@deepseek-ai/dsh-trace
Embedded yiTrace plugin for DeepSeek Harness turns, model steps, and tool calls
85 results
Embedded yiTrace plugin for DeepSeek Harness turns, model steps, and tool calls
dsh plugin: auto-open the default browser as soon as the dsh web GUI server is listening — one-step startup for `dsh web`
DSH plugin: view OBJ/STL/STEP models in the web UI and translate them into a standardized, LLM-readable JSON
DevTools for DeepSeek Harness web: read-only Agent Runtime Trace — turn/step execution timeline, model latency (TTFT/decode), tool-call durations, retries, hook and turn end reasons. Metadata-first, durable events only, zero behavior modification.
Work Continuity for DSH — goal/decisions/checkpoints/next-steps as explicit WorkState, decoupled from generic memory
DeepSeek Harness plugin: learning agentic router — rule + UCB + LinUCB + k-NN experts under an EXP3 meta-selector, with a durable quality-reward flywheel, REAL model switching via the session request-header precedence chain, and STEP-LEVEL routing (v1.5.0)
Per-turn, per-step LLM cost metering for DeepSeek Harness: a costUsage session projection, a cost line under each message, and an LLM+web price auto-maintenance tool.
Dual-model routing policy: premium plans and rescues, local model implements ordinary steps
A configurable DeepSeek Harness host plugin that silently appends route-specific system-prompt guidance for GPT and DeepSeek steps.
DSH Web plugin: collapse consecutive tool-use and thinking steps between messages into a single bracketed, expandable item
Hot-switch agent presets (working modes) inside a running DSH session: mode changes are armed immediately and applied at the next step boundary through agentPresets.recompose, via the /mode slash command.
Fail-closed per-turn step, tool-call, and provider-token budgets for DeepSeek Harness
Plug-and-play proteomics analysis bundle for DeepSeek Harness: auto-managed R 4.4 runtime, step-wise traceable pipeline (normalize / PCA / batch / DEA / enrichment / GSEA), cached GO-KEGG annotation backgrounds.
DSH 插件安检机 — install-time static security auditor for DeepSeek Harness plugin bundles. Zero dependencies, zero install scripts, zero build step.
Pre-step skill router for DeepSeek Harness: rule-first task-to-skill matching that pours matched skill bodies only on high-confidence hits and stays silent otherwise. Companion to skill-bartender.
Token usage dashboard for DeepSeek Harness: cache, context, efficiency, per-step details, and cross-session comparison
Token usage analytics tab for the dsh web GUI: whole-log projections plus a per-turn/per-step breakdown
Git-backed long-term memory for DeepSeek Harness: a local .mem git repo (single main branch, immutable entries, structured commit messages) with mem_search / mem_read / mem_write / mem_delete / mem_replace tools and fully automatic legacy migration (no CLI needed; the CLI remains for explicit control), auto-managed by the root agent with zero manual steps.
First-run health check for DeepSeek Harness (dsh): verifies Node/pnpm/dsh toolchain, profile, API key, workspace and registry config, then prints actionable next steps. Zero runtime dependencies, read-only. CLI + agent-callable quickstart tool.
dsh-experience-flywheel · DSH 经验飞轮插件 — agent cross-session experience memory + dual-acceptance gates (auto query/inject via agent/pre-step middleware; exit-1 gate scripts)
DSH 自记忆插件 v3(P0 升级):在 v2(加权关键词/泛化/倒排索引/前提感知/同义词规范化)之上增加事件驱动自动采集(会话轨迹落盘)、pre-step 自动召回注入(<selfmem_memory> 围栏)、写路径可靠队列(失败入 pending 重放)、CJK 感知 token 预算。
Ponytail for DSH — the lazy senior dev. Live intensity switching (off/lite/full/ultra) with a toggle in the composer, injecting a minimal-code ruleset before every step to cut token usage.
DSH web plugin: a draggable ring-gauge dashboard pinned to the bottom-right that shows live model output speed (tok/s), with an expandable detail panel (per-step exact speed, TTFT, cache-hit rate, cache read/write, reasoning and input tokens, model, cumulative tokens).
Turn-outline sidebar tab for dsh-better-sidebar: fold a session into user turns (input + tool steps + output) with structured jump-back into the conversation | DSH 轮次轨迹侧边栏:按用户轮次折叠会话(输入+工具步骤+输出),一键定位回对话原位