dsh-geo
★ 0dsh-geo生成式引擎优化(GEO)DeepSeek Harness plugin for SEO, GEO and AEO analysis of Markdown knowledge bases.
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:winyh/dsh-geo최고의 DSH 플러그인을 만나보세요
1213개 결과
대화 기억, 지식 검색 및 컨텍스트 관리 플러그인을 찾아보세요.
dsh-geo生成式引擎优化(GEO)DeepSeek Harness plugin for SEO, GEO and AEO analysis of Markdown knowledge bases.
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:winyh/dsh-geodsh-geometry-knowledge几何论(共扼谱几何 CSG)知识库插件 for DeepSeek Harness:以 Markdown 文章为唯一真源,程序自动重建 BM25 索引、类型化主张依赖图与修复工作单并做一致性审计,附 869 条真理层与 SymPy 公式验证。开箱即用的离线知识库(31 个 geo_* 工具),零运行时依赖。Conjugate Spectral Geometry knowledge base plugin for DeepSeek Harness: offline BM25 retrieval, typed claim graph, consistency audit and SymPy formula verification.
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:sdoygb/dsh-geometry-knowledgedsh-global-memoryDSH 全局记忆插件:侧边栏「全局记忆」页,直接查看/编辑 ~/.dsh/AGENTS.md 与 ~/.dsh/memory/*.md。Host 半边注册 /api/dsh-memory 路由(回环限制),client 半边注入侧边栏入口与中心面板。热插拔挂载,不改 DSH 源码。
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:xujiping/dsh-plugins#1adc7e76acdbef227d539c496cca5adf7a53d43e&path:packages/dsh-memorydsh-goodmemoryAutomatic cross-session GoodMemory recall and writeback for DeepSeek Harness
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:hjqcan/dsh-goodmemorydsh-governed-memoryGoverned long-term memory core and DSH technical-preview adapter contracts.
dsh-harmonyos-arktsHarmonyOS NEXT / ArkTS development skills for DeepSeek Harness — ArkTS/ArkUI coding knowledge and ArkTS code review.
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:spike-faye-lei/dsh-harmonyos-arktsdsh-hermes-memoryHermes-style persistent memory for DeepSeek Harness (DSH) — a faithful port of the hermes-agent MEMORY.md / USER.md mechanism: bounded dual-bank memory, one `memory` tool (add/replace/remove/batch), frozen-snapshot injection, nudge reminders. Zero external dependencies, zero LLM cost.
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:isheng-eqi/dsh-hermes-memorydsh-hindsightOfficial-grade DeepSeek Harness memory plugin backed by Hindsight (vectorize-io). Add /hindsight commands and model tools that recall, remember, inspect and forget through a Hindsight memory bank.
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:Ryu6Zero/dsh-hindsightdsh-hindsight-memoryHindsight long-term memory for DeepSeek Harness: auto-recall context injection before each agent step (agent/pre-step) and auto-retain of each finished turn (session/event).
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:jackyytche/dsh-hindsight-memorydsh-honcho-memoryDSH plugin: honcho long-term memory tools (memory_store / memory_search) over a self-hosted Honcho v3 backend
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:kingcharleslzy-ai/dsh-honcho-memorydsh-hot-memoryProject the Mnemon runtime memory files (USER.md / MEMORY.md) into every session's system prompt as one lazy section, independent of dsh-mnemon's lifecycle gate.
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:Ln1m/dsh-hot-memorydsh-humanized-deepseek-maidGives the DeepSeek Harness agent a configurable humanized whale-girl maid persona (address, self-name, speaking mode), an immersive no-plugin-meta rule, and a lightweight layered memory with query-triggered recall.
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:loonai321/dsh-humanized-deepseek-maiddsh-hypatiaHypatia skills for DeepSeek Harness: knowledge-graph query skill plus automatic conversation-memory bridge. Requires the `hypatia` CLI on PATH.
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:tkliuxing/dsh-hypatiadsh-hypercompactDeterministic, zero-LLM, byte-budget context compaction for DeepSeek Harness, with byte-exact recall of compacted history.
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:mrbeandev/dsh-hypercompactdsh-ihow-memoryInstall iHow Memory as a local-first shared memory plugin for DeepSeek Harness.
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:iHow1/dsh-ihow-memorydsh-ima-copilotDSH IMA Copilot 插件:通过 ima_ask 深度问答腾讯 IMA 知识库,含 Web 配置界面
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:onclaw-dev/dsh-ima-copilotdsh-ima-kbTencent ima (ima.copilot) knowledge-base tools for DeepSeek Harness: list, cross-knowledge-base fan-out search, browse, URL import, file upload and notes, over the official ima OpenAPI.
dsh-industry-graph-mcp零依赖、本地优先的 A股 产业链 / 申万行业 / 概念板块 知识图谱 MCP server —— 查询个股所属行业、同业竞品、产业链上下游、概念成分与交叉选股。无需 API key、无网络依赖。
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:helibeiqi/dsh-industry-graph-mcpdsh-infinite-contextDeepSeek Harness plugin: multi-tier memory management, semantic retrieval, structured memory, and model-context awareness for infinite context.
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:chocobo77/dsh-infinite-contextdsh-instruction-memoryUser-only instruction memory for DSH: long-term instructions maintained in the settings UI and auto-injected into every conversation — the model has no write access. 指令记忆:只由用户在设置页维护的长期指令,自动注入此后每轮对话;模型没有任何写入口。
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:HERO476/dsh-instruction-memorydsh-intelhub第一个 zvec 原生情报站:刷到的信息自动沉淀(采集/文件夹/网页/笔记),agent 语义+关键词混合检索带出处,零守护进程、零 API key、文档不出本机;支持 Obsidian 反哺与定时自动化
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:IKEASven69/dsh-intelhubdsh-jev-memoryTyped, auditable long-term memory for the DeepSeek Harness: a turn-end write hook, a judgement layer (Jev or a deterministic fallback), a local store, and per-session recall injection.
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:lrqiisrom/dsh-jev-memorydsh-k12-substrateK12 capability substrate for DeepSeek Harness: 143 objectively-decidable capability anchors and 6,091 list items extracted from China's MOE 2022 curriculum standards, with local-only learner profiles
$ npx -p @deepseek-ai/dsh dsh plugin --profile web add github:qiuyiwu1989-star/dsh-k12-substratedsh-keyword-contextDeepSeek Harness (DSH) plugin — inject context when user input or model output hits configured keywords, with toggleable/configurable rules and model-editable memory.
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