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11 results

Bundle

dsh-continual-evolve

Continual self-evolution plugin for DeepSeek Harness: versioned, auditable, rollback-safe harness state (prompt notes, memories, skills, subagent specs) refined from session trajectories.

ZK-Andy14

Bundle

Execution-discipline plugin suite for DeepSeek Harness: anti-stuck retry interception, forced reflection, todo discipline, memory dedup, and self-evolving skills. Zero dependencies, never touches DSH source.

Classicoke6

Bundle

dsh-proactive

自主智能多模型协同调度系统 — 主动感知、自主决策、多模型并行、质量反思自愈、经验沉淀进化

beijingwahw5

Bundle

dsh-phoenix

Graceful restart + client auto-reconnect + cross-restart goal continuation for DeepSeek Harness (dsh). After a plugin update dsh-phoenix restarts dsh at a safe idle boundary, keeps the browser page connected, and resumes a running goal so a task can keep evolving across restarts.

StvLi3

Bundle

dsh-desk-rsi

Recursive Self-Improvement (RSI) engine for DeepSeek Harness: a bounded perceive → integrate → verify → repair → propose loop that keeps a downstream desktop app in sync with the fast-moving upstream harness. AI for AI — DSH maintains DSH.

huchunlinnk3

Bundle

dsh-auto-evolve

Self-evolving plugin for DeepSeek Harness: observes agent behavior, proposes improvements to its own skills/policies via LLM, validates them in a sandboxed trial agent, and applies versioned mutations with rollback.

lispking3

Bundle

dsh-kirocrew

KiroCrew bridge for DeepSeek Harness: let your dsh agent delegate to a persistent, self-evolving KiroCrew workspace over ACP (JSON-RPC 2.0 over stdio).

zoahdev2

Bundle

dsh-computer-use-vision

Windows computer-use capability for DeepSeek Harness: screenshot → vision model → simulated mouse/keyboard input, with self-evolving knowledge base.

xuanyuanluoxue1

Bundle

dsh-evolution-lab

Proof-carrying Skill self-evolution for DeepSeek Harness.

JayDong91301

Bundle

dsh-daoing-memory

Self-evolving memory for DeepSeek Harness (DSH): earned experiences, diary/fact semantic memory, concern tracking, and an append-only audit ledger.

daoing1

Bundle

dsh-skill-optimization

把技能当可训练参数：像训练神经网络一样训练 agent 技能（epochs/batchsize/learning rate/验证门禁，但不碰模型权重）——rollout→reflect→aggregate→select→update→evaluate 循环、候选编辑仅在严格改善 held-out 验证分时接受、文本学习率预算、零推理时模型调用、部署紧凑 best_skill.md。受 microsoft/SkillOpt（MIT）启发。

satan9394