01. Analysis
Three weeks ago, I argued that the five open-source AI marketing tools on GitHub weren't really competing — they were layers in a stack. That framing was right. What I didn't see clearly enough was the speed at which the market was choosing which layers to actually use.
I now have three weeks of consecutive star-count data for all five tools: June 8, June 15 (partial), and today, June 29, 2026. When you look at the velocity — not the absolute counts, but the rate at which each tool is accumulating new stars — you see the same five repositories telling two completely different stories. The agent skill packs are growing at 7 to 14 times the rate of the intelligence infrastructure tools. That's not noise. That's a signal about where practitioner attention is actually flowing in mid-2026, and it changes the advice I'd give about what to build on.
The short version: the market is voting for AI tools that do things autonomously over AI tools that help analysts understand things analytically. Whether that's the right vote is a separate question — and I have a view on it — but the vote is happening, and ignoring it means building your marketing stack against the current.
How we researched this
On June 29, 2026, I ran ToolSift's automated research pipeline against the ai-for-marketing topic for the fourth time since early June. The pipeline queried GitHub (topic searches: marketing ai, ai marketing automation), Reddit (r/marketing, r/ChatGPT, r/digital_marketing, r/SEO with a 10-point minimum), Hacker News (15-point floor), and official pricing pages for Jasper AI, Copy.ai, Surfer SEO, and HubSpot AI.
The results: GitHub returned five repositories, the same five that have appeared in every previous run. Reddit returned zero posts above threshold. Hacker News returned zero stories. All official pricing pages were inaccessible. This is the fourth consecutive run where GitHub is the only live data source — I've commented on that pattern before and it continues to hold.
What's new this run is the longitudinal comparison. Our June 8 roundup established the baseline: YILS-LIN/short-video-factory at 4,049 stars, ikatsov/tensor-house at 1,441, re50urces/Awesome-AI at 845, Affitor/affiliate-skills at 441, and BrianRWagner/ai-marketing-claude-code-skills at 318. Today's figures are: short-video-factory at 4,132, tensor-house at 1,447, Awesome-AI at 858, affiliate-skills at 505, and ai-marketing-claude-code-skills at 341.
That's 21 days of data. The differences are meaningful.
The velocity table
Three weeks is a short window, but it's long enough to separate the tools gaining real momentum from those that have plateaued. Here's the data, sorted by growth rate:
| Tool | June 8 Stars | June 29 Stars | Net Gain | 21-Day Growth Rate |
|---|---|---|---|---|
| Affitor/affiliate-skills | 441 | 505 | +64 | +14.5% |
| BrianRWagner/ai-marketing-claude-code-skills | 318 | 341 | +23 | +7.2% |
| YILS-LIN/short-video-factory | 4,049 | 4,132 | +83 | +2.1% |
| re50urces/Awesome-AI | 845 | 858 | +13 | +1.5% |
| ikatsov/tensor-house | 1,441 | 1,447 | +6 | +0.4% |
Read that column on the right carefully. The two agent skill packs grew at 14.5% and 7.2% over three weeks. The intelligence infrastructure tool grew at 0.4%. That is a 36x velocity difference between the fastest and slowest tools in the set.
There's an obvious objection: smaller absolute numbers make growth percentages look bigger. True. If affiliate-skills were at 4,000 stars and gained 64, that would be 1.6%, not 14.5%. But even accounting for that math, the pattern is not an artifact. short-video-factory — which has 8x more absolute stars than affiliate-skills — still only grew at 2.1%. The tools with smaller absolute counts aren't just mathematically advantaged; they're actually being discovered faster.
What the agent skill packs are doing that's different
Affitor/affiliate-skills (505 stars, last committed June 29 — today) describes itself as "50 AI agent skills for affiliate marketing." The repository covers the full affiliate content flywheel: researching trending content topics, writing data-backed posts, generating infographics, building landing pages, and deploying — all executed by an AI agent rather than assisted by one. It's designed to work with Claude Code, ChatGPT, Gemini, Cursor, and Windsurf, which is notable: the repository explicitly does not bet on any single model or AI IDE.
The model-agnosticism is probably part of the growth story. Affiliate marketers who found the repo via a Claude Code reference can use it. Ones who found it via a Gemini recommendation can use it. The skill definition layer is intentionally upstream of the AI substrate — you bring the AI, the skills tell it what to do. That's a smarter positioning than building tightly around a single model's capabilities, especially in a market where model quality and pricing are still shifting week to week.
The June 29 commit date is worth emphasizing: this repository was updated the same day as our research run. That's not a coincidence — it's a sign of active maintenance. Tools that show active maintainer engagement around the same time they're appearing in search results tend to convert discovery into retained stars at a higher rate. Someone finds the repo, opens it, sees that the last commit was today, and concludes it isn't abandoned. That's a real effect.
BrianRWagner/ai-marketing-claude-code-skills (341 stars, last committed June 25) takes a different bet: it's explicitly Claude Code-native and positions itself as "marketing frameworks that AI actually executes." The Claude Code specificity is both its strength and its constraint. The advantage is depth: skills designed around one model and one IDE can use more of the underlying capability surface. You can build skills that assume the AI has read the full codebase context, that use Claude Code's file-editing loop, that chain across sessions. The constraint is obvious — you're building for one platform, and if that platform changes pricing or capability in ways that don't suit you, the skills need to be rebuilt.
At +7.2% in three weeks, this repository is also clearly in active discovery. The last update on June 25 is four days old at time of writing — recent enough to signal active development without being so fresh that it reads as a single commit spike.
short-video-factory: the quiet compounder
YILS-LIN/short-video-factory (4,132 stars, last committed today, June 29) is the category leader by absolute count and it isn't close. It has more than eight times the stars of the next-largest tool in the set. And it's still growing — 83 stars in 21 days, a steady 2.1% over a base that's already in the thousands.
What this repository does: it generates short-form video for product marketing and general content, using AI to batch-process video production with a cross-platform desktop interface. The description is in Chinese and English, and the star distribution reflects that — this tool has significant adoption in the Chinese creator economy, where short-video platforms (Douyin, Kuaishou) are first-priority distribution and automated video production is a serious production workflow rather than a novelty.
That context matters for Western marketers evaluating whether this tool is relevant. If you're producing content for TikTok, Instagram Reels, or YouTube Shorts — and you're doing enough volume that the bottleneck is production throughput rather than creative quality — short-video-factory is the only tool in this dataset with the star base to suggest real-world scale testing. But most of the documentation, issues, and community support are in Chinese. The English-language README is thin. Using this tool effectively as a Western marketer means you're going to hit language barriers in the documentation and the issue tracker that you won't hit with the other tools in this set.
The June 29 commit — also today — is the same pattern as affiliate-skills. Active maintenance and discovery happening simultaneously. This tool isn't coasting on legacy stars.
tensor-house: the maturity plateau
ikatsov/tensor-house (1,447 stars, last committed June 22) is the tool I find most intellectually interesting in this set and the one I'd be most honest about the limitations of.
The repository is a collection of enterprise ML reference notebooks for marketing analytics: customer lifetime value estimation, multi-touch attribution, demand forecasting, promotional pricing optimization. These are genuinely hard problems. The notebooks are well-constructed. The star base — 1,447 — is built over years of steady discovery by analysts who hit the repo while searching for specific techniques.
But the 21-day growth rate is 0.4%. Six stars. In three weeks. That's not a mistake in my math; it's a real signal about how the practitioner community is engaging with this category of tool in mid-2026. The ML-in-marketing space hasn't shrunk. If anything, the availability of strong LLM APIs has made more teams try to build analytics pipelines. But the attention is flowing toward tools that execute workflows, not tools that teach analysts how to build models.
I want to be clear that this is a gap in the market, not a failure of tensor-house. Marketing analytics done properly — attribution modeling, LTV-based segmentation, price elasticity — produces decisions that no amount of AI-generated content can improve on its own. A team that knows which channels actually drive revenue will outperform a team that can produce twice as much content every time, eventually. Tensor-house addresses the more important problem. It just isn't the problem that gets GitHub stars in mid-2026.
The last commit on June 22 suggests active maintenance. The content of those commits — which I can see from the repository's pulse — focuses on notebook refinements and dependency updates rather than new capabilities. It's a mature reference library that's being maintained rather than actively extended.
Awesome-AI: what 1.5% means for a list repository
re50urces/Awesome-AI (858 stars, last updated June 26) is a curated list of AI tools, not an executable product. I include it in this analysis for completeness and because its 1.5% growth rate over three weeks tells us something useful: discovery lists in the AI tools space still compound, but slowly. People add "awesome" repositories to their stars as reference bookmarks, not as tools they're actively deploying. The growth rate here is probably close to the ceiling for this category of repository, and it tracks roughly with the growth rate of interest in the AI tools category overall, rather than any specific tool momentum.
I've excluded Awesome-AI from the head-to-head analysis and the "what we'd use" recommendation below for the same reason I noted in our June 8 roundup: it's a discovery tool, not a deployment tool.
What I'd use and why
If I were rebuilding a marketing operation from scratch right now with what's available in this dataset, I'd sequence it this way:
Start with Affitor/affiliate-skills. The 14.5% growth rate in three weeks is the single clearest market signal in this dataset. Active maintainers, model-agnostic design, and a repository that was updated today — those three things together mean you're not picking a tool that's about to go dark. The affiliate-marketing focus in the name is limiting in how it sounds but not in what the skills actually cover. Content research, post writing, infographic generation, landing page deployment — those jobs exist in every content-driven marketing operation, not just affiliate programs. I'd use this as the execution layer.
Add short-video-factory if you're doing video at any volume. The scale signals on this repository are the most convincing in the dataset by a wide margin. 4,132 stars means thousands of people have evaluated this tool and decided it was worth saving. That's a different quality signal than a tool at 341 stars that might represent 341 people who starred it without using it. The documentation language barrier is real; budget time for it.
Layer in tensor-house selectively, if you have an analyst. The 0.4% growth rate doesn't mean this is a bad tool; it means it's not a beginner tool and the market knows it. If your team has someone who can run Python notebooks and you have enough historical data to train an attribution model, the intelligence this layer provides makes every decision made by the layers above it more precise. Skip it if you don't have that person. Don't skip it if you do.
Limitations
The usual: Reddit and Hacker News returned zero posts above threshold in this run, same as all three previous runs. Every number in this article is GitHub metadata — stars and commit dates — supplemented by the descriptions in the repository READMEs and my own prior knowledge of these tools' actual capabilities. I have not run all five tools in production for this article.
Star counts measure interest and discovery, not actual deployments or user satisfaction. A tool at 505 stars could be deployed by 50 production teams or by zero. The velocity data reduces but does not eliminate that uncertainty — a tool that's gaining stars quickly is at minimum being discovered, which is a prerequisite for production deployment. But the link between discovery and use is not guaranteed, and I can't verify it without community signals that have been consistently absent from these runs.
Pricing for Jasper AI, Copy.ai, and HubSpot AI — which are relevant context for why teams reach for open-source tools — has been inaccessible in every research run in this series. All figures from prior articles were flagged as unverified baseline estimates and remain so.
Bottom line
The velocity data is the story. The AI marketing tools market is not choosing analytics infrastructure over execution tools; it's doing the opposite, and the star counts are moving accordingly. Affitor/affiliate-skills added 14.5% in three weeks. tensor-house added 0.4%. The category is splitting between tools that make AI do the marketing work and tools that make analysts understand the marketing data — and right now, the doers are pulling ahead.
That doesn't mean the analysts' tools are wrong. It means that most teams adding AI to their marketing stack are starting with execution and deferring intelligence infrastructure until later. Whether that's the right order is a judgment call for each team. My view: it's a defensible starting point as long as you have a plan to add the intelligence layer before execution volume masks the fact that you're producing content without knowing what's actually working.
The three tools I'd prioritize — Affitor/affiliate-skills for execution, short-video-factory for video production, tensor-house if you have the analytical capacity — haven't changed from the June 8 roundup. What's changed is my confidence in that ordering, because the market has now spent three more weeks expressing a preference, and the preference is visible in the data.