Open-source AI marketing skills — growth experiments, sales pipeline, content ops, outbound, SEO, and finance automation
2.8k
Stars
593
Forks
6
Open issues
1
Contributors
AI Analysis
This is a collection of production-ready Claude Code skills (AI-powered automation workflows) for marketing and sales teams, covering growth experiments, sales pipeline management, content operations, SEO, outbound prospecting, finance automation, and related functions. It is purpose-built for marketing professionals and revenue teams who want to integrate AI coding agents into their existing workflows — not a general-purpose library. Best suited for organizations using Claude Code and seekin...
Inferred from signals mentioned in the README (tests, CI, type safety) — not a review of the actual code.
AI's overall editorial judgment — not an average of the bars above, can weigh other factors too.
Python AI marketing workflows for Claude Code — battle-tested sales, content, and growth automation
ai-marketing-skills is a collection of Python-based workflows designed to run inside Claude Code (Anthropic's AI coding agent) for marketing and sales operations: growth experiments, sales pipeline qualification, content production, outbound prospecting, SEO, finance analysis, and revenue intelligence. Built by Single Brain and released March 2026, it targets marketing and revenue teams seeking to automate multi-step business processes rather than write prompts. The project gained 2,734 stars and 125 stars in the past week as of late June 2026, indicating early-stage momentum among AI-native marketing practitioners.
Launched March 2026 (roughly 3 months before evaluation date), ai-marketing-skills emerged as Claude Code adoption grew and teams began seeking plug-and-play workflows rather than one-off prompt templates. The README emphasizes battle-tested production use at Single Brain. It positions itself as the inverse of generic prompt libraries — complete automation pipelines with scoring algorithms and statistical rigor rather than copy-paste examples.
Growth from launch (March 2026) to evaluation date (June 2026) shows 2,734 stars and 578 forks over ~3 months, with 125 stars added in the final 7 days. This trajectory suggests accelerating interest coinciding with broader Claude Code adoption and AI agent maturation. The 125 stars in 7 days implies viral momentum among a specific cohort (likely marketing technologists and revenue ops teams). Absence of earlier star history makes long-term growth patterns unclear.
README claims these are 'battle-tested on real pipelines generating millions in revenue' at Single Brain. However, this is a founder claim with no independent verification. No case studies, customer testimonials, or deployment counts are documented. Public GitHub activity (stars, forks) reflects engineer/enthusiast interest, not necessarily production adoption. Adoption not verified at scale beyond Single Brain's own operations.
Based on README, the project organizes skills into 14 category folders (growth-engine, sales-pipeline, content-ops, etc.), each containing Python scripts and supporting reference files. The README describes use of statistical methods (bootstrap confidence intervals, Mann-Whitney U tests), multi-layer intelligence (e.g., Deal Resurrector with 'follow the champion' logic), and expert panel scoring systems. Each skill includes a SKILL.md file designed for Claude Code integration. Appears modular and composable, but actual implementation quality cannot be assessed from README alone.
Not documented in README. No mention of unit tests, integration tests, CI/CD pipelines, or test harnesses. This is a significant gap for production workflows handling real business operations.
Last push: June 21, 2026 (8 days before evaluation date). Repository is actively maintained. However, the project is only ~3 months old; long-term maintenance patterns are not yet established. README quality is thorough and professionally written, suggesting serious authorship intent. Absence of issue visibility or contribution guidelines in excerpt limits assessment of maintainability culture.
ADOPT IF: you use Claude Code, run marketing/revenue operations, have technical enough teams to modify Python scripts, and want open-source control over automation logic rather than SaaS lock-in. The workflows appear substantive (statistical rigor, multi-layer logic) and the project is actively maintained. AVOID IF: you need enterprise support, want zero configuration, lack Python-comfortable teams, or require test coverage and documented SLAs. Early-stage project with unverified production adoption outside founder's company. MONITOR IF: you're evaluating Claude Code as your AI coding agent runtime — adoption of this library is a signal of ecosystem maturity, and improvements in test coverage + documented deployments would strengthen trust considerably.
Independent dimensions
Mainstream potential
4/10
Technical importance
6/10
Adoption evidence
2/10
- Test coverage and error handling are not documented; scripts may fail silently or unpredictably in production with unfamiliar data shapes.
- Adoption is claimed but not independently verified — may be limited to Single Brain's internal use or small early-adopter cohort; viral star count may not correlate to actual usage.
- Claude Code itself is a young, rapidly evolving platform; skills may break as Claude API/Code Agent interfaces change, and maintenance burden on small team could become unsustainable.
- Pricing and API cost for Claude Code + Anthropic API are not negligible; total cost of ownership compared to traditional marketing automation platforms is unclear.
- Repository is only ~3 months old; no track record of long-term maintenance, community contribution culture, or issue resolution velocity.
ai-marketing-skills will likely remain a niche but credible resource for Claude Code users in marketing/revenue ops, particularly among technical founders and small teams. Growth rate may plateau once Claude Code adoption stabilizes. Mainstream success depends on: (1) Claude Code achieving broader enterprise adoption, (2) community contributions expanding coverage beyond Single Brain's use cases, (3) demonstrated ROI in case studies. Smaller than general-purpose agent libraries, but may become canonical 'marketing skills' layer for Claude Code ecosystem if maintained and documented well.
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Languages
Information
- Website
- https://www.singlegrain.com
- Language
- Python
- License
- MIT
- Last updated
- 5d ago
- Created
- 3mo ago
- Analyzed with
- anthropic/claude-haiku-4-5
Stars over time
Contributors over time
Top 100 contributors only — repos with more will plateau at 100.
Open issues
Top contributors
Recent releases
No releases published yet.
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35K stars, JavaScript, appears to be a prompt/prompt-template library. ai-marketing-skills differentiates by offering complete executable workflows rather than prompts, and targets Claude Code agents rather than general LLM APIs.
2K stars, Python, similar positioning. Direct competitor on star count. Likely overlaps in problem space (marketing automation) but README does not detail strategic differences.
1.4K stars, TypeScript, appears to serve marketing operations. Smaller ecosystem, different language choice, positioning unclear from this metadata alone.
These are enterprise SaaS platforms with built-in workflows. ai-marketing-skills targets teams wanting open-source, Claude Code-native, customizable automation over proprietary platforms — a different trade-off (control + cost vs. support + polish).
Horizontal AI-agent tooling. ai-marketing-skills is vertical (marketing-specific workflows). Not direct competitors but adjacent ecosystems; adoption of Claude Code as first-class agent runtime strengthens ai-marketing-skills' positioning.