dsh-context-maid
Run the following command in DeepSeek Harness:
dsh plugin install Icstick/dsh-context-maid
Paste the following prompt into your AI chat to install this plugin:
Install the dsh-context-maid plugin in DeepSeek Harness (source: https://github.com/Icstick/dsh-context-maid); after installation, disable the official compaction-basic and tool-result-pruner as instructed, then restart dsh.
About this plugin
The most frustrating moment in a long session is rarely the conversation itself — it is the tool output and dead logs that bloat the context. A failed build spits three hundred lines of stderr, the same file gets read five times, an intermediate experiment result is already superseded. Worse, the requirement you stated thirty minutes ago, the goal you are halfway through, the decision you just confirmed — they are buried in that noise, and the moment compression triggers they risk being flattened into the same vague summary as the stderr they sit beside. dsh-context-maid exists to keep garbage and critical context apart so that neither drags the other down.
Its approach is a five-tier curation model (PIN / KEEP / SLIM / SWEEP / FOLD) applied to every message node. Tool output is trimmed in a content-aware way — errors keep their tail, JSON keeps its skeleton, logs keep head and tail — rather than blindly chopping a fixed number of characters from both ends. User-stated requirements, corrections, and the current goal are pinned in a no-loss-compression zone and re-injected into the summarisation prompt every cycle. Before anything is removed it is archived into the ACP ledger, and the compression summary itself carries a supersedes chain so you can always trace back. You can also point the summarisation step at a cheaper or local OpenAI-compatible model, keeping the main conversation on your preferred engine.
If your daily dsh workflow involves multi-turn debugging, batch experiments, or any pipeline heavy on tool calls, think of this plugin as a context maid: it tosses the trash, files what matters into a drawer, wipes the table — and that drawer is open to you whenever you need it back.
Use Cases
- Long sessions where tool outputs and failed logs accumulate, rapidly inflating the context window
- Compression triggers and user requirements plus in-progress goals get flattened into a vague one-line summary
- Multi-turn debugging where dead intermediate conclusions mix with valid decisions and become hard to tell apart
Best For
- Heavy dsh users whose workflows are tool-call intensive
- Engineers running multi-turn debugging or batch experiments where context bloat is constant
- Developers who care about context traceability and compression fidelity
Related Plugins
Memory layer for coding agents that indexes local session history and auto-recalls relevant context before edits, commands, and failures, with no manual search needed.
Traceable, searchable cross-session memory for AI agents that turns conversation knowledge into a typed knowledge graph and recalls relevant subgraphs instead of replaying full history, natively integrated with DeepSeek Harness.
Gives DSH AI cross-session long-term memory, to-do and skill management, plus multi-session orchestration, external AI delegation, and an infinite canvas that grows with you.
dsh-mnemon is a three-tier, pluggable, Agent-driven memory system for DeepSeek Harness, combining Runtime memory, Project Documents, and replaceable Memory Spaces with nine long-term providers.