bio-manuscript-pipeline
End-to-end pipeline from structured research input to a full manuscript plan (一条龙 Pipeline)
BioClaw integration notes:
- This skill is staged under
container/skills/ as part of a multi-skill manuscript pipeline. - Shared templates and helper scripts are available under the sibling directory
bio-manuscript-common/. - When this pipeline needs supporting capabilities, prefer the copied BioClaw sibling skills in
container/skills/ over any ~/.openclaw/... layout assumptions. - This skill family is being integrated as a BioClaw community-contributed workflow.
- Upstream source reference: https://github.com/donghongyu2020/bio-manuscript-forge/tree/main/bio-manuscript-forge
- Contributor reference for attribution and documentation: Hongyu Dong, Westlake University PhD candidate, BioClaw community contributor / BioClaw community co-creation contributor.
- In BioClaw, treat the sibling manuscript skills as stepwise companion skills. You should explicitly follow their guidance phase by phase rather than assuming an automatic runtime dispatcher.
- If a later phase depends on outputs from an earlier phase, write those outputs into the group workspace first and then continue with the next sibling skill using those artifacts as context.
- At the end of each substantial run, also write a concise human-readable execution summary. Prefer
FINAL_EXEC_SUMMARY.md; for integration-focused runs, also write INTEGRATION_TEST_REPORT.md.
Welcome
Welcome to Bio-Manuscript-Forge. This workflow helps turn a rough research idea into a manuscript-ready planning package.
Input Template
Provide your project in the following structure:
text
topic: [research topic]
base_work:
- paper: [related paper link]
- code: [related code repository]
innovation: [one-sentence innovation summary]
- algorithmic novelty (算法创新性): [core method novelty]
- tasks (任务): [task1, task2, task3, ...]
- data (数据): [dataset source or type]
- benchmark: [evaluation benchmark]
- metrics (计算指标): [metric1, metric2, ...]
- biological analyses (生物学分析手段): [how biological significance will be shown]
demo_data: [demo dataset link]
target_journal: [optional, default nat-communications]
num_refine_rounds: [optional, default 2]
Example Input
topic: spatial multi-omics integration
base_work:
- paper: https://www.nature.com/articles/s41592-021-01336-8
- code: https://github.com/broadinstitute/Tangram
innovation: jointly align spatial transcriptomics and proteomics while preserving tissue-domain boundaries
- algorithmic novelty (算法创新性): boundary-aware cross-modal alignment with explicit domain-consistency regularization
- tasks (任务): cell annotation, spatial domain detection, cross-modal integration, biological interpretation
- data (数据): public spatial transcriptomics and spatial proteomics cohorts with matched single-cell references
- benchmark: compare against mapping, domain, and integration baselines on public tumor datasets
- metrics (计算指标): ARI, NMI, Macro-F1, boundary preservation score, biological consistency
- biological analyses (生物学分析手段):
- marker recovery across modalities
- pathway enrichment consistency
- neighborhood preservation
- tissue-boundary case studies
demo_data: https://zenodo.org/record/0000000
target_journal: nat-communications
num_refine_rounds: 2
Expected Outputs
| File | Content | |
|---|
| PPT | Lab meeting / progress presentation | |
| FINAL_PROPOSAL | Full research proposal | |
| Figure 2-7 (v3) | Detailed task-wise figure designs | |
| Manuscript text (v2) | Introduction, Results, Discussion, Methods | |
Provide the project description and the pipeline can begin.
Purpose
Run the full manuscript pipeline, generate a journal-style plan, and iteratively refine it through reviewer-style feedback.
Input Schema
text
topic: [research topic]
base_work: [paper links + code links]
innovation: [high-level innovation summary]
- algorithmic novelty (算法创新性): [core algorithmic novelty]
- tasks (任务): [downstream tasks, comma-separated]
- data (数据): [dataset source / type]
- benchmark: [benchmark dataset or evaluation setup]
- metrics (计算指标): [safety + task metrics such as ASR, ARI, etc.]
- biological analyses (生物学分析手段): [marker genes, pathways, neighborhood analysis, etc.]
demo_data: [demo dataset link]
target_journal: [optional, default nat-communications]
num_refine_rounds: [optional, default 2]
Example Input (public-safe sample)
topic: spatial multi-omics integration
base_work:
- paper: https://www.nature.com/articles/s41592-021-01336-8
- code: https://github.com/broadinstitute/Tangram
innovation: jointly align spatial transcriptomics and proteomics while preserving tissue-domain boundaries
- algorithmic novelty (算法创新性): boundary-aware cross-modal alignment with explicit domain-consistency regularization
- tasks (任务): cell annotation, spatial domain detection, cross-modal integration, biological interpretation
- data (数据): public spatial transcriptomics and spatial proteomics cohorts with matched single-cell references
- benchmark: compare against mapping, domain, and integration baselines on public tumor datasets
- metrics (计算指标): ARI, NMI, Macro-F1, boundary preservation score, biological consistency
- biological analyses (生物学分析手段):
- marker recovery across modalities
- pathway enrichment consistency
- neighborhood preservation
- tissue-boundary case studies
demo_data: https://zenodo.org/record/0000000
target_journal: nat-communications
num_refine_rounds: 2
Field Guide
| Field | Required | Description | |
|---|
| topic | yes | concise research topic | |
| base_work | yes | paper + code links | |
| innovation | yes | high-level idea plus structured subfields | |
| demo_data | yes | demo dataset link | |
| target_journal | no | default nat-communications | |
| num_refine_rounds | no | default 2 | |
Innovation Subfields
| Subfield | Description | Example | |
|---|
| algorithmic novelty (算法创新性) | core method novelty | attention entropy, loss redesign, architecture change | |
| tasks (任务) | downstream tasks covered | cell annotation, perturbation, GRN inference | |
| data (数据) | dataset source / type | public cohorts, user data, target tissue | |
| benchmark | evaluation setup | existing benchmark or new benchmark | |
| metrics (计算指标) | safety + task metrics | ASR, Accuracy, F1, ARI, Pearson | |
| biological analyses (生物学分析手段) | how biology will be demonstrated | marker gene, pathway, regulatory links | |
Execution Flow
Phase 1: System building (Steps 1-5)
Input parsing: first extract the key signals from user input:
topic → 用于创新性搜索base_work → 提取已有工作数据集、指标、方法innovation.algorithmic novelty / innovation.算法创新性 -> novelty assessmentinnovation.tasks / innovation.任务 -> task system designinnovation.data / innovation.数据 -> dataset search directioninnovation.metrics / innovation.计算指标 -> metric system designinnovation.biological analyses / innovation.生物学分析手段 -> analysis system design
Step 1: 创新性检测
├─ 解析输入：topic, base_work, innovation.算法创新性
├─ 调用 searxng/web_search 搜索
├─ Topic 同义变换生成 10-20 个变体
├─ 搜索 PubMed + bioRxiv + arXiv q-bio
├─ 统计相似文章数量
├─ 结合 innovation.算法创新性 判断创新性级别
└─ 输出：01_INNOVATION_ASSESSMENT.md
Step 2: 任务体系构建
├─ 解析输入：innovation.任务
├─ 若用户提供任务列表 → 直接使用
├─ 若未提供 → 搜索领域主要任务分类
├─ 识别任务层级（Level 1-4）
├─ 确保难度递进
└─ 输出：02_TASK_SYSTEM.md
Step 3: 数据集搜索
├─ 解析输入：innovation.数据, innovation.benchmark, demo_data
├─ 若用户提供数据描述 → 搜索匹配数据集
├─ 从 base_work 论文提取数据集
├─ 数据集与任务匹配
└─ 输出：03_DATASET_CATALOG.md
Step 4: 指标体系构建
├─ 解析输入：innovation.计算指标
├─ 若用户提供指标 → 直接使用并补充
├─ 若未提供 → 从已有工作提取指标
├─ 分类：安全指标 + 任务指标
└─ 输出：04_METRIC_SYSTEM.md
Step 5: 分析方法体系
├─ 解析输入：innovation.生物学分析手段
├─ 若用户提供分析手段 → 直接使用并补充
├─ 若未提供 → 从已有工作提取分析方法
├─ 标注 OmicsClaw/Bioclaw skill
├─ 说明为什么用、证明什么、体现什么生物学意义
└─ 输出：05_ANALYSIS_SYSTEM.md
Phase 2: 设计与文案（Steps 6-7）
⚠️ 核心原则：
- 任务为先：Figure 2-N 每个对应一个任务，数据/指标/分析随任务而定
- 分析增强：每个 Figure 必须包含安全 + 生物学分析
- 文案同步：Figure 改完立即更新 Results
Step 6: Figure 设计
│
├─ Figure 1：算法创新性（方法框架）
│ ├─ Panel a：方法 Overview
│ ├─ Panel b：创新点示意
│ ├─ Panel c：模型覆盖
│ ├─ Panel d：任务覆盖
│ └─ Panel e：指标体系
│
├─ Figure 2-N：每个 Figure = 一个任务 ⭐ 任务为先原则
│ │
│ ├─ Panel a: 任务 Overview（数据流）
│ │
│ ├─ Panel b-d: 定量测评
│ │ ├─ 多模型对比
│ │ ├─ ASR 降低
│ │ └─ 任务指标保持
│ │
│ ├─ Panel e: Technical analysis ⭐ must include
│ │ ├─ representation pattern shifts
│ │ ├─ error / uncertainty analysis
│ │ └─ failure-mode or boundary-case inspection
│ │
│ ├─ Panel f: 生物学分析 ⭐ 必须包含
│ │ ├─ Marker gene recovery
│ │ ├─ Pathway preservation
│ │ └─ 具体生物学意义
│ │
│ ├─ Panel g: In-depth case studies ⭐ 1-2 cases
│ │ ├─ concrete biological question
│ │ ├─ baseline vs proposed method comparison
│ │ └─ interpretation of recovered biological structure
│ │
│ └─ 数据/指标/分析依据任务选取
│
├─ Figure N+1: Summary + 生物学意义总结
│
└─ 输出：06_FIGURE_DESIGNS/
Step 6.5: 文案同步检查 ⭐ 必须
├─ Figure 有这个 Panel → Results 有对应段落？
├─ Figure 有这个案例 → Results 有详细展开？
└─ 检查通过才能进入下一步
Step 7: 论文文案生成
│
├─ Introduction（5段）
│ ├─ 第一段：领域介绍
│ ├─ 第二段：相关工作调研
│ ├─ 第三段：现有方法不足
│ ├─ 第四段：本文方法介绍
│ └─ 第五段：意义与应用
│
├─ Results（与 Figure 对应）⭐ 结构对齐
│ ├─ 2.1 Overview（对应 Figure 1）
│ ├─ 2.2 Task 1 / Main claim（对应 Figure 2）
│ │ ├─ quantitative evaluation
│ │ ├─ technical analysis
│ │ ├─ biological analysis
│ │ └─ case study
│ ├─ 2.3 Task 2 / Main claim（对应 Figure 3）
│ ├─ ...每个任务一个 section
│ └─ 2.N Summary（对应最后一个 Figure）
│
├─ Discussion
│ ├─ 方法优势总结
│ ├─ 安全-生物学结合意义 ⭐
│ ├─ 与现有方法对比
│ ├─ 方法局限性
│ └─ 未来方向
│
├─ Methods
│ ├─ 数据预处理
│ ├─ 模型架构
│ ├─ 任务特定方法 ⭐ 按任务组织
│ ├─ 生物学分析方法 ⭐
│ ├─ 统计分析
│ └─ 代码与数据可用性
│
└─ 输出：07_MANUSCRIPT_TEXT/
Figure 设计检查清单：
- [ ] Figure 1 是方法框架？
- [ ] Figure 2-N 每个对应一个任务？
- [ ] 每个 Figure 包含多模型对比？
- [ ] 每个 Figure 有定量测评 Panel？
- [ ] 每个 Figure 有安全分析 Panel？ ⭐
- [ ] 每个 Figure 有生物学分析 Panel？ ⭐
- [ ] 每个 Figure 有 1-2 个深入案例？ ⭐
- [ ] 分析手段多样化？
- [ ] Results 结构与 Figure 对应？ ⭐
Phase 2.5: Refine Loop ⭐
Step 7.5: 三审稿人迭代优化
│
├─ Round 0: 保存初始方案
│ └─ 输出：refine-logs/round-0-initial-proposal.md
│
├─ Round 1 Review:
│ ├─ Editor Review（创新性评估，Nature子刊标准）
│ │ ├─ 概念创新 / 方法创新 / 应用创新
│ │ └─ 评分：创新性 / 可行性 / 推荐度
│ │
│ ├─ 计算审稿人 Review（算法/方法评审）
│ │ ├─ 算法设计合理性 / 方法创新性
│ │ ├─ 实验设计严谨性（Baseline/指标/Ablation）
│ │ └─ 评分：方法创新 / 技术严谨 / 代码可行
│ │
│ ├─ 生物分析审稿人 Review（生物学意义评审）
│ │ ├─ 生物学意义 / 分析设计合理性
│ │ ├─ 数据集选择合理性
│ │ └─ 评分：生物意义 / 分析设计 / 数据选择
│ │
│ └─ 输出：refine-logs/round-1/
│
├─ Round 1 Refinement:
│ ├─ 汇总三审稿人意见
│ ├─ 问题分类（Critical/Major/Minor）
│ ├─ 逐条响应和修改
│ ├─ 更新 Proposal
│ └─ 输出：refine-logs/round-1/refinement.md
│
├─ Round 2 Review:（同 Round 1）
│ └─ 输出：refine-logs/round-2/
│
├─ Round 2 Refinement:
│ └─ 输出：refine-logs/round-2/refinement.md
│
└─ 最终输出：
├─ refine-logs/REVIEW_SUMMARY.md（每轮汇总）
├─ refine-logs/FINAL_PROPOSAL.md（最终方案）
├─ refine-logs/score-history.md（评分历史）
└─ refine-logs/REFINEMENT_REPORT.md（完整报告）
Phase 2.6: 人类反馈验证 ⭐ NEW
Step 7.6: 人类反馈循环
│
├─ 呈现 Proposal
│ ├─ 展示 FINAL_PROPOSAL.md 核心内容
│ ├─ 包含：创新点、Figure 设计、实验方案、关键修改
│ └─ 格式：结构化摘要 + 关键决策点
│
├─ 等待人类反馈
│ ├─ 选项 A: 同意 → 继续 Phase 3
│ └─ 选项 B: 有意见 → 收集反馈内容
│
├─ 反馈处理
│ ├─ 如果同意 → 记录并进入 Phase 3
│ └─ 如果不同意 →
│ ├─ 记录反馈意见到 refine-logs/human-feedback/
│ ├─ 根据反馈类型决定返回点：
│ │ ├─ Phase 1 级问题：创新性/任务体系需重构
│ │ ├─ Phase 2 级问题：Figure/文案需调整
│ │ └─ Phase 2.5 级问题：细节优化
│ ├─ 执行迭代修改
│ ├─ 重新运行 Phase 2.5 Refine Loop
│ └─ 再次呈现给人类验证
│
└─ 输出：
├─ refine-logs/human-feedback/feedback-round-X.md
└─ refine-logs/HUMAN_APPROVAL.md（最终批准记录）
人类反馈处理流程：
人类反馈 → 问题分类 → 返回点决策
│
├─ Critical 问题（创新性方向错误）
│ └─ 返回 Phase 1 → 重新评估创新点
│
├─ Major 问题（设计/方案需要大改）
│ └─ 返回 Phase 2 → 调整 Figure/文案
│
├─ Minor 问题（细节优化）
│ └─ 返回 Phase 2.5 → Refine Loop
│
└─ 批准
└─ 进入 Phase 3