litmind-analyzer
LitMind Paper Analyzer — 将论文全文转为结构化科研知识 (PaperAnalysis)
DeepseekModel
Curated skill
Quality Excellent · 78
v1.0.0
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name litmind-analyzer description LitMind Paper Analyzer — 将论文全文转为结构化科研知识 (PaperAnalysis) LitMind Paper Analyzer 将 Paper Parser 输出的 PaperContent（论文全文）通过 LLM 分析，提取为结构化 PaperAnalysis。 不做自由文本摘要。 重点是知识提取：研究问题、方法、变量、统计、发现、声明、局限、未来方向、深度数值提取。 工作流程 Step 1: 输入 接收 PaperContent JSON（来自 /litmind-parser 的输出）。 Step 2: 分析 调 LLM 提取结构化知识。支持： Anthropic Claude (default) OpenAI GPT-4o Step 3: 验证 Pydantic schema 验证 + 字段完整性补全。 Step 4: 输出 统一 PaperAnalysis JSON（含可选的 deepExtraction 深度提取数据）。 深度提取 (DeepExtraction) v0.4.0+ 除摘要级知识外，analyzer 还会从 Results/Methods 中提取 细粒度数值数据 ： numericalFindings : 含条件、指标、数值、单位、统计量的结构化数据 例: {"condition": "Flatfoot + CS shoe", "metric": "Ankle eversion ROM", "value": 12.3, "unit": "deg", "statistics": "p=0.003"} experimentalProtocols : 实验参数，如 "Drop height: 45cm" , "Sampling rate: 1000Hz" 此为 optional 字段 ，LLM 无法提取时留空，不影响原流程 深度提取数据存入独立 ChromaDB collection，M5 问答时可检索到具体数值 输出格式 输出格式 { "paperId" : "KP33THHS" , "researchQuestion" : "Does landing height affect GRF?" , "studyDesign" : "Experimental Study" , "participants" : { "sampleSize" : 20 , "groups" : [ "Flat" , "Normal" ] , "population" : "Healthy males" } , "methods" : [ "3D motion capture" , "Force plate" , "EMG" ] , "statistics" : [ "Repeated measures ANOVA" ] , "mainFindings" : [ "Flat feet had greater peak GRF" ] , "claims" : [ { "statement" : "Flat feet increase GRF" , "evidenceSource" : "Results" } ] } 调用方式 # CLI litmind-analyze paper_parsed.json -o analysis.json # 指定 provider litmind-analyze paper.json --provider openai --model gpt-4o # 批量 litmind-analyze batch ./parsed/ -o ./analyses/ 环境变量 ANTHROPIC_API_KEY — Claude provider OPENAI_API_KEY — OpenAI provider LITMIND_LOG_LEVEL — 日志级别 (DEBUG/INFO/WARNING/ERROR, 默认 WARNING)
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The downloaded .skill package contains the following fields.
| Field | Description |
|---|---|
| format | Format tag (skill/v1) |
| skill_id | Unique skill ID |
| name | Skill name |
| version | Version |
| description | Description |
| category | Categories (array) |
| trigger_words | Trigger words |
| tags | Tags |
| source | Source |
| source_url | Source URL (this page) |
| exported_at | Exported at (set per download) |
| system_prompt | System prompt body |
| model_config | Model config: provider / model / temperature / max_tokens / top_p |
| examples | Examples |
| install_guide | Import guide for Coze / Dify / Claude / custom frameworks |
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