Super Marketing Pro
by @wd041216-bit
Full-stack B2B marketing execution skill equivalent to a 10-person agency team. Use for: building ICP and brand messaging, generating multi-platform content...
clawhub install super-marketing-pro๐ About This Skill
name: super-marketing-pro description: > Full-stack B2B marketing execution skill equivalent to a 10-person agency team. Use for: building ICP and brand messaging, generating multi-platform content matrices, writing cold email sequences, SEO topic cluster strategy, competitor battle cards, content repurposing (1 long-form โ LinkedIn/X/TikTok/Xiaohongshu), and monthly/quarterly marketing reports. Covers Chinese platforms (ๆ้ณ, ๅฐ็บขไนฆ, ๅพฎไฟก) and Western platforms (LinkedIn, YouTube, Instagram, X). Triggers on: marketing strategy, social media content, SEO analysis, competitor research, email sequence, content calendar, hashtag, ICP, ่ฅ้็ญ็ฅ, ็คพๅชๅ ๅฎน, ็ซๅๅๆ, ๅ ๅฎนๆฅๅ, ้ฎไปถๅบๅ.
Super Marketing Pro
Full-stack B2B marketing skill. Always run strategy_builder.py first to define ICP before generating any content.
Golden Rule: Strategy First
Never generate copy without an ICP. The workflow is always: ICP โ Content โ Repurpose โ Distribute โ Monitor โ Report
Scripts
All scripts are in scripts/. Run with python3. Requires openai package (pip3 install openai).
| Script | Function | Key Args |
|--------|----------|----------|
| strategy_builder.py | Generate ICP, messaging framework, elevator pitch | --industry --product |
| content_repurposer.py | 1 long-form doc โ multi-platform content matrix | --source --platforms |
| hashtag_generator.py | Platform-specific hashtag matrix | --content --platforms "linkedin,douyin,xiaohongshu" |
| content_calendar.py | Weekly/monthly publishing schedule | --months --output |
| email_sequence_generator.py | 5-stage cold email sequence | --target --stages |
| seo_analyzer.py | LLM-powered Topic Cluster strategy | --seed-keyword --depth deep |
| competitor_monitor.py | Batch competitor battle cards | --batch-list --export-format json |
| data_reporter.py | Multi-month cross-platform ROI report | --type monthly --months 1,2,3 |
| llm_utils.py | Shared LLM utility (auto-imported) | โ |
llm_utils.py uses OPENAI_API_KEY env var. Default model: gemini-3.0-flash. Includes exponential backoff retry (3 attempts).
Execution Workflow
Stage 1 โ Strategy: Run strategy_builder.py โ get ICP, buyer personas, messaging pillars.
Stage 2 โ Content Creation: Based on goal:
seo_analyzer.py โ write long-form pillar content.references/content_templates.md โ write whitepaper or case study.email_sequence_generator.py.Stage 3 โ Repurpose: Run content_repurposer.py on any long-form asset โ get LinkedIn post, X thread, TikTok/Douyin script, Xiaohongshu note.
Stage 4 โ Distribute: Run hashtag_generator.py for tags, then content_calendar.py for scheduling.
Stage 5 โ Convert: Run email_sequence_generator.py for lead nurturing sequences.
Stage 6 โ Monitor & Report: Run competitor_monitor.py for battle cards, data_reporter.py for attribution reports.
Knowledge Base (References)
Load the relevant reference file before executing platform-specific tasks:
Strategy: abm_framework.md (ABM + sales alignment), messaging_icp_guide.md (ICP workshop), funnel_strategy.md (TOFU/MOFU/BOFU + attribution)
Channels: linkedin_guide.md, youtube_seo.md, douyin_algorithm.md, xiaohongshu_tips.md
Content: content_templates.md (whitepapers, case studies, emails), keyword_library.md (B2B keyword matrix)