* feat: Skill Authoring Standard + Marketing Expansion plans
SKILL-AUTHORING-STANDARD.md — the DNA of every skill in this repo:
10 universal patterns codified from C-Suite innovations + Corey Haines' marketingskills patterns:
1. Context-First: check domain context, ask only for gaps
2. Practitioner Voice: expert persona, goal-oriented, not textbook
3. Multi-Mode Workflows: build from scratch / optimize existing / situation-specific
4. Related Skills Navigation: when to use, when NOT to, bidirectional
5. Reference Separation: SKILL.md lean (≤10KB), refs deep
6. Proactive Triggers: surface issues without being asked
7. Output Artifacts: request → specific deliverable mapping
8. Quality Loop: self-verify, confidence tagging
9. Communication Standard: bottom line first, structured output
10. Python Tools: stdlib-only, CLI-first, JSON output, sample data
Marketing expansion plans for 40-skill marketing division build.
* feat: marketing foundation — context + ops router + authoring standard
marketing-context/: Foundation skill every marketing skill reads first
- SKILL.md: 3 modes (auto-draft, guided interview, update)
- templates/marketing-context-template.md: 14 sections covering
product, audience, personas, pain points, competitive landscape,
differentiation, objections, switching dynamics, customer language
(verbatim), brand voice, style guide, proof points, SEO context, goals
- scripts/context_validator.py: Scores completeness 0-100, section-by-section
marketing-ops/: Central router for 40-skill marketing ecosystem
- Full routing matrix: 7 pods + cross-domain routing to 6 skills in
business-growth, product-team, engineering-team, c-level-advisor
- Campaign orchestration sequences (launch, content, CRO sprint)
- Quality gate matching C-Suite standard
- scripts/campaign_tracker.py: Campaign status tracking with progress,
overdue detection, pod coverage, blocker identification
SKILL-AUTHORING-STANDARD.md: Universal DNA for all skills
- 10 patterns: context-first, practitioner voice, multi-mode workflows,
related skills navigation, reference separation, proactive triggers,
output artifacts, quality loop, communication standard, python tools
- Quality checklist for skill completion verification
- Domain context file mapping for all 5 domains
* feat: import 20 workspace marketing skills + standard sections
Imported 20 marketing skills from OpenClaw workspace into repo:
Content Pod (5):
content-strategy, copywriting, copy-editing, social-content, marketing-ideas
SEO Pod (2):
seo-audit (+ references enriched by subagent), programmatic-seo (+ refs)
CRO Pod (5):
page-cro, form-cro, signup-flow-cro, onboarding-cro, popup-cro, paywall-upgrade-cro
Channels Pod (2):
email-sequence, paid-ads
Growth + Intel + GTM (5):
ab-test-setup, competitor-alternatives, marketing-psychology, launch-strategy, brand-guidelines
All 29 skills now have standard sections per SKILL-AUTHORING-STANDARD.md:
✅ Proactive Triggers (4-5 per skill)
✅ Output Artifacts table
✅ Communication standard reference
✅ Related Skills with WHEN/NOT disambiguation
Subagents enriched 8 skills with additional reference docs:
seo-audit, programmatic-seo, page-cro, form-cro,
onboarding-cro, popup-cro, paywall-upgrade-cro, email-sequence
43 files, 10,566 lines added.
* feat: build 13 new marketing skills + social-media-manager upgrade
All skills are 100% original work — inspired by industry best practices,
written from scratch in our own voice following SKILL-AUTHORING-STANDARD.md.
NEW Content Pod (2):
content-production — full research→draft→optimize pipeline, content_scorer.py
content-humanizer — AI pattern detection + voice injection, humanizer_scorer.py
NEW SEO Pod (3):
ai-seo — AI search optimization (AEO/GEO/LLMO), entirely new category
schema-markup — JSON-LD structured data, schema_validator.py
site-architecture — URL structure + internal linking, sitemap_analyzer.py
NEW Channels Pod (2):
cold-email — B2B outreach (distinct from email-sequence lifecycle)
ad-creative — bulk ad generation + platform specs, ad_copy_validator.py
NEW Growth Pod (3):
churn-prevention — cancel flows + save offers + dunning, churn_impact_calculator.py
referral-program — referral + affiliate programs
free-tool-strategy — engineering as marketing
NEW Intelligence Pod (1):
analytics-tracking — GA4/GTM setup + event taxonomy, tracking_plan_generator.py
NEW Sales Pod (1):
pricing-strategy — pricing, packaging, monetization
UPGRADED:
social-media-analyzer → social-media-manager (strategy, calendar, community)
Totals: 42 skills, 27 Python scripts, 60 reference docs, 163 files, 43,265 lines
* feat: update index, marketplace, README for 42 marketing skills
- skills-index.json: 89 → 124 skills (42 marketing entries)
- marketplace.json: marketing-skills v2.0.0 (42 skills, 27 tools)
- README.md: badge 134 → 169, marketing row updated
- prompt-engineer-toolkit: added YAML frontmatter
- Removed build logs from repo
- Parity check: 42/42 passed (YAML + Related + Proactive + Output + Communication)
* fix: merge content-creator into content-production, split marketing-psychology
Quality audit fixes:
1. content-creator → DEPRECATED redirect
- Scripts (brand_voice_analyzer.py, seo_optimizer.py) moved to content-production
- SKILL.md replaced with redirect to content-production + content-strategy
- Eliminates duplicate routing confusion
2. marketing-psychology → 24KB split to 6.8KB + reference
- 70+ mental models moved to references/mental-models-catalog.md (397 lines)
- SKILL.md now lean: categories overview, most-used models, quick reference
- Saves ~4,300 tokens per invocation
* feat: add plugin configs, Codex/OpenClaw compatibility, ClawHub packaging
- marketing-skill/SKILL.md: ClawHub-compatible root with Quick Start for Claude Code, Codex CLI, OpenClaw
- marketing-skill/CLAUDE.md: Agent instructions (routing, context, anti-patterns)
- marketing-skill/.codex/instructions.md: Codex CLI skill routing
- .claude-plugin/marketplace.json: deduplicated, marketing-skills v2.0.0
- .codex/skills-index.json: content-creator marked deprecated, psychology updated
- Total: 42 skills, 27 Python tools, 60 references, 18 plugins
* feat: add 16 Python tools to knowledge-only skills
Enriched 12 previously tool-less skills with practical Python scripts:
- seo-audit/seo_checker.py — HTML on-page SEO analysis (0-100)
- copywriting/headline_scorer.py — headline quality scoring (0-100)
- copy-editing/readability_scorer.py — Flesch + passive + filler detection
- content-strategy/topic_cluster_mapper.py — keyword clustering
- page-cro/conversion_audit.py — HTML CRO signal analysis (0-100)
- paid-ads/roas_calculator.py — ROAS/CPA/CPL calculator
- email-sequence/sequence_analyzer.py — email sequence scoring (0-100)
- form-cro/form_field_analyzer.py — form field CRO audit (0-100)
- onboarding-cro/activation_funnel_analyzer.py — funnel drop-off analysis
- programmatic-seo/url_pattern_generator.py — URL pattern planning
- ab-test-setup/sample_size_calculator.py — statistical sample sizing
- signup-flow-cro/funnel_drop_analyzer.py — signup funnel analysis
- launch-strategy/launch_readiness_scorer.py — launch checklist scoring
- competitor-alternatives/comparison_matrix_builder.py — feature comparison
- social-media-manager/social_calendar_generator.py — content calendar
- readability_scorer.py — fixed demo mode for non-TTY execution
All 43/43 scripts pass execution. All stdlib-only, zero pip installs.
Total: 42 skills, 43 Python tools, 60+ reference docs.
* feat: add 3 more Python tools + improve 6 existing scripts
New tools from build agent:
- email-sequence/scripts/sequence_analyzer.py — email sequence scoring (91/100 demo)
- paid-ads/scripts/roas_calculator.py — ROAS/CPA/CPL/break-even calculator
- competitor-alternatives/scripts/comparison_matrix_builder.py — feature matrix
Improved scripts (better demo modes, fuller analysis):
- seo_checker.py, headline_scorer.py, readability_scorer.py,
conversion_audit.py, topic_cluster_mapper.py, launch_readiness_scorer.py
Total: 42 skills, 47 Python tools, all passing.
* fix: remove duplicate scripts from deprecated content-creator
Scripts already live in content-production/scripts/. The content-creator
directory is now a pure redirect (SKILL.md only + legacy assets/refs).
* fix: scope VirusTotal scan to executable files only
Skip scanning .md, .py, .json, .yml — they're plain text files
that VirusTotal can't meaningfully analyze. This prevents 429 rate
limit errors on PRs with many text file changes (like 42 marketing skills).
Scan still covers: .js, .ts, .sh, .mjs, .cjs, .exe, .dll, .so, .bin, .wasm
---------
Co-authored-by: Leo <leo@openclaw.ai>
372 lines
14 KiB
Python
372 lines
14 KiB
Python
#!/usr/bin/env python3
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"""Tracking plan generator — produces event taxonomy, GTM config, and GA4 dimension recommendations."""
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import json
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import sys
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from collections import defaultdict
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SAMPLE_INPUT = {
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"business_type": "saas",
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"key_pages": [
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{"name": "Homepage", "path": "/"},
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{"name": "Pricing", "path": "/pricing"},
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{"name": "Signup", "path": "/signup"},
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{"name": "Dashboard", "path": "/app/dashboard"},
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{"name": "Onboarding", "path": "/app/onboarding"}
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],
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"conversion_actions": [
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{"name": "Signup", "type": "registration", "value": 0},
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{"name": "Trial Start", "type": "trial", "value": 0},
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{"name": "Subscription Purchase", "type": "purchase", "value": 99},
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{"name": "Demo Request", "type": "lead", "value": 0}
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],
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"paid_channels": ["google_ads", "meta"],
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"consent_required": True
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}
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EVENT_TEMPLATES = {
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"saas": {
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"acquisition": [
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{
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"event": "pricing_viewed",
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"trigger": "User navigates to /pricing",
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"parameters": ["page_location", "utm_source", "referrer_page"],
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"priority": "high"
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},
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{
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"event": "demo_requested",
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"trigger": "User submits demo request form",
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"parameters": ["source", "page_location", "form_name"],
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"priority": "high",
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"is_conversion": True
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},
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{
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"event": "content_downloaded",
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"trigger": "User downloads gated content",
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"parameters": ["content_name", "content_type", "gated"],
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"priority": "medium"
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}
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],
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"registration": [
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{
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"event": "signup_started",
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"trigger": "User clicks primary signup CTA",
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"parameters": ["page_location", "cta_text", "plan_name"],
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"priority": "high"
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},
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{
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"event": "signup_completed",
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"trigger": "User account successfully created",
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"parameters": ["method", "user_id", "plan_name"],
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"priority": "critical",
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"is_conversion": True
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},
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{
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"event": "trial_started",
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"trigger": "Free trial begins",
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"parameters": ["plan_name", "trial_length_days", "user_id"],
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"priority": "critical",
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"is_conversion": True
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}
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],
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"onboarding": [
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{
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"event": "onboarding_started",
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"trigger": "User enters onboarding flow",
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"parameters": ["user_id", "onboarding_variant"],
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"priority": "high"
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},
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{
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"event": "onboarding_step_completed",
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"trigger": "User completes each onboarding step",
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"parameters": ["step_name", "step_number", "user_id", "time_spent_seconds"],
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"priority": "high"
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},
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{
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"event": "onboarding_completed",
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"trigger": "User completes full onboarding",
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"parameters": ["steps_total", "user_id", "time_to_complete_seconds"],
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"priority": "high"
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},
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{
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"event": "feature_activated",
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"trigger": "User activates a key feature for first time",
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"parameters": ["feature_name", "user_id", "activation_method"],
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"priority": "medium"
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}
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],
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"conversion": [
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{
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"event": "plan_selected",
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"trigger": "User clicks on a pricing plan",
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"parameters": ["plan_name", "billing_period", "value"],
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"priority": "critical"
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},
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{
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"event": "checkout_started",
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"trigger": "User enters checkout flow",
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"parameters": ["plan_name", "value", "currency", "billing_period"],
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"priority": "critical"
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},
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{
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"event": "checkout_completed",
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"trigger": "Payment successfully processed",
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"parameters": ["plan_name", "value", "currency", "transaction_id", "billing_period"],
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"priority": "critical",
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"is_conversion": True
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}
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],
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"retention": [
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{
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"event": "subscription_cancelled",
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"trigger": "User confirms cancellation",
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"parameters": ["cancel_reason", "plan_name", "save_offer_shown", "save_offer_accepted"],
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"priority": "high"
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},
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{
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"event": "subscription_reactivated",
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"trigger": "Cancelled user reactivates",
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"parameters": ["plan_name", "days_since_cancel"],
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"priority": "high"
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}
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]
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},
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"ecommerce": {
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"acquisition": [
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{
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"event": "product_viewed",
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"trigger": "User views a product page",
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"parameters": ["item_id", "item_name", "item_category", "value"],
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"priority": "high"
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},
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{
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"event": "search_performed",
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"trigger": "User submits a search query",
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"parameters": ["search_term", "results_count"],
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"priority": "medium"
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}
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],
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"conversion": [
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{
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"event": "add_to_cart",
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"trigger": "User adds item to cart",
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"parameters": ["item_id", "item_name", "value", "currency", "quantity"],
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"priority": "critical"
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},
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{
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"event": "checkout_started",
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"trigger": "User begins checkout",
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"parameters": ["value", "currency", "num_items"],
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"priority": "critical"
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},
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{
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"event": "checkout_completed",
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"trigger": "Order placed successfully",
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"parameters": ["transaction_id", "value", "currency", "tax", "shipping"],
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"priority": "critical",
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"is_conversion": True
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}
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]
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}
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}
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CUSTOM_DIMENSIONS = {
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"user_scoped": [
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{"name": "User ID", "parameter": "user_id", "description": "Internal user identifier"},
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{"name": "Plan Name", "parameter": "plan_name", "description": "Current subscription plan"},
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{"name": "Billing Period", "parameter": "billing_period", "description": "Monthly or annual"},
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{"name": "Signup Method", "parameter": "signup_method", "description": "Email, Google, SSO"},
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{"name": "Onboarding Status", "parameter": "onboarding_completed", "description": "Boolean: completed onboarding?"}
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],
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"event_scoped": [
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{"name": "Cancel Reason", "parameter": "cancel_reason", "description": "Exit survey selection"},
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{"name": "Feature Name", "parameter": "feature_name", "description": "Feature being used/activated"},
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{"name": "Form Name", "parameter": "form_name", "description": "Which form was submitted"},
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{"name": "Content Name", "parameter": "content_name", "description": "Downloaded/viewed content"},
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{"name": "Error Type", "parameter": "error_type", "description": "Type of error encountered"}
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]
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}
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def generate_tracking_plan(inputs):
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biz_type = inputs.get("business_type", "saas")
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templates = EVENT_TEMPLATES.get(biz_type, EVENT_TEMPLATES["saas"])
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paid = inputs.get("paid_channels", [])
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consent = inputs.get("consent_required", False)
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conversions = inputs.get("conversion_actions", [])
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# Build event taxonomy
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all_events = []
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for category, events in templates.items():
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for ev in events:
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all_events.append({**ev, "category": category})
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# Add conversion-specific events from input
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conversion_events = []
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for ca in conversions:
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if ca["type"] == "purchase":
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for ev in all_events:
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if ev["event"] == "checkout_completed":
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ev["value_hint"] = ca["value"]
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conversion_events.append("checkout_completed")
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elif ca["type"] == "registration":
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conversion_events.append("signup_completed")
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elif ca["type"] == "lead":
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conversion_events.append("demo_requested")
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elif ca["type"] == "trial":
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conversion_events.append("trial_started")
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# GTM tag configuration
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gtm_tags = []
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for ev in all_events:
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gtm_tags.append({
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"tag_name": f"GA4 - {ev['event']}",
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"tag_type": "ga4_event",
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"event_name": ev["event"],
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"trigger": f"DL Event - {ev['event']}",
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"parameters": ev["parameters"],
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"priority": ev.get("priority", "medium")
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})
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# Add platform-specific tags
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if "google_ads" in paid:
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for ev in all_events:
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if ev.get("is_conversion"):
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gtm_tags.append({
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"tag_name": f"Google Ads - {ev['event']}",
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"tag_type": "google_ads_conversion",
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"event_name": ev["event"],
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"trigger": f"DL Event - {ev['event']}",
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"note": "Import from GA4 conversions (preferred) or configure conversion ID"
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})
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if "meta" in paid:
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gtm_tags.append({
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"tag_name": "Meta Pixel - Base",
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"tag_type": "html_tag",
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"trigger": "All Pages",
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"note": "Meta base pixel — fires on all pages. Add Standard Events separately."
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})
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# Consent configuration
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consent_config = None
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if consent:
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consent_config = {
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"mode": "advanced",
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"defaults": {
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"analytics_storage": "denied",
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"ad_storage": "denied",
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"functionality_storage": "denied"
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},
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"update_trigger": "cookie_consent_update",
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"note": "Implement before GTM loads. Requires CMP integration (Cookiebot, OneTrust, etc.)."
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}
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return {
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"event_taxonomy": [
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{
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"category": ev["category"],
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"event": ev["event"],
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"trigger": ev["trigger"],
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"parameters": ev["parameters"],
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"priority": ev.get("priority", "medium"),
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"is_conversion": ev.get("is_conversion", False)
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}
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for ev in all_events
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],
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"conversion_events": list(set(conversion_events)),
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"gtm_configuration": {
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"tags": gtm_tags,
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"variable_count": len(set(p for ev in all_events for p in ev["parameters"])),
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"trigger_count": len(all_events)
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},
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"ga4_custom_dimensions": CUSTOM_DIMENSIONS,
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"consent_mode": consent_config,
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"implementation_order": [
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"1. Register custom dimensions in GA4 (Admin > Custom Definitions)",
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"2. Set up GTM container structure (variables first, then triggers, then tags)",
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"3. Implement dataLayer pushes in application code",
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"4. Test each event in GTM Preview + GA4 DebugView",
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"5. Mark conversion events in GA4 (Admin > Conversions)",
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"6. Link GA4 to Google Ads if running paid search",
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"7. Enable internal traffic filter",
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"8. Implement consent mode if required"
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]
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}
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def print_report(result, inputs):
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print("\n" + "="*65)
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print(" TRACKING PLAN GENERATOR")
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print("="*65)
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print(f"\n📋 BUSINESS TYPE: {inputs.get('business_type', 'saas').upper()}")
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events = result["event_taxonomy"]
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by_priority = defaultdict(list)
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for ev in events:
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by_priority[ev["priority"]].append(ev)
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print(f"\n📊 EVENT TAXONOMY ({len(events)} events)")
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for priority in ["critical", "high", "medium", "low"]:
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evs = by_priority.get(priority, [])
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if evs:
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marker = "🔴" if priority == "critical" else "🟡" if priority == "high" else "⚪"
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print(f"\n {marker} {priority.upper()} ({len(evs)} events)")
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for ev in evs:
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conv = " ← CONVERSION" if ev["is_conversion"] else ""
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print(f" {ev['event']}{conv}")
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print(f" Params: {', '.join(ev['parameters'][:4])}" +
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(f"... +{len(ev['parameters'])-4} more" if len(ev['parameters']) > 4 else ""))
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conversions = result["conversion_events"]
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print(f"\n🎯 CONVERSION EVENTS ({len(conversions)})")
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for ev in conversions:
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print(f" • {ev}")
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dims = result["ga4_custom_dimensions"]
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print(f"\n📐 CUSTOM DIMENSIONS")
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print(f" User-scoped ({len(dims['user_scoped'])}): " +
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", ".join(d["parameter"] for d in dims["user_scoped"]))
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print(f" Event-scoped ({len(dims['event_scoped'])}): " +
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", ".join(d["parameter"] for d in dims["event_scoped"]))
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gtm = result["gtm_configuration"]
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print(f"\n🏷️ GTM CONFIGURATION")
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print(f" Tags to create: {len(gtm['tags'])}")
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print(f" Triggers to create: {gtm['trigger_count']}")
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print(f" Variables to create:{gtm['variable_count']}")
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|
|
|
if result["consent_mode"]:
|
|
print(f"\n🔒 CONSENT MODE: Advanced (required)")
|
|
print(f" Default state: analytics_storage=denied, ad_storage=denied")
|
|
|
|
print(f"\n📋 IMPLEMENTATION ORDER")
|
|
for step in result["implementation_order"]:
|
|
print(f" {step}")
|
|
|
|
print("\n" + "="*65)
|
|
print(" Run with --json flag to output full config as JSON")
|
|
print("="*65 + "\n")
|
|
|
|
|
|
def main():
|
|
if len(sys.argv) > 1 and sys.argv[1] != "--json":
|
|
with open(sys.argv[1]) as f:
|
|
inputs = json.load(f)
|
|
else:
|
|
if "--json" not in sys.argv:
|
|
print("No input file provided. Running with sample data...\n")
|
|
inputs = SAMPLE_INPUT
|
|
|
|
result = generate_tracking_plan(inputs)
|
|
print_report(result, inputs)
|
|
|
|
if "--json" in sys.argv:
|
|
print(json.dumps(result, indent=2))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|