Menü
Persist and query biomedical causal triples from BioCSSwitch conversations, literature checks, and debate outputs.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
用于给一个 outcome 的证据确定性做 GRADE 评级、产出 Summary of Findings (SoF) 表。触发：证据确定性、certainty of evidence、GRADE、证据质量分级、这个结论有多可靠、SoF 表、summary of findings、要不要下推荐、指南证据分级、quality of evidence、能不能信这个结论、证据强度、evidence certainty、把证据评个级。也用于"这个疗法的证据到底有多强"、"帮我给这几个结局评 GRADE"、"这条推荐的证据基础可靠吗"。禁用于：单纯核对某个 PMID 真伪（那用 evidence-audit / evidence_verify）。
Use for cross-modal target discovery from an unmet clinical need, combining literature, genetics, druggability, clinical trials, single-cell, and spatial evidence in one auditable context.
Design executable biomedical experiments from literature gaps or hypotheses, including testability, power, protocol, reagents, cost, preregistration, and failure modes.
用于任何**可能含患者数据 / 临床原始记录 / 医疗档案**的场景，强制先做 PHI 检测再决定去向。触发：患者、病历、病案、临床数据、EHR、EMR、住院记录、门诊记录、handover note、progress note、discharge summary、SOAP、真实世界数据、RWD、reidentification、identifiable data、真实病人、我这里有一段病历、这是一份体检报告、帮我看一下这份 CT 报告、我上传一份门诊记录、患者档案、患儿、受试者数据、subject-level data、临床原始表、GCP 数据、eCRF、我要处理 HIPAA、PHI、脱敏、去标识化、我们医院的数据。也在提到 sensitive_mode / 敏感模式 / 隐私模式 时触发。禁用于：公开文献 / 已发表数据 —— 那些不含 PHI，扫描是浪费时间。
Use when the user opts into a local research-interest model, wants personalized paper/preprint/trial updates, saves or rejects research suggestions, asks for a proactive briefing, or wants workflow prediction across sessions.
用于单细胞 RNA-seq 数据的标准化预处理、QC、doublet 检测、batch 整合、基因 ID 转换、细胞类型注释与内容指纹，为下游聚类、scFM embedding、DEG、轨迹或细胞通信准备可复现输入。触发：单细胞预处理、scRNA-seq QC、scanpy 预处理、AnnData、h5ad、质控、doublet、Scrublet、scDblFinder、batch correction、Harmony、scVI、BBKNN、基因 ID 转换、CellTypist、SingleR、CITE-seq、multiome、空间转录组、Visium。禁用于：bulk RNA-seq（用 geo-triage / omics_deseq2），以及 embedding 之后的 DEG/trajectory/cell communication（交给 sc-downstream-analysis）。
| name | causal-knowledge-graph |
| description | Persist and query biomedical causal triples from BioCSSwitch conversations, literature checks, and debate outputs. |
Use this skill when a biomedical answer produces causal claims that should be remembered, checked for conflicts, or converted into new hypotheses.
kg_extract_triples.kg_query for the subject/object/context to see what the local graph already contains.kg_add_triples.kg_conflict_scan.kg_causal_paths and treat paths as hypotheses unless every edge is strongly supported.kg_gap_analysis and hand high-priority gaps to bio-experiment.agentic_experiment_plan.Each causal edge should include subject, relation, object, evidence, direction, context, experiment type, model system, confidence, timestamp, and source. Do not store PHI. If a claim has no PMID, DOI, NCT, or local evidence record, mark it as a gap rather than established knowledge.