Dynamic Context Discovery in Cursor

Key Points

As AI agents improve, providing fewer static details upfront lets agents dynamically retrieve context themselves—improving token efficiency and response quality
Files as a universal primitive: Cursor writes tool outputs to files that agents can progressively read (e.g., using tail), instead of truncating long responses
Chat history files: When context windows fill up and get summarized, agents can reference history files to recover crucial details lost in compression
Agent Skills Standard: Skill descriptions stored as files, discoverable via grep/semantic search rather than all pre-loaded
MCP tool optimization: Tool descriptions synced to folders; agents look up tools when needed, not statically. A/B testing showed 46.9% reduction in total agent tokens
Terminal outputs synced to filesystem: Agents query them dynamically instead of requiring manual copy-paste

File system is one of the best representations of LLM memory—it's the most intuitive interface for models
This validates the CLAUDE.md/MindCapsule approach: filesystem as the state layer, agent discovers context as needed
Dynamic discovery > static context stuffing. Let the agent pull what it needs.
The "files everywhere" pattern keeps showing up: Lee Robinson's post, this post, my own experience. Strong signal.

How does Cursor handle very long terminal outputs that exceed reasonable file sizes?
What's the semantic search implementation for skill discovery? Embeddings?
Could MindCapsule benefit from an explicit "skills" folder for Claude Code to discover?
How does dynamic context discovery interact with context summarization quality?

Coding Agents and Complexity Budgets - Same insight: direct file access > abstractions for agents
Agent.md as the Future of Software - File system as state layer, agent as runtime
Embedding, Indexing & Retrieval - Semantic search for skill/tool discovery
Cloud Infrastructure - Simpler primitives win with agents