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data-structure-protocol
k-kolomeitsev February 20, 2026
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Graph-based long-term memory skill for AI (LLM) coding agents — faster context, fewer tokens, safer refactors
Agent Definition
[](https://github.com/k-kolomeitsev/data-structure-protocol) [](LICENSE) [](https://python.org) []() []() []() # Data Structure Protocol (DSP) **The missing memory layer for AI-assisted development** --- ## The problem Your agent re-reads the same codebase every session. **DSP fixes that.** Every time you start a new task, your AI coding agent spends the first 5–15 minutes "getting oriented" — scanning files, tracing imports, figuring out what depends on what. On large projects this becomes a constant tax on tokens and attention. Context is rebuilt from scratch, every single time. DSP is a graph-based long-term structural memory stored in `.dsp/`. It gives agents a persistent, versionable map of your codebase — entities, dependencies, public APIs, and the *reasons* behind every connection — so they can pick up exactly where they left off. > **DSP is not another workflow framework.** It's the persistent structural memory layer that's missing from every AI coding workflow. --- ## Install **macOS / Linux:** ```bash curl -fsSL https://raw.githubusercontent.com/k-kolomeitsev/data-structure-protocol/main/install.sh | bash ``` **Windows:** ```powershell irm https://raw.githubusercontent.com/k-kolomeitsev/data-structure-protocol/main/install.ps1 | iex ``` **Codex:** ``` $skill-installer install https://github.com/k-kolomeitsev/data-structure-protocol/tree/main/skills/data-structure-protocol ``` --- ## What you get - **Agent stops re-learning your project every session** — structural context persists across tasks, sessions,
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