dsh-advisor
dsh plugin bundle porting the rpiv advisor subsystem: an on-demand zero-parameter advisor() tool that forwards the full session to a separately-configured reviewer model and returns a plan, correction, or stop signal.
263 results
dsh plugin bundle porting the rpiv advisor subsystem: an on-demand zero-parameter advisor() tool that forwards the full session to a separately-configured reviewer model and returns a plan, correction, or stop signal.
dsh plugin that turns the iterate skill into an autonomous closed-loop harness with quality command center and experience bank (v3.3). Features: plan -> parallel review xN -> atomic fixes -> validate -> loop -> auto-stop, plus dry-run pure-review mode, qu
DeepSeek Harness tools for Feishu Project (Meego): query, plan, create, update, and transition work items.
DSH-native multi-runtime baseline, ablation, and reproducible evaluation control plane
Natural-language driven dynamic assembler for DeepSeek Harness (dsh): discovers plugins at runtime (official-first, third-party optional), generates assembly plans, and loads them via Cordis — with built-in security audit gates for unofficial plugins.
OpenGame browser-game planning and building Skill for DeepSeek Harness
RALPH cognitive-loop plugin for DeepSeek Harness: Reflect → Assess → Learn → Plan → Handle autonomous code-generation and error-repair state machine with a hard cycle cap and event-sourced observability.
DSH web plugin: takes over the plan review card and adds an execution-model selector at its bottom — pick which model will carry out the plan; on approve the plugin switches the session model first, then answers the review so execution runs on the chosen model.
A Claude Code-style coding agent for the terminal, composed on the DeepSeek Harness (dsh): interactive TTY surface, plan mode, approvals, custom commands, and session management over the dsh plugin runtime
Correctly activate DeepSeek v4 flash on the Volcano Ark plan API for DeepSeek Harness: default config injection (ark provider + reasoningEfforts + reasoningEffort=max) plus an ark_plan_doctor self-check tool.
Education toolkit for DeepSeek Harness agents: lesson-plan skeletons, quiz validation, question scaffolding, analytic rubrics, grading sheets, study plans, class statistics, flashcards and readability levels
Plan and execute Atlas Cloud image, video, audio, and 3D workflows in DeepSeek Harness.
Visual plan mode for DeepSeek Harness: structured plan.json + plan.md, an editable React Flow canvas, comments, plan diff, versioned revisions, and reliable write-back to the agent.
Host-plane kernel mesh for DeepSeek Harness: kimi/grok/codex/minimax kernel adapters, L2 subagent recipes, and kernel tools.
Minimalist, native-adaptive DeepSeek Harness balance widget: DeepSeek official account balance + OpenCode Go plan consumption ring in the sidebar footer, frosted-glass hover card, in-UI restart. · 极简原生风 DSH 余额插件:侧边栏常驻 DeepSeek 余额与 Go 套餐消耗圆环,磨砂悬浮卡,一键重启
Multi-provider usage monitor for DeepSeek Harness: per-provider balance / token-plan usage card in the sidebar footer (above the Settings button) plus a「用量监控」settings section for per-provider special config (cookies, API keys), persisted in settings.yaml.
DeepSeek Harness plugin: LLM model selection & deployment analysis (deployability, VRAM, TTFT, latency, throughput, power for 38 models × 20 GPUs/NPUs)
Bridges the four z.ai GLM Coding Plan MCP servers into DeepSeek Harness via the in-box @deepseek-ai/dsh-mcp-client: vision (GLM-4.6V stdio via npx @z_ai/mcp-server), web reader, web search prime, and zread repo MCP (streamable-http at open.bigmodel.cn)
DeepSeek Harness plugin: configurable keyboard shortcuts (settings UI + input history + approval/plan hotkeys)
常驻 API 用量监控:DeepSeek/GLM Coding Plan/OpenCode Go/火山 Coding+Agent Plan 余额与配额,会话树 token 与花费(侧边栏入口 + 独立浮动窗)。
Selection-first explanations, follow-ups, memories and annotations for DeepSeek Harness web sessions.
计划与任务拆解:把目标拆成可验证的小任务、排序依赖、估算工作量、追踪进度。受 addyosmani/agent-skills(88k★ MIT)启发。
销售目标拆解
Local-first AI teaching workspace for DeepSeek Harness — context, unit packages, assessment diagnosis, and next-lesson feedback loops