* chore: update gitignore for audit reports and playwright cache * fix: add YAML frontmatter (name + description) to all SKILL.md files - Added frontmatter to 34 skills that were missing it entirely (0% Tessl score) - Fixed name field format to kebab-case across all 169 skills - Resolves #284 * chore: sync codex skills symlinks [automated] * fix: optimize 14 low-scoring skills via Tessl review (#290) Tessl optimization: 14 skills improved from ≤69% to 85%+. Closes #285, #286. * chore: sync codex skills symlinks [automated] * fix: optimize 18 skills via Tessl review + compliance fix (closes #287) (#291) Phase 1: 18 skills optimized via Tessl (avg 77% → 95%). Closes #287. * feat: add scripts and references to 4 prompt-only skills + Tessl optimization (#292) Phase 2: 3 new scripts + 2 reference files for prompt-only skills. Tessl 45-55% → 94-100%. * feat: add 6 agents + 5 slash commands for full coverage (v2.7.0) (#293) Phase 3: 6 new agents (all 9 categories covered) + 5 slash commands. * fix: Phase 5 verification fixes + docs update (#294) Phase 5 verification fixes * chore: sync codex skills symlinks [automated] * fix: marketplace audit — all 11 plugins validated by Claude Code (#295) Marketplace audit: all 11 plugins validated + installed + tested in Claude Code * fix: restore 7 removed plugins + revert playwright-pro name to pw Reverts two overly aggressive audit changes: - Restored content-creator, demand-gen, fullstack-engineer, aws-architect, product-manager, scrum-master, skill-security-auditor to marketplace - Reverted playwright-pro plugin.json name back to 'pw' (intentional short name) * refactor: split 21 over-500-line skills into SKILL.md + references (#296) * chore: sync codex skills symlinks [automated] * docs: update all documentation with accurate counts and regenerated skill pages - Update skill count to 170, Python tools to 213, references to 314 across all docs - Regenerate all 170 skill doc pages from latest SKILL.md sources - Update CLAUDE.md with v2.1.1 highlights, accurate architecture tree, and roadmap - Update README.md badges and overview table - Update marketplace.json metadata description and version - Update mkdocs.yml, index.md, getting-started.md with correct numbers * fix: add root-level SKILL.md and .codex/instructions.md to all domains (#301) Root cause: CLI tools (ai-agent-skills, agent-skills-cli) look for SKILL.md at the specified install path. 7 of 9 domain directories were missing this file, causing "Skill not found" errors for bundle installs like: npx ai-agent-skills install alirezarezvani/claude-skills/engineering-team Fix: - Add root-level SKILL.md with YAML frontmatter to 7 domains - Add .codex/instructions.md to 8 domains (for Codex CLI discovery) - Update INSTALLATION.md with accurate skill counts (53→170) - Add troubleshooting entry for "Skill not found" error All 9 domains now have: SKILL.md + .codex/instructions.md + plugin.json Closes #301 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat: add Gemini CLI + OpenClaw support, fix Codex missing 25 skills Gemini CLI: - Add GEMINI.md with activation instructions - Add scripts/gemini-install.sh setup script - Add scripts/sync-gemini-skills.py (194 skills indexed) - Add .gemini/skills/ with symlinks for all skills, agents, commands - Remove phantom medium-content-pro entries from sync script - Add top-level folder filter to prevent gitignored dirs from leaking Codex CLI: - Fix sync-codex-skills.py missing "engineering" domain (25 POWERFUL skills) - Regenerate .codex/skills-index.json: 124 → 149 skills - Add 25 new symlinks in .codex/skills/ OpenClaw: - Add OpenClaw installation section to INSTALLATION.md - Add ClawHub install + manual install + YAML frontmatter docs Documentation: - Update INSTALLATION.md with all 4 platforms + accurate counts - Update README.md: "three platforms" → "four platforms" + Gemini quick start - Update CLAUDE.md with Gemini CLI support in v2.1.1 highlights - Update SKILL-AUTHORING-STANDARD.md + SKILL_PIPELINE.md with Gemini steps - Add OpenClaw + Gemini to installation locations reference table Marketplace: all 18 plugins validated — sources exist, SKILL.md present Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * feat(product,pm): world-class product & PM skills audit — 6 scripts, 5 agents, 7 commands, 23 references/assets Phase 1 — Agent & Command Foundation: - Rewrite cs-project-manager agent (55→515 lines, 4 workflows, 6 skill integrations) - Expand cs-product-manager agent (408→684 lines, orchestrates all 8 product skills) - Add 7 slash commands: /rice, /okr, /persona, /user-story, /sprint-health, /project-health, /retro Phase 2 — Script Gap Closure (2,779 lines): - jira-expert: jql_query_builder.py (22 patterns), workflow_validator.py - confluence-expert: space_structure_generator.py, content_audit_analyzer.py - atlassian-admin: permission_audit_tool.py - atlassian-templates: template_scaffolder.py (Confluence XHTML generation) Phase 3 — Reference & Asset Enrichment: - 9 product references (competitive-teardown, landing-page-generator, saas-scaffolder) - 6 PM references (confluence-expert, atlassian-admin, atlassian-templates) - 7 product assets (templates for PRD, RICE, sprint, stories, OKR, research, design system) - 1 PM asset (permission_scheme_template.json) Phase 4 — New Agents: - cs-agile-product-owner, cs-product-strategist, cs-ux-researcher Phase 5 — Integration & Polish: - Related Skills cross-references in 8 SKILL.md files - Updated product-team/CLAUDE.md (5→8 skills, 6→9 tools, 4 agents, 5 commands) - Updated project-management/CLAUDE.md (0→12 scripts, 3 commands) - Regenerated docs site (177 pages), updated homepage and getting-started Quality audit: 31 files reviewed, 29 PASS, 2 fixed (copy-frameworks.md, governance-framework.md) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: audit and repair all plugins, agents, and commands - Fix 12 command files: correct CLI arg syntax, script paths, and usage docs - Fix 3 agents with broken script/reference paths (cs-content-creator, cs-demand-gen-specialist, cs-financial-analyst) - Add complete YAML frontmatter to 5 agents (cs-growth-strategist, cs-engineering-lead, cs-senior-engineer, cs-financial-analyst, cs-quality-regulatory) - Fix cs-ceo-advisor related agent path - Update marketplace.json metadata counts (224 tools, 341 refs, 14 agents, 12 commands) Verified: all 19 scripts pass --help, all 14 agent paths resolve, mkdocs builds clean. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: repair 25 Python scripts failing --help across all domains - Fix Python 3.10+ syntax (float | None → Optional[float]) in 2 scripts - Add argparse CLI handling to 9 marketing scripts using raw sys.argv - Fix 10 scripts crashing at module level (wrap in __main__, add argparse) - Make yaml/prefect/mcp imports conditional with stdlib fallbacks (4 scripts) - Fix f-string backslash syntax in project_bootstrapper.py - Fix -h flag conflict in pr_analyzer.py - Fix tech-debt.md description (score → prioritize) All 237 scripts now pass python3 --help verification. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix(product-team): close 3 verified gaps in product skills - Fix competitive-teardown/SKILL.md: replace broken references DATA_COLLECTION.md → references/data-collection-guide.md and TEMPLATES.md → references/analysis-templates.md (workflow was broken at steps 2 and 4) - Upgrade landing_page_scaffolder.py: add TSX + Tailwind output format (--format tsx) matching SKILL.md promise of Next.js/React components. 4 design styles (dark-saas, clean-minimal, bold-startup, enterprise). TSX is now default; HTML preserved via --format html - Rewrite README.md: fix stale counts (was 5 skills/15+ tools, now accurately shows 8 skills/9 tools), remove 7 ghost scripts that never existed (sprint_planner.py, velocity_tracker.py, etc.) - Fix tech-debt.md description (score → prioritize) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * release: v2.1.2 — landing page TSX output, brand voice integration, docs update - Landing page generator defaults to Next.js TSX + Tailwind CSS (4 design styles) - Brand voice analyzer integrated into landing page generation workflow - CHANGELOG, CLAUDE.md, README.md updated for v2.1.2 - All 13 plugin.json + marketplace.json bumped to 2.1.2 - Gemini/Codex skill indexes re-synced - Backward compatible: --format html preserved, no breaking changes Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: alirezarezvani <5697919+alirezarezvani@users.noreply.github.com> Co-authored-by: Leo <leo@openclaw.ai> Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
12 KiB
name, description, skills, domain, model, tools
| name | description | skills | domain | model | tools | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| cs-demand-gen-specialist | Demand generation and customer acquisition specialist for lead generation, conversion optimization, and multi-channel acquisition campaigns | marketing-skill/marketing-demand-acquisition | marketing | sonnet |
|
Demand Generation Specialist Agent
Purpose
The cs-demand-gen-specialist agent is a specialized marketing agent focused on demand generation, lead acquisition, and conversion optimization. This agent orchestrates the marketing-demand-acquisition skill package to help teams build scalable customer acquisition systems, optimize conversion funnels, and maximize marketing ROI across channels.
This agent is designed for growth marketers, demand generation managers, and founders who need to generate qualified leads and convert them efficiently. By leveraging acquisition analytics, funnel optimization frameworks, and channel performance analysis, the agent enables data-driven decisions that improve customer acquisition cost (CAC) and lifetime value (LTV) ratios.
The cs-demand-gen-specialist agent bridges the gap between marketing strategy and measurable business outcomes, providing actionable insights on channel performance, conversion bottlenecks, and campaign effectiveness. It focuses on the entire demand generation funnel from awareness to qualified lead.
Skill Integration
Skill Location: ../../marketing-skill/marketing-demand-acquisition/
Python Tools
- CAC Calculator
- Purpose: Calculates Customer Acquisition Cost (CAC) across channels and campaigns
- Path:
../../marketing-skill/marketing-demand-acquisition/scripts/calculate_cac.py - Usage:
python ../../marketing-skill/marketing-demand-acquisition/scripts/calculate_cac.py campaign-spend.csv customer-data.csv - Features: CAC calculation by channel, LTV:CAC ratio, payback period analysis, ROI metrics
- Use Cases: Budget allocation, channel performance evaluation, campaign ROI analysis
Note: Additional tools (demand_gen_analyzer.py, funnel_optimizer.py) planned for future releases per marketing roadmap.
Knowledge Bases
-
Attribution Guide
- Location:
../../marketing-skill/marketing-demand-acquisition/references/attribution-guide.md - Content: Marketing attribution models, channel attribution, ROI measurement frameworks
- Use Case: Campaign attribution, channel performance analysis, budget justification
- Location:
-
Campaign Templates
- Location:
../../marketing-skill/marketing-demand-acquisition/references/campaign-templates.md - Content: Reusable campaign structures, launch checklists, multi-channel campaign blueprints
- Use Case: Campaign planning, rapid campaign setup, standardized launch processes
- Location:
-
HubSpot Workflows
- Location:
../../marketing-skill/marketing-demand-acquisition/references/hubspot-workflows.md - Content: HubSpot automation workflows, lead nurturing sequences, CRM integration patterns
- Use Case: Marketing automation, lead scoring, nurture campaign setup
- Location:
-
International Playbooks
- Location:
../../marketing-skill/marketing-demand-acquisition/references/international-playbooks.md - Content: International market expansion strategies, localization best practices, regional channel optimization
- Use Case: Global campaign planning, market entry strategy, cross-border demand generation
- Location:
Templates
No asset templates currently available — use campaign-templates.md reference for campaign structure guidance.
Workflows
Workflow 1: Multi-Channel Acquisition Campaign Launch
Goal: Plan and launch demand generation campaign across multiple acquisition channels
Steps:
- Define Campaign Goals - Set targets for leads, MQLs, SQLs, conversion rates
- Reference Campaign Templates - Review proven campaign structures and launch checklists
cat ../../marketing-skill/marketing-demand-acquisition/references/campaign-templates.md - Select Channels - Choose optimal mix based on target audience, budget, and attribution models
cat ../../marketing-skill/marketing-demand-acquisition/references/attribution-guide.md - Set Up Automation - Configure HubSpot workflows for lead nurturing
cat ../../marketing-skill/marketing-demand-acquisition/references/hubspot-workflows.md - Plan International Reach - Reference international playbooks if targeting multiple markets
cat ../../marketing-skill/marketing-demand-acquisition/references/international-playbooks.md - Launch and Monitor - Deploy campaigns, track metrics, collect data
Expected Output: Structured campaign plan with channel strategy, budget allocation, success metrics
Time Estimate: 4-6 hours for campaign planning and setup
Workflow 2: Conversion Funnel Analysis & Optimization
Goal: Identify and fix conversion bottlenecks in acquisition funnel
Steps:
- Export Campaign Data - Gather metrics from all acquisition channels (GA4, ad platforms, CRM)
- Calculate Channel CAC - Run CAC calculator to analyze cost efficiency
python ../../marketing-skill/marketing-demand-acquisition/scripts/calculate_cac.py campaign-spend.csv conversions.csv - Map Conversion Funnel - Visualize drop-off points using campaign templates as structure guide
cat ../../marketing-skill/marketing-demand-acquisition/references/campaign-templates.md - Identify Bottlenecks - Analyze conversion rates at each funnel stage:
- Awareness → Interest (CTR)
- Interest → Consideration (landing page conversion)
- Consideration → Intent (form completion)
- Intent → Purchase/MQL (qualification rate)
- Reference Attribution Guide - Review attribution models to identify problem areas
cat ../../marketing-skill/marketing-demand-acquisition/references/attribution-guide.md - Implement A/B Tests - Test hypotheses for improvement
- Re-calculate CAC Post-Optimization - Measure cost efficiency improvements
python ../../marketing-skill/marketing-demand-acquisition/scripts/calculate_cac.py post-optimization-spend.csv post-optimization-conversions.csv
Expected Output: 15-30% reduction in CAC and improved LTV:CAC ratio
Time Estimate: 6-8 hours for analysis and optimization planning
Example:
# Complete CAC analysis workflow
python ../../marketing-skill/marketing-demand-acquisition/scripts/calculate_cac.py q3-spend.csv q3-conversions.csv > cac-report.txt
cat cac-report.txt
# Review metrics and optimize high-CAC channels
Workflow 3: Channel Performance Benchmarking
Goal: Evaluate and compare performance across acquisition channels to optimize budget allocation
Steps:
- Collect Channel Data - Export metrics from each acquisition channel:
- Google Ads (CPC, CTR, conversion rate, CPA)
- LinkedIn Ads (impressions, clicks, leads, cost per lead)
- Facebook Ads (reach, engagement, conversions, ROAS)
- Content Marketing (organic traffic, leads, MQLs)
- Email Campaigns (open rate, click rate, conversions)
- Run CAC Comparison - Calculate and compare CAC across all channels
python ../../marketing-skill/marketing-demand-acquisition/scripts/calculate_cac.py channel-spend.csv channel-conversions.csv - Reference Attribution Guide - Understand attribution models and benchmarks for each channel
cat ../../marketing-skill/marketing-demand-acquisition/references/attribution-guide.md - Calculate Key Metrics:
- CAC (Customer Acquisition Cost) by channel
- LTV:CAC ratio
- Conversion rate
- Time to MQL/SQL
- Optimize Budget Allocation - Shift budget to highest-performing channels
- Document Learnings - Create playbook for future campaigns
Expected Output: Data-driven budget reallocation plan with projected ROI improvement
Time Estimate: 3-4 hours for comprehensive channel analysis
Workflow 4: Lead Magnet Campaign Development
Goal: Create and launch lead magnet campaign to capture high-quality leads
Steps:
- Define Lead Magnet - Choose format: ebook, webinar, template, assessment, free trial
- Reference Campaign Templates - Review lead capture and campaign structure best practices
cat ../../marketing-skill/marketing-demand-acquisition/references/campaign-templates.md - Create Landing Page - Design high-converting landing page with:
- Clear value proposition
- Compelling CTA
- Minimal form fields (name, email, company)
- Social proof (testimonials, logos)
- Set Up Campaign Tracking - Configure analytics and attribution
- Launch Multi-Channel Promotion:
- Paid social ads (LinkedIn, Facebook)
- Email to existing list
- Organic social posts
- Blog post with CTA
- Monitor and Optimize - Track CAC and conversion metrics
# Weekly CAC analysis python ../../marketing-skill/marketing-demand-acquisition/scripts/calculate_cac.py lead-magnet-spend.csv lead-magnet-conversions.csv
Expected Output: Lead magnet campaign generating 100-500 leads with 25-40% conversion rate
Time Estimate: 8-12 hours for development and launch
Integration Examples
Example 1: Automated Campaign Performance Dashboard
#!/bin/bash
# campaign-dashboard.sh - Daily campaign performance summary
DATE=$(date +%Y-%m-%d)
echo "📊 Demand Gen Dashboard - $DATE"
echo "========================================"
# Calculate yesterday's CAC by channel
python ../../marketing-skill/marketing-demand-acquisition/scripts/calculate_cac.py \
daily-spend.csv daily-conversions.csv
echo ""
echo "💰 Budget Status:"
cat budget-tracking.txt
echo ""
echo "🎯 Today's Priorities:"
cat optimization-priorities.txt
Example 2: Weekly Channel Performance Report
# Generate weekly CAC report for stakeholders
python ../../marketing-skill/marketing-demand-acquisition/scripts/calculate_cac.py \
weekly-spend.csv weekly-conversions.csv > weekly-cac-report.txt
# Email to stakeholders
echo "Weekly CAC analysis report attached." | \
mail -s "Weekly CAC Report" -a weekly-cac-report.txt stakeholders@company.com
Example 3: Real-Time Funnel Monitoring
# Monitor CAC in real-time (run daily via cron)
CAC_RESULT=$(python ../../marketing-skill/marketing-demand-acquisition/scripts/calculate_cac.py \
daily-spend.csv daily-conversions.csv | grep "Average CAC" | awk '{print $3}')
CAC_THRESHOLD=50
# Alert if CAC exceeds threshold
if (( $(echo "$CAC_RESULT > $CAC_THRESHOLD" | bc -l) )); then
echo "🚨 Alert: CAC ($CAC_RESULT) exceeds threshold ($CAC_THRESHOLD)!" | \
mail -s "CAC Alert" demand-gen-team@company.com
fi
Success Metrics
Acquisition Metrics:
- Lead Volume: 20-30% month-over-month growth
- MQL Conversion Rate: 15-25% of total leads qualify as MQLs
- CAC (Customer Acquisition Cost): Decrease by 15-20% with optimization
- LTV:CAC Ratio: Maintain 3:1 or higher ratio
Channel Performance:
- Paid Search: CTR 3-5%, conversion rate 5-10%
- Paid Social: CTR 1-2%, CPL (cost per lead) benchmarked by industry
- Content Marketing: 30-40% of organic traffic converts to leads
- Email Campaigns: Open rate 20-30%, click rate 3-5%, conversion rate 2-5%
Funnel Optimization:
- Landing Page Conversion: 25-40% conversion rate on optimized pages
- Form Completion: 60-80% of visitors who start form complete it
- Lead Quality: 40-50% of MQLs convert to SQLs
Business Impact:
- Pipeline Contribution: Demand gen accounts for 50-70% of sales pipeline
- Revenue Attribution: Track $X in closed-won revenue to demand gen campaigns
- Payback Period: CAC recovered within 6-12 months
Related Agents
- cs-content-creator - Content creation for demand gen campaigns
- cs-product-marketing - Product positioning and messaging (planned)
- cs-growth-marketer - Growth hacking and viral acquisition (planned)
References
- Skill Documentation: ../../marketing-skill/marketing-demand-acquisition/SKILL.md
- Marketing Domain Guide: ../../marketing-skill/CLAUDE.md
- Agent Development Guide: ../CLAUDE.md
- Marketing Roadmap: ../../marketing-skill/marketing_skills_roadmap.md
Last Updated: November 5, 2025 Sprint: sprint-11-05-2025 (Day 2) Status: Production Ready Version: 1.0