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Persist and query biomedical causal triples from BioCSSwitch conversations, literature checks, and debate outputs.
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Baseado na classificação ocupacional SOC
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.