Stop hoarding random agent skills — assemble reusable AI workflow collections

Engineering, product, and SRE teams increasingly waste time stitching together one-off agent skills, scripts, and API calls that are brittle, undocumented, and duplicated across orgs; this fragmentation slows delivery for an estimated 8 million addressable teams and drives maintenance costs that compounds with every bespoke automator. The problem is acute where LLMs are already used to call tools: teams need composable, discoverable workflows rather than hoarded snippets, and decision-makers are willing to pay for reliable automation (we model a $4K ACV per team, implying a $32.0B TAM). You could build a curated marketplace and orchestration layer that sells reusable AI workflow collections: versioned modules (auth, retries, observability), protocol adapters, CI/CD integrations, access controls, and test harnesses, plus an SDK and templates so teams can compose well-formed agent behaviors instead of reinventing them. The product should emphasize provenance, security scanning, and one-click deployment to common runtimes, with analytics and billing to support both internal adoption and a subscription marketplace. This market is attractive now because LLMs calling tools are mainstream, composable software practices are favored over monoliths, and enterprises are accelerating automation initiatives; those trends give a timing advantage to a product that reduces time-to-production for agent-driven workflows. Strengths include tapping an underserved operational need and the potential for network effects from a high-quality catalog, while challenges include building sufficient curated content, overcoming trust and governance hurdles, and competing in a medium-competition landscape where integration and enterprise compliance will be decisive.