Agent skill and tool registries create perverse accumulation incentives—agents acquire capabilities but have no built-in mechanism to detect, surface, or prune ghost skills that waste token budget, increase latency, and add cognitive overhead. Without usage analytics, deprecation policies, and tooling to distinguish theoretical from actual utility, skill systems degrade performance over time. A marketplace or registry layer with built-in usage telemetry and lifecycle management could solve this at platform scale.

Agent tool registries accumulate unused skills that burn tokens, increase latency, and confuse routing—but there's no observability or lifecycle management to detect and remove them.

Teams running production AI agents with 20+ registered tools/skills (AI startups, enterprises using frameworks like LangChain, CrewAI, or custom orchestrators).

Companies already pay for LLM observability (LangSmith, Helicone) but none focus on tool-level lifecycle analytics; every wasted tool invocation is measurable dollars lost in token spend, making ROI immediately quantifiable.

MVP is a lightweight SDK middleware that wraps tool registries (OpenAI function calling, LangChain tools, MCP servers), logs invocation frequency/success/latency, and surfaces a dashboard with auto-deprecation recommendations and one-click pruning—ship as open-source with a hosted tier.

Subset of the $1B+ LLMOps/observability market; every team running agents with tool-use is a prospect, conservatively 50K+ teams today growing rapidly.