dsh-plugin-meta-memory
Structured long-term memory system for DeepSeek Harness.
What it does
Teaches the agent how to organize memories using a structured system:
- Units: Work categories named
verb-modifier-noun.unit
- Records: Each memory has a brief (quick scan) and full (detailed) version
- Index: Self-maintained global storyline
- Auto-injection: Relevant briefs are injected into context automatically
Install
dsh plugin --profile your-profile add dsh-plugin-meta-memory
How it works
Memory structure
.meta-memory/
├── index.md
└── units/
├── debug-auth-module.unit/
│ ├── 0.token-refresh-fail.brief.md
│ └── 0.token-refresh-fail.full.md
└── read-deeplearning-paper.unit/
├── 0.transformer-attention.brief.md
└── 0.transformer-attention.full.md
Brief format (injected into context)
# token-refresh-fail
JWT token refresh returns 401 due to timestamp offset calculation error.
Full format (loaded on demand)
# token-refresh-fail
## Context
Auth module was failing on token refresh after 1 hour.
## Finding
expiresAt calculated using milliseconds instead of seconds.
## Evidence
src/auth/refresh.ts:42 - Math.floor(Date.now() / 1000) fixes it.
## Relevance
Any JWT/token related debugging in the future.
CLI tool
mm init # Initialize memory directory
mm list # List all units and records
mm read <unit> # Read all briefs in a unit
mm search <query> # Search briefs by keyword
mm index # Show the index
Configuration
- id: meta-memory
name: dsh-plugin-meta-memory
config:
memoryPath: '~/.dsh/meta-memory'
enableInjection: true
enableSkill: true
maxBriefBytes: 4096
License
MIT -- YYTbit
