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dsh-memory-loader

Memory Updated 2026.08.24

Run the following command in DeepSeek Harness:

dsh plugin install wuruihi/dsh-memory-loader

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install wuruihi/dsh-memory-loader in your terminal to install this plugin; the full source repository is available at https://github.com/wuruihi/dsh-memory-loader

About this plugin

DSH natively auto-loads only instruction file chains (AGENTS.md, CLAUDE.md); memory directories for global and project notes rely entirely on the model reading them voluntarily, which is probabilistic and degrades as context grows. dsh-memory-loader turns this into a deterministic, harness-level injection so that every new session reliably receives its two-level memory without hoping the model remembers to do so.

The plugin hooks into the agent/pre-step seam and injects four files in broad-to-specific order: global MEMORY.md, global daily date file, project MEMORY.md, and project daily date file. Each injected frame carries a fixed marker and scans session history for deduplication, so new sessions, resumes, sub-agents, and forks never receive duplicates. The byte budget defaults to 16384 bytes and is independent of the 64KB instruction chain; on overflow the least-specific file is dropped whole first, and the most-specific file is truncated last with a notice. Sensitive delimiters inside memory content are escaped to prevent frame escape, and any internal error degrades gracefully to skipping injection without interrupting the session. The plugin is strictly read-only and never writes back.

Ideal for developers who maintain a two-level memory convention in their DSH workflows and want guaranteed context injection from the harness rather than a prompt instruction to please read the notes. If your team already has a memory directory layout, this plugin ensures every new session starts with full context out of the box, no extra prompting needed.

Use Cases

  • Auto-inject global and project two-level memory on every new session without relying on the model to read the memory directory
  • Isolate memory per project in multi-project workflows with a byte budget independent of the instruction chain, dropping least-specific files first on overflow
  • Deduplicate via frame markers across sub-agent, fork, and resume sessions so memory is injected exactly once without repetition

Best For

  • Developers who maintain a memory directory convention in DSH and want guaranteed full context on every new session
  • Technical teams that prefer a harness-level memory mechanism over prompt instructions to reduce the risk of model forgetting
  • Engineers who need consistent cross-session memory without adding read instructions to every prompt