A full engineering bench for your coding agent: the work survives any session, and every done comes with proof. Runs on Claude Code, Codex CLI, Copilot CLI, Kiro CLI, and DeepSeek Harness.
Assembled context for DeepSeek Harness: a context tree over the session surface with per-node assemble modes (full / key / off), agent-authored presets, and a panel that decides what the model actually sees.
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.
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.
