Kanadego/dsh-heartbeat
An agent that wakes up on its own schedule: by default every 60 minutes, ±20% (48–72). It browses the interests you set, drops what it finds into a local pool of notes, and keeps a profile of what you like. When it has enough worth sharing it hands the session you linked a short package of notes — whether to speak, and which note to use, is that session's agent's call in the moment. Quiet hours, busy windows, a daily limit and a cooldown are decided in code; when the answer is no, the decision and expression phases after the gate call no model at all — browsing runs before the gate and is counted separately. Windows, Linux and macOS (Linux and macOS are minimal support).
$ dsh plugin --profile web add @kanadego/dsh-heartbeat$ dsh plugin --profile web add github:kanadego/dsh-heartbeatHealth breakdown
75 / 100Score reflects license, community signals, documentation and distribution. It is not a code audit — review the source before installing.
Security
Key metrics
Related
Related plugins
Adds /pua to steer the agent to try another approach and verify results before claiming completion, with 15 company styles, persona modes, global and per-conversation settings, and an optional loop that continues until a verification command passes, the iteration limit is reached, or the user cancels.
Cross-agent, local-first persistent memory plugin for DeepSeek Harness (DSH), powered by Mnemon. It shares long-term memory across Mnemon-enabled agents and adds runtime memory, searchable project documents, semantic recall, knowledge graph, and a Sidebar UI.
Adds a summonable domain-expert roster that runs as subagents while the parent session keeps the task and final answer.
Layered memory for DSH whose agent-facing surface is 10 tools: high-privilege writes (memory_add, memory_delete, memory_import), rumination controls (memory_ruminate, _cancel, _status), and a memory graph (memory_search_graph, memory_expand_graph_node), built over an L0-L3 distillation pipeline that recalls and injects relevant memories before each model step.
White-box AGI architecture exploration: metacognition (self-cognition loop), continual learning (knowledge flywheel), world model (condition space, spatiotemporal memory graph), self-improvement (bootstrap discipline), zero-LLM white-box pipeline, and auditable trust guardrails.
Shared real browser for DSH: a native Electron window the human can watch and take over, driven by the agent over CDP with 20 browser_* tools (open/snapshot/execute/fill/screenshot/download/auth), per-task session isolation, cookie persistence, CAPTCHA detection; self-hosts on plain dsh web without a desktop shell.