DeepSeek Harness（DSH）第二大脑载入器：把有硬上限、带时间戳的脑快照（身份＋状态指纹＋未决项＋指针与边界）注入每个新会话；只读、零写入、fail-loud、零依赖。 | Second Brain loader for DSH — a bounded, timestamped snapshot of your local brain in every session prompt; read-only, zero-dependency.
System-1 decision models (Jev/Laya/Kev-class) as the governance layer for an LLM agent context lifecycle: growing association graph over session segments, relevance-gated recall with Trace-as-State ordering, and plan pre-ranking — measured in solve rate, cache hit/miss tokens, cost and latency. DSH plugin + harness-agnostic proxy.
See the context a Session submits to its model in the DeepSeek Harness Web UI: the system prompt, the tool schemas and every message, drawn as a grid of token squares.
Per-session Node REPL terminal in the DSH web UI: a session-local floating terminal (ghostty-web) bridged over WebSocket to an in-process node:repl that has the live agent Cordis Context (agent.ctx) in scope.
What will your agent's context cost before it runs? Audit instruction files, skills, MCP tool schemas and memory per source — with duplication, always-on versus on-demand costs, and a budget you can enforce in CI. Reads files and nothing else.
