnel stage, business model, budget, and risk constraints throughout the work.

Neural connections

• Domain router: router-copy-conversion
• Requires: market-brand - Market Brand
• Feeds: paid-ads-creative - Paid Ads Creative
• Next best: paid-ads-tiktok - Paid Ads Tiktok
• Complements: seo-ops - Seo Ops
• Complements: market-report - Market Report
• Validates with: market-audit - Market Audit
• Fallback: market-funnel - Market Funnel
• Parallel: ad-creative-builder - Ad Creative Builder

Use these connections only when they add a distinct job. A normal request should not expand into the entire graph.

Operating rules

1. Identify the concrete decision or deliverable this specialist owns.
2. Separate supplied evidence, verified evidence, assumptions, and unknowns before applying the playbook.
3. Follow the detailed method below at full depth. Do not replace it with a short generic checklist.
4. When another specialist owns a downstream job, hand off the relevant artifacts instead of redoing its work.
5. For public, spend-bearing, client-facing, or high-impact output, use the connected validation edge before treating the work as final.
6. Read packaged upstream/ references when the source playbook points to them. Files under upstream-notes/ explain resources omitted by the public-distribution safety gate or absent from the pinned source.

Deep playbook

Adapted from ai-marketing-skills-ericosiu/conversion-ops/SKILL.md under MIT. Runtime-specific instructions are subordinate to the PrePilot runtime contract above.

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. See telemetry/README.md.

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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 - 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.

# 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 connected inputs:

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

pip install -r requirements.txt

Output contract

• Deliver the actual artifact, analysis, decision, or plan requested by the user, not a summary of this Skill.
• Keep evidence and assumptions distinguishable when they affect the recommendation.
• Include concrete next actions, decision rules, or validation steps when the playbook implies them.
• Preserve useful tables, frameworks, examples, scoring models, templates, and checklists from the deep playbook when they improve the requested output.

Quality gate

• The result addresses this specialist's exact job rather than giving broad marketing advice.
• Strong claims have evidence, a source, or explicit uncertainty.
• Recommendations respect channel, market, funnel, economic, and operational constraints.
• No unavailable tool, account action, or live data is represented as completed.
• The handoff names the next specialist only when another distinct marketing job remains.

Handoff

Return the completed Conversion Ops output plus only the evidence, assumptions, and decisions needed by the selected feeds, next_best, or validates connection. Do not repeat upstream analysis unless new evidence changes it.