一键导入
knowledge-extraction
Directory index for knowledge-extraction: knowledge-extraction
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
菜单
Directory index for knowledge-extraction: knowledge-extraction
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
引用三验 — 参考文献是否存在(L1) + 引用是否得当(L2) + 引用是否全面(L3)。三位一体验证管线,从DOI验真到语义审查到遗漏检测。
**触发条件**: 对一批论文(10-34 篇)批量处理 `step_quality_check.md` 中的 quality_score 并写入 `state.json`。
子skill | NotebookLM CLI全功能指南 — Q&A知识提取、内容生成(报告/视频/音频/信息图/幻灯片)、文献检索。响应paper-pipeline的P1阶段调用。
生产力工具 — Airtable、Google Workspace、Linear、Notion、Jupyter等。
Complete paper pipeline: retrieval, extraction, quality review, analysis, and publication.
双循环进化:内部反思(P0) + 外部吸收(P1)。Cross-project absorption methodology — multi-round cross-project comparison, active project tracking, self-expanding keyword discovery. 动灵驱动吸收(Entelechy-Driven Absorption v4.3).
| name | knowledge-extraction |
| description | Directory index for knowledge-extraction: knowledge-extraction |
| version | 1.0.0 |
| license | MIT |
| author | Synthos |
| metadata | {"synthos":{"signature":"paper_content: str, schema: dict -> structured_knowledge: dict (entities, relations, claims, evidence)","atom_type":"skill","priority":"P1","related_skills":[]}} |
详细内容请加载此skill后按需执行。核心流程和命令已提炼如下:
详细文档和完整命令列表已被移至 references/ 目录以保持简洁。
This skill has been compressed. Full content is available in references/.
paper_text: str, query_focus: strextracted: list[KnowledgeExtract] — 包含 topic, method, dataset, metric, result, confidence, source, quote对应原则:P2(机械原子暴露输入输出规范)
This skill also governs the knowledge_entry step of the research pipeline (step 4/4: literature_scan → gap_analysis → hypothesis_generation → knowledge_entry). When loaded for knowledge_entry generation, produce a synthesis document that integrates the outputs of all prior steps.
candidate_id: str, hypothesis_generation_output: dict, gap_analysis_output: dict, literature_scan_output: dict — outputs from all 3 prior pipeline stepsknowledge_entry: dict — markdown file with 9 required sections, 6-dimension quality scoreA knowledge_entry markdown file MUST contain the following sections in order:
| # | Section | Content | Source |
|---|---|---|---|
| 1 | Pipeline Completion Summary | Table with step scores (literature_scan, gap_analysis, hypothesis_generation, knowledge_entry) + score evolution (HG→KE delta + rationale) | All prior steps |
| 2 | 6-Dimension Quality Scoring | Weighted scoring table with 6 dimensions (see reference knowledge-entry-rubric.md). Each dimension needs score + rationale paragraph. Show weighted total calculation. | Novel synthesis |
| 3 | Domain Overview | Natural history of the physiological system (3-5 paragraphs), clinical problem table (condition, prevalence, existing metric, gap), white space statement, "why now" justification | literature_scan + gap_analysis |
| 4 | Model Architecture | 2-ODE+PINN description: ODE-1 and ODE-2 dynamics (state vars, parameters, key behavior). ASCII diagram or equation blocks. Multi-scale temporal assessment table. Cross-ODE identifiability confound analysis (detection→assessment→mitigation→residual). | gap_analysis |
| 5 | Entity Extraction | Two tables: (a) Entities — name, type, description for all physiological systems, diseases, parameters, devices, kernels; (b) Relations — source, relation, target for structural_analog/neighbor, clinical_biomarker, integrates, etc. | All prior steps |
| 6 | Hypothesis Portfolio | Ranked table (rank, ID, title, composite, recommendation) + detailed description of primary hypothesis (statement, population, measurement, endpoint, rejection criteria, rationale) + discriminative experiment design (Pattern #5 phase table) | hypothesis_generation |
| 7 | Kernel Registration | Structured table: kernel ID, name, type, domain, state/parameters, dynamics, clinical inputs, connected kernels, unique contribution, clinical conditions | Novel synthesis |
| 8 | Clinical Translation & Market | Phase 1-4 pathway table (timeline, activity, success criterion). Addressable populations table (population, size, current test, PINN advantage). Zero-equipment justification. | gap_analysis + hypothesis_generation |
| 9 | Critical Assessment & Limitations | Star-rating table (novelty, data feasibility, clinical impact, model complexity, parameter identifiability, reproducibility) + key risks (3-5 items, each with mitigation). Pipeline completion summary (domain expansion trajectory, next actions). | All prior steps |
The knowledge_entry's 6-dimension weighted score is systematically 1-4% below the hypothesis_generation step score. This is EXPECTED and normal — the 6D rubric weights conservative dimensions (Methodological Soundness 0.20, Reproducibility 0.10) that don't appear in the 5-dimension hypothesis scoring rubric.
| State of gap_analysis | Typical Δ (HG → KE) | Notes |
|---|---|---|
| PASS (≥0.85) | −1 to −3 points | 6D rubric restores conservative judgment |
| CONDITIONAL (0.65-0.78) | −2 to −4 points | Larger delta because methodological concerns get more weight in 6D |
See references/knowledge-entry-rubric.md for the full rubric with detailed scoring criteria per dimension per score band.
Existing knowledge_entry files follow this exact format. Reference examples:
outputs/papers/_knowledge_only/cochlear-mechanics-PINN/knowledge_entry_cochlear-mechanics-PINN.md (auditory, K-013)outputs/papers/_knowledge_only/baroreflex-regulation-PINN/knowledge_entry_baroreflex-regulation-PINN.md (cardiovascular, K-010)outputs/papers/_knowledge_only/respiratory-sinus-arrhythmia-PINN/knowledge_entry_respiratory-sinus-arrhythmia-PINN.md (cardiopulmonary, K-011)outputs/papers/_knowledge_only/vocal-fold-phonation-PINN/knowledge_entry_vocal-fold-phonation-PINN.md (laryngeal, K-014 — most recent example)