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\n\n**中文** | [English](README.en.md)\n\n
\n\n**面向 DeepSeek Harness (dsh) 的生物信息学「许愿式分析」插件**\n\n> **dsh bio analysis** · **dsh biology analysis** · **deepseek harness bioinformatics** · Biopython · sequence analysis · genomics\n>\n> 说人话，出结果。用户用自然语言描述生物学分析需求，dsh 的 agent 自动完成分析。\n\n**下载安装即用** —— 无需用户安装 Python 或 Biopython，插件首次运行自动引导完全隔离的 Python 环境。\n\n---\n\n## ✨ 特性\n\n| 特性 | 说明 |\n|------|------|\n| 🪄 **许愿式分析（Wish Coding）** | 说人话就能分析：*\"这条序列的 GC 含量和 EcoRI 酶切位点？\"* |\n| 🧩 **全功能覆盖** | `bio_python` 执行器可运行任意 Biopython 代码（比对、PDB、Phylo、motif、BLAST…），配合 22 个领域/研究 skill 配方 |\n| ⚡ **高频语义化工具** | 57 个固定参数工具（GC 含量、翻译、限制酶、k-mer、文件 IO、BLAST、多序列比对、系统发育树、Entrez 检索、通路富集、PubMed 文献、参考基因组、Addgene 质粒库检索、UniProt 蛋白知识库、内含子外显子、点阵图、树比较、RNA 二级结构、单细胞 QC、出版级绘图、机器学习、DNA 设计、Primer3 引物/多约束 DNA 优化/克隆模拟/SBOL 标准化/生产包络线/CRISPR 向导设计/编辑验证/合成可行性检查、基因回路编译/动力学仿真、差异表达/GSEA）+ 5 个执行器/元工具（bio_python / bio_env / bio_log / bio_memory / bio_goal）——省 token、输出稳定、参数有校验 |\n| 📦 **零安装** | 自动下载隔离的 Python 环境（uv + venv + Biopython 绘图栈）到 `$DSH_HOME/dsh-bio-genie/`，不污染系统 |\n| 🇨🇳 **网络自动适配** | 默认直连官方源，任一环节失败自动切换国内镜像（uv→清华 PyPI、CPython→npmmirror、PyPI 包→清华镜像），无需任何配置 |\n| 🛡️ **环境隔离** | Python 子进程以 `-I`（isolated）模式运行，不受宿主 PYTHONPATH 污染 |\n| 🔁 **自愈执行（ACR）** | bio_python 失败返回 `needs_repair` 信号 + stderr，模型自动修复重试（最多 3 次），失败即如实报告 |\n| 📜 **透明性日志** | 每次代码执行/工具调用异步记 JSONL 日志（哈希/预览/耗时），`bio_log` 可回溯任何一次分析；日志自动 30 天轮转清理 |\n| 🧬 **科学严谨性约束** | persona 强制「生物学结论必须可溯源到工具输出」，纯推断标注 [推断-未验证] |\n| 🧠 **会话记忆** | 成功代码模式 + 错误→修复经验自动沉淀（本地 JSON），`bio_memory` 查询，越用越聪明 |\n| ⚙️ **设置面板** | dsh 设置面板（⚙️ 齿轮）侧栏「BioGenie」菜单——标签页：总览（包元信息/配置默认值）、Skill 模块（50 个条目按主 skill/领域/研究/协议/指南分组）、Python 环境（venv 包列表）、工具试运行；同实例安装 dsh-bio-gem / dsh-bio-graft / dsh-bio-galatea 时还分别显示「代谢建模」「基因编辑设计」「蛋白设计」只读五态面板（**蛋白设计页含「模型目录」管理：可把 galatea 大模型安装到其他磁盘**） |\n| 📚 **协议知识库** | 19 个高频任务协议（质控/比对/BLAST/克隆/建树/结构/富集/出版级绘图/坐标系统/统计检验/差异表达/GSEA/NGS 流程…），每个含可执行代码模板 + 常见坑，随插件打包 |\n\n---\n\n## 📦 安装\n\n本插件已发布为 npm 包 `@dsh-bio/dsh-bio-genie`，使用 dsh 官方标准的 `dsh plugin` 命令安装\n（下面统一写 `npx -y @deepseek-ai/dsh`：**无需全局安装 dsh CLI，只要机器上有 Node/npm**。\n若你已全局装过 dsh CLI，把 `npx -y @deepseek-ai/dsh` 整体换成 `dsh` 即可）：\n\n```sh\n# 方式一：从 npm 安装（推荐，安装预构建代码）\nnpx -y @deepseek-ai/dsh plugin --profile web add @dsh-bio/dsh-bio-genie\n\n# 方式二：从 GitHub 安装（拉取源码；本插件为纯 ESM 无构建步骤，可直接加载）\nnpx -y @deepseek-ai/dsh plugin --profile web add github:moonbowterfly/dsh-bio-genie\n\n# 方式三：从本地目录安装（本地源码）\nnpx -y @deepseek-ai/dsh plugin --profile web add ./dsh-bio-genie\n\n# 方式四：从本地 tarball 安装（npm pack 产物）\ncd /path/to/dsh-bio-genie && npm pack\nnpx -y @deepseek-ai/dsh plugin --profile web add ./dsh-bio-dsh-bio-genie-<版本>.tgz\n```\n\n`--profile ` 是**必填选项**（不传报 `required option '--profile ' not specified`），Web 端固定用 `web`。\n\n**引擎兼容**：0.1.x 侧经 dsh **0.1.5-rc.2** 完整兼容核验（v0.1.3-alpha.2 → 0.1.5-rc.2 全量变更核对：零适配命中）与实机验证（全新安装 + 工具注册 + Python 引导 + 设置面板 + 真实会话）。**0.2.0+ 侧（含官方桌面端）已适配**：peer 声明放宽为 `^0.1.0-rc.6 || ^0.2.0-rc.1`（0.2.0-rc.2 上直接安装）；agent preset 改由插件自带声明提供（见「精灵专家人设」一节）；双端实机验证通过（0.2.0-rc.2：preset 自动出现 + 工具全量注册 + skill 动态定位；0.1.5-rc.2：启动正常、preset 行自动跳过）。\n\n安装后重启 dsh web 服务，插件即被加载。首次启动时插件会在后台自动引导 Python\n环境（下载 uv → Python 3.12 → venv → biopython，约 1-2 分钟），之后秒级就绪。\n\n验证插件层是否生效（无需启动）：\n\n```sh\nnpx -y @deepseek-ai/dsh --profile web --dump-config # 输出中应包含 \"# == dsh-bio-genie\" 层\n```\n\n### 安装后批准构建脚本（0.1.x 必需；0.2.0+ 可选）\n\n**两代引擎的 preset 供给机制不同：**\n\n- **DSH 0.2.0+（含官方桌面端）**：preset 由插件自带声明（`cordis.patch.yml` 中的\n `@deepseek-ai/dsh-agent-preset` 行）自动提供——**装完即有「生物基因精灵」，无需任何后置动作**；\n 构建脚本批不批准都不影响（postinstall 的复制动作在此仅是冗余保险）。\n- **DSH 0.1.x**：preset 走 `postinstall` **把文件复制到 `~/.dsh/.agent-presets/bio-genie/`**；\n pnpm v11 默认拦截 lifecycle 脚本，**批准一次后**才会复制。\n\n0.1.x 上安装时若打印：\n\n```\n[ERR_PNPM_IGNORED_BUILDS] Ignored build scripts: @dsh-bio/dsh-bio-genie@0.6.37\n```\n\n表示文件式副本没装上（0.1.x 预设选择器里没有「生物基因精灵」）。在 profile 目录批准一次即可：\n\n```sh\ncd ~/.dsh/profiles/web && pnpm approve-builds # 或 pnpm approve-builds --all 全批准\n```\n\n批准后 pnpm 会把确切的键写进 `~/.dsh/profiles/web/pnpm-workspace.yaml` 的 `allowBuilds`。\n注意键会随安装方式变化：**从 GitHub 源安装时键含 commit 哈希，仓库每更新一次 commit 就得重新批准一次**；\n从 npm 安装的键是版本号，只在升版本时变；**从本地 tarball 安装的键含 tgz 文件路径**。\n\n**免交互批准（脚本化 / 远程场景，2026-09-19 实测）**：不用开交互式的 `pnpm approve-builds`——\n直接编辑 `~/.dsh/profiles/web/pnpm-workspace.yaml`，把 `allowBuilds` 下对应键的值从\n占位符 `set this to true or false` 改成 `true`，然后**重跑一次同一条 `dsh plugin add` 命令**，\npnpm 会自动补跑被拦的 postinstall（输出 `[install-preset] preset installed → ...`）。等价流程也可用\n`pnpm approve-builds` 完成（它写的就是同一个键）。\n\n兜底：`node scripts/install-preset.js` 手动复制（见下文「手动安装 / 卸载」）。\n\n### 故障排除：profile 已有本地包导致 pnpm 校验失败\n\n若你的 profile 里已装过**不在 npm registry 的本地包**（如皮肤插件），`dsh plugin add`\n触发的 pnpm 全量校验可能报 `ERR_PNPM_FETCH_404`。此时可手动挂载（已验证可行）：\n\n```bash\nmkdir -p ~/.dsh/profiles/web/node_modules/@dsh-bio/dsh-bio-genie\ncd /path/to/dsh-bio-genie\ncp -r src index.js cordis.patch.yml package.json skills prompts python docs \\\n README.md README.en.md LICENSE THIRD_PARTY_NOTICES.md \\\n ~/.dsh/profiles/web/node_modules/@dsh-bio/dsh-bio-genie/\n```\n\n然后在 `~/.dsh/profiles/web/package.json` 中：\n- `dependencies` 添加：`\"@dsh-bio/dsh-bio-genie\": \"file:.../dsh-bio-genie\"`\n- `dsh.profile.bundles` 数组添加：`\"@dsh-bio/dsh-bio-genie\"`\n\n最后重启 dsh web 服务。\n\n---\n\n## 🛠 工具总览\n\n### 执行器与元工具\n\n| 工具 | 功能 |\n|------|------|\n| `bio_python` | 运行任意 Biopython Python 程序（比对/PDB/Phylo/motif/复杂流程/自定义分析/出版级绘图），覆盖 100% 长尾需求 |\n| `bio_env` | Python 环境诊断 / 重建 |\n| `bio_log` | 执行日志回溯（bio_python 代码哈希/预览/耗时 + 工具调用记录） |\n| `bio_memory` | 会话记忆查询（成功代码模式 / 错误修复经验，越用越聪明） |\n\n### 语义化工具（高频稳定操作）\n\n| 工具 | 功能 | 典型触发词 |\n|------|------|-----------|\n| `bio_seq_analyze` | 长度 / GC% / 反向互补 / **六框翻译**（正负链）/ 分子量 / 蛋白 AA 组成 / 密码子统计（`codon_stats=true`） | GC含量、序列特征、翻译、密码子适应 |\n| `bio_seq_translate` | DNA→蛋白翻译（可指定密码子表） | 翻译、蛋白序列 |\n| `bio_seq_gc_skew` | GC skew（复制起点识别） | 偏斜、复制起点 |\n| `bio_seq_find_orf` | 最长开放阅读框 | ORF、编码区 |\n| `bio_seq_kmer` | k-mer 频率统计 | k-mer |\n| `bio_seq_io_read` | 读 FASTA/GenBank（UTF-8/GBK 自适应） | 读取fasta、解析文件 |\n| `bio_seq_io_write` | 写序列文件 | 写fasta、保存序列 |\n| `bio_seq_restriction` | 限制酶切位点（CommOnly 默认 / all 可选；`detail=false` 未指定酶仅计数、指定酶 ≤10 坐标，`true` 全量） | 限制酶、酶切位点 |\n| `bio_blast_search` | 远程 BLAST（NCBI qblast：blastn/blastp/blastx → 命中 accession/e-value/score/一致性） | BLAST、同源性搜索 |\n| `bio_msa` | 多序列比对（clustalw/muscle；返回 Clustal+FASTA 比对、共识序列、保守性统计；缺二进制返回安装提示） | 多序列比对、MSA |\n| `bio_phylo_build` | 系统发育树构建（nj/upgma → Newick；可接 bio_msa 的 alignment_fasta 输出） | 进化树、建树、NJ |\n| `bio_entrez_search` | NCBI 检索（esearch+esummary；db=gene 返回基因元数据摘要：全名/染色体位置/别名） | NCBI、检索基因、查基因信息 |\n| `bio_entrez_fetch` | NCBI 取序列 | 下载序列 |\n| `bio_enrichr` | 通路/GO 富集分析（基因符号列表 → p 值排序条目；GO/KEGG/Reactome/MSigDB 等库） | 富集分析、通路、GO、KEGG |\n| `bio_pubmed_search` | PubMed 文献检索（PMID/标题/期刊/作者/DOI） | 查文献、PubMed |\n| `bio_pubmed_abstract` | 按 PMID 取结构化摘要（标题/摘要全文/作者/日期/DOI） | 读摘要、PMID |\n| `bio_ref_genome` | 参考基因组 assembly 信息（Ensembl：assembly 名/染色体/下载目录） | 参考基因组、基因组版本 |\n| `bio_plasmid_search` | Addgene 质粒库检索（13,000+ 质粒：ID/名称/用途/沉积者/文献/插入片段；实时抓取公开目录页） | 质粒库、找质粒、Addgene |\n| `bio_plasmid_info` | 按 Addgene ID 取完整元数据（30+ 字段：抗性/拷贝数/启动子/生长菌株/插入片段/许可） | 质粒详情、质粒抗性 |\n| `bio_uniprot` | UniProt 蛋白知识库（`mode=entry/ptm/xref/pathway/sequence/search`：功能注释/PTM 位点+证据码/100+ 库交叉引用/通路/FASTA） | UniProt、PTM、蛋白注释 |\n| `bio_seq_introns` | 内含子-外显子结构 + 剪接位点（GenBank 多段 CDS → 外显子/内含子坐标、GT-AG 判定） | 内含子、外显子、剪接位点 |\n| `bio_seq_dotplot` | 序列点阵图（滑窗一致性矩阵 → 相似区段/重复/重排 + 300 DPI PNG） | 点阵图、dotplot、共线性 |\n| `bio_phylo_compare` | 系统发育树比较（Robinson-Foulds 距离 + 归一化 + 拓扑差异明细） | 树比较、RF 距离 |\n| `bio_rna_fold` | RNA 二级结构预测（ViennaRNA：MFE 结构/ΔG/碱基对/集合自由能 + 结构示意图；首次调用自动装） | RNA 二级结构、折叠、MFE |\n| `bio_sc_qc` | 单细胞 RNA-seq 质控（scanpy：QC 指标 → MAD 过滤 → 出图 → 保存 h5ad；首次调用自动装） | 单细胞、scRNA-seq、QC、h5ad |\n\n### 序列类型自动判断\n\n`bio_seq_analyze` 的 `seq_type` 默认 `auto`，自动识别三类序列：\n- 含 U 无 T → **RNA**\n- 含 IUPAC 模糊碱基（R/Y/S/W/K/M/B/D/H/V）、X（未知/修饰碱基）、比对 gap 字符（-/.）→ **DNA**（引物/探针/SNP/比对结果安全）\n- 出现非核酸字母 → **蛋白质**\n\nX 与 gap 在翻译时按未知碱基处理（Biopython 标准行为），含 X/gap 的序列不会因模糊密码子崩溃。\n\n---\n\n## 📚 Skill 体系（50 个）\n\n### 主 skill：`dsh-bio-genie`\n工具分层决策树：**先查语义化工具表 → 命中就用；否则用 bio_python 执行器写 Biopython 代码**。\n\n### 17 个领域配方 + 5 个研究方法\n\n| Skill | 覆盖的 Biopython 模块 |\n|-------|---------------------|\n| `bio-core` | 核心工作流（任何分析先加载） |\n| `bio-io` | Bio.SeqIO（FASTA/FASTQ/GenBank/EMBL…） |\n| `bio-seq` | Bio.Seq / Bio.SeqUtils（GC、Tm、分子量） |\n| `bio-align` | Bio.Align.PairwiseAligner / Bio.AlignIO |\n| `bio-blast` | Bio.Blast（NCBIWWW / NCBIXML） |\n| `bio-searchio` | Bio.SearchIO（BLAST/HMMER/Exonerate 解析） |\n| `bio-entrez` | Bio.Entrez（esearch/efetch/esummary/elink） |\n| `bio-phylo` | Bio.Phylo（Newick/Nexus、系统发育） |\n| `bio-structure` | Bio.PDB（结构解析、距离、叠合） |\n| `bio-motif` | Bio.motifs（PWM、JASPAR/MEME） |\n| `bio-restriction` | Bio.Restriction（酶切位点、片段） |\n| `bio-utils` | Bio.Data.CodonTable（遗传密码表、密码子用法） |\n| `bio-graphics` | Bio.Graphics.GenomeDiagram（图谱绘制） |\n| `bio-popgen` | Bio.PopGen（群体遗传学） |\n| `bio-figure` | 出版级科研绘图顾问（figurelib：选图决策、18 陷阱、期刊规格、CJK 中文） |\n| `bio-ml` | scikit-learn 生物数据机器学习（分类/降维/聚类/特征重要性） |\n| `bio-dna-design` | DNA 设计（引物、密码子优化、质粒图谱） |\n\n研究方法 skill：`bio-survival-analysis`（生存分析）、`bio-variant-analysis`（变异分析）、`bio-literature-review`（文献检索与综述，含 PRISMA 2020 系统综述流程）、`bio-evidence-appraisal`（证据分级与结论强度：证据层级/GRADE/四轴评估/措辞边界）、`bio-paper-writing`（论文写作）。另有 19 个协议模板与 8 份 agent 使用指南，随插件打包。\n\n---\n\n## 🧞 精灵专家人设（`bio-genie` preset）\n\n本插件同时提供一个 **dsh agent preset（智能体预设）**——`bio-genie`，让 AI 一进 dsh 就成为精通本插件的「**生物基因精灵**」专家人设。\n\n### 它是什么\n\n- **人设源文件**（`preset/bio-genie/`：`preset.yml` + `agent.cordis.yml` + `skills/`）——覆盖 base persona，告诉 AI「你手头有 62 个工具 + 50 个 skill」；`cordis.patch.yml` 的 preset 段由 `scripts/build-preset-patch.mjs` 从此目录生成（单一数据源，勿手改生成物）。\n- **入门口诀**（`skills/dsh-bio-genie-expert.md`）——一个 meta-skill：「先看工作区 → 二选一（语义化工具 / `bio_python`） → 失败按 ACR 三层修 → 报告带可追溯链」。\n- **一键安装**：0.2.0+ 由插件自带的 preset 声明直接提供（装完即有，零后置动作）；0.1.x 由 `postinstall` 钩子把 preset 复制到 `~/.dsh/.agent-presets/bio-genie/`（需先批准构建脚本，见上文）。\n\n### 它**不是**\n\n- ❌ **不接管 62 个工具**——所有 `bio_*` 工具仍由本插件的 `cordis.patch.yml` 注入，preset **不重声明**任何工具，避免冲突。\n- ❌ **不抢默认人设**——安装后「生物基因精灵」出现在 dsh 预设选择器里；用户**主动选择**才激活。`agent-presets.default` 不会被改成 `bio-genie`。\n- ❌ **不破坏其他插件**——presets 与 plugins 是 dsh 的两个独立 seam，共存不冲突。\n\n### 怎么用\n\n1. **安装本插件**：`pnpm add @dsh-bio/dsh-bio-genie`（0.2.0+ 装完即有 preset；0.1.x 见上文的构建脚本批准说明）。\n2. **重启 dsh web**。\n3. **设置面板** → 选「**生物基因精灵**」人设。\n4. 之后 AI 启动会话即说：「我是生物基因精灵……你的工作区是 `{{cwd}}`……先看看你有什么数据再开工」。\n\n### 手动安装 / 卸载\n\n```bash\n# 手动复制（0.1.x 专用；0.2.0+ 通常不需要——preset 由插件声明提供）\nnode scripts/install-preset.js\n\n# 强制覆盖（用户就地编辑过 preset 时也覆盖）\nnode scripts/install-preset.js --force\n\n# 试运行（只看会做什么）\nnode scripts/install-preset.js --dry-run\n\n# 卸载：直接删\n# Windows: rd /s /q %USERPROFILE%\\.dsh\\.agent-presets\\bio-genie\n# macOS/Linux: rm -rf ~/.dsh/.agent-presets/bio-genie\n```\n\n### 故障排除\n\n- **预设选择器看不见「生物基因精灵」**：\n - 0.2.0+：确认插件版本 ≥ 0.6.37 且已加载（`dsh --profile

--dump-config` 输出中应有 `preset-bio-genie` 行）；重启一次 dsh 再试。\n - 0.1.x：检查 `~/.dsh/.agent-presets/bio-genie/preset.yml` 是否存在；不存在则 `node scripts/install-preset.js` 手动装。\n- **切到 preset 后工具没出现** → 工具由插件注入，与 preset 无关；检查插件是否真在 `dependencies`（`pnpm ls @dsh-bio/dsh-bio-genie`）。\n- **想自定义 persona**：\n - 0.2.0+：在 profile 的 `cordis.patch.yml` 按行 id `preset-bio-genie` 写覆盖补丁（覆盖 `config.plugins`）。\n - 0.1.x：直接编辑 `~/.dsh/.agent-presets/bio-genie/agent.cordis.yml`（不被自动覆盖除非 `--force`）。\n\n---\n\n## 🚀 使用示例\n\n**场景 1：语义化工具路径（高频操作）**\n\n> 用户：*\"分析这个文件里的序列 GC 含量和 EcoRI 位点：D:/data/genes.fasta\"*\n\n```\nagent 自动：\n1. bio_seq_io_read → 读取 FASTA\n2. bio_seq_analyze → 逐条 GC 含量\n3. bio_seq_restriction → 检查 EcoRI\n4. 汇总报告 + 生物学解读\n```\n\n**场景 2：执行器路径（语义化工具覆盖不到的功能）**\n\n> 用户：*\"画一下这两个基因的蛋白结构比对\"*\n\n```\nagent 自动：\n1. 加载 bio-align / bio-structure skill\n2. bio_python 写 Biopython 程序执行\n3. 产出文件 + 报告\n```\n\n**场景 3：组合路径（实测）**\n\n> 用户：*\"读取 FASTA 分析每条序列的 GC、最长 ORF 和 EcoRI 位点\"*\n\n```\nagent 自动（实测行为）：\n1. 加载 dsh-bio-genie 主 skill（决策指引）\n2. bio_seq_io_read 读取文件\n3. bio_python 一次性完成 GC + ORF + 酶切组合分析\n4. 输出汇总表（GC 48.28%、ORF 7aa、EcoRI nt 3-8）+ 生物学解读\n```\n\n---\n\n## 🔧 环境引导（零依赖自举）\n\n首次调用（或 dsh 启动后台预热）时插件自动执行：\n\n```\n1. 下载 uv → $DSH_HOME/dsh-bio-genie/bin/uv\n （官方 GitHub 直连失败自动切换清华 PyPI 的 uv wheel，实测 18MB/约 2 秒）\n2. uv python install → $DSH_HOME/dsh-bio-genie/python/（私有 CPython 3.12）\n （官方源失败自动切换 npmmirror 的 python-build-standalone 镜像）\n3. uv venv --seed → $DSH_HOME/dsh-bio-genie/python-env/（预装 pip，方便按需补包）\n4. uv pip install → biopython + numpy + matplotlib + reportlab + pandas/scipy/seaborn/Pillow（出版级绘图栈；官方 PyPI 失败自动切换清华镜像）\n```\n\n- **网络自动适配**：每个环节默认直连官方源，失败自动切换国内镜像，全程无需用户配置；\n 高级用户可用环境变量覆盖镜像地址（`DSH_BIO_UV_BASE` / `DSH_BIO_PYTHON_MIRROR` / `DSH_BIO_PYPI_INDEX`，\n 也尊重 uv 官方变量 `UV_PYTHON_INSTALL_MIRROR` / `UV_DEFAULT_INDEX` / `UV_INDEX_URL`）；\n ⚠️ uv 二进制下载后一律做 SHA256 校验（官方/镜像通道均校验，校验失败拒绝执行）——\n 自定义 `DSH_BIO_UV_BASE` 镜像需在镜像根目录提供 `sha256sums.txt`（与 uv 官方 release 同格式）\n- **全部产物**在 `$DSH_HOME/dsh-bio-genie/`（默认 `~/.dsh/dsh-bio-genie/`），删除即完全卸载\n- **不假设系统有任何 Python/uv**（自举）；引导失败自动回退系统 python（若有）\n- **升级插件不丢环境**：环境在 DSH_HOME 私有目录，与插件本体（node_modules）分离\n- **幂等**：已就绪则秒级复用；引导失败自动重试\n- 首次引导需网络；引导完成后可离线使用语义化工具\n\n---\n\n## 🔄 兼容性\n\n| 维度 | 要求 |\n|------|------|\n| **Node** | `^22.19 \\|\\| >=24`（与 dsh 一致） |\n| **dsh** | peer 依赖 `@deepseek-ai/dsh-tools` 等为 `^0.1.0-rc.6 \\|\\| ^0.2.0-rc.1`（0.1.x 与 0.2.0+ 双线支持，含官方桌面端；安装方式见上文「安装」节） |\n| **平台** | Windows / macOS / Linux（x86_64 / arm64），按平台自动下载对应 uv/Python |\n\n---\n\n## 🧩 开发\n\n纯 ESM JavaScript，**无构建步骤**，改完即用：\n\n```bash\ngit clone https://github.com/moonbowterfly/dsh-bio-genie\n# 直接调用引导器（首次会下载环境，约 1-2 分钟）：\nnode --input-type=module -e \"import('./src/runtime.js').then(m => m.ensureEnvironment({}))\"\n```\n\n- 架构设计详见 [docs/ARCHITECTURE.md](docs/ARCHITECTURE.md)\n- **给 dsh agent 的使用说明书**：[docs/agent-guide/](docs/agent-guide/)——8 份指南（总览/工具参考/skill 导航/bio_python 编程/工作流/绘图专题/故障排查/严谨性），随插件注册为 `dsh-bio-genie-guide-*` 技能，agent 可随时加载\n- 加语义化工具：`python/bio_ops.py` 加 op + `src/tools.js` 加 bioTool 条目\n- 加领域 skill：`skills/bio-xxx.md` + `src/skills.js` 的 SKILL_MANIFEST\n- **skill 语言标注约定**：所有 skill（领域/协议/指南）开头 frontmatter 必须含 `language:` 字段（`python`/`r`/`mixed`/`none`），test-skills.mjs 强制校验\n\n---\n\n## 📄 许可证\n\n- **dsh-bio-genie 本体**：MIT License\n- **Biopython**：Biopython License Agreement / BSD 3-Clause（宽松，详见 [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md)）\n- **numpy**：BSD License\n- **scipilot-figure-skill**（figurelib 绘图脚本）：MIT（Copyright Haojae，详见 THIRD_PARTY_NOTICES.md）\n- **K-Dense scientific-agent-skills**（figurelib 样式资产 + 知识型协议来源）：MIT（Copyright K-Dense Inc.，详见 THIRD_PARTY_NOTICES.md）\n- **不含 BioSQL**（LGPL，刻意排除）\n\n---\n\n## 🙏 致谢\n\n本项目的一切生物学计算能力都建立在 **Biopython** 之上 —— 感谢 [biopython/biopython](https://github.com/biopython/biopython) 项目及全体贡献者 25 年来的卓越工作：他们维护的序列分析、比对、结构生物学、系统发育等高质量实现，让\"许愿式生物信息学\"成为可能。Biopython 采用宽松的 [Biopython License Agreement](https://github.com/biopython/biopython/blob/master/LICENSE.rst)（兼容 BSD 3-Clause），允许自由复制、修改与分发，本插件因此得以安心地依赖并推广它。\n\n出版级绘图能力（figurelib）借鉴了 [Haojae/scipilot-figure-skill](https://github.com/Haojae/scipilot-figure-skill)（MIT）的\"可视化顾问\"工作流与视觉自检设计，样式资产与部分知识型协议参考 [K-Dense-AI/scientific-agent-skills](https://github.com/K-Dense-AI/scientific-agent-skills)（MIT）——一并致谢。\n\n同时感谢 [DeepSeek Harness](https://github.com/deepseek-ai/deepseek-harness) 提供的插件化 Agent 框架，以及 numpy 社区的基础贡献。\n","readmeFilename":"README.md"}