dsh-subagent-model-policy
Per-session subagent model picker for DeepSeek Harness with immutable child bindings
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Per-session subagent model picker for DeepSeek Harness with immutable child bindings
Auto-adjust model reasoning effort per task: off/low/high/max with task classification, peak-pricing-period capping, countdown notices, and in-turn error escalation
DeepSeek Harness plugin to audit and correct llm-pi-ai model capability declarations by probing the endpoint itself: out-of-range maxTokens, contextWindow, reasoning levels, image input.
Grant official-style reasoning-effort selection to hand-declared pi-ai models (DeepSeek relays and the like) in the chat box
DSH Effort Router(模型分流):按每一轮请求的难度自动选择模型与思考强度——简单问题走便宜快模型,难题才叫强模型。规则判定零 token,灰区交给一次小模型裁定,图片档强度随难度升降(low/high/max)。每轮请求级覆盖,不改你的会话选择与默认模型。
Per-route context window, compaction threshold, and per-model thinking-effort controls for DSH models. · DSH 模型上下文窗口、压缩阈值与思考强度设置。
Cost policy for the DeepSeek Harness: per-call metering plus a local fuse that enforces budget, model and reasoning-effort limits before any token is spent.
dsh web Settings section (Custom models): subagent delegation allow-list, plus per-model image-input / reasoning-effort mapping.
DeepSeek Harness 推理强度与网关增强插件:自动为 llm-pi-ai 下所有模型补齐推理强度档位(off→max 全七档),并自动为 OpenCode Go 网关注入必需的 x-opencode-session 路由 Header,写入 settings 由 dsh 原生解析生效。
DeepSeek Harness web UI plugin: floating API token usage, context pressure, reasoning-effort control, and cost estimation with official peak/off-peak pricing.
Add configurable thinking-strength (reasoning effort) levels to custom llm-pi-ai providers in DeepSeek Harness
Replace the DSH Web reasoning-effort chooser with a compact animated FX slider that scales with effort level.