.claude/skills/prioritize-drug-targets-lsRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
Generate and prioritize experimentally testable target hypotheses from one or more small-molecule structures. Use when a user supplies SMILES or a compound library and asks which targets the molecules may modulate, how structural neighbors and SAR support the hypotheses, which biological and competitive evidence should be checked, or which compounds and targets should advance to orthogonal validation.
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These checks parse the fixed package against dated platform rules. They do not execute the Skill or verify task behavior.
.claude/skills/prioritize-drug-targets-lsRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
.agents/skills/prioritize-drug-targets-lsRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
skills/prioritize-drug-targets-lsRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
This command is recorded from the source ecosystem and resolves the registry's latest release. The fixed release shown on this page should be inspected before adoption.
clawhub install @yuanzhian-patsnap/prioritize-drug-targets-lsclawhub inspect @yuanzhian-patsnap/prioritize-drug-targets-ls --version 1.0.0This automated, non-executing scan is bound to this release hash. It is not a safety certification and may contain false positives or false negatives.
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sha256:08af17fc03671ec33f70a1835e1abcd9ba2c3c42b5d023877ad248d1036aec36skill-card.mdSKILL.md- Initial public release of the "prioritize-drug-targets-ls" skill for evidence-backed target hypothesis generation from compound structures. - Supports intake of one or more small-molecule structures (SMILES, SDF/MOL, or structured files) and decision context for triage, discovery, or series selection. - Outputs prioritized hypotheses, compound quality, annotated neighbors, structure–activity insights, validation routes, and follow-up experiment suggestions. - Integrates with verified PatSnap MCPs for patent, target/disease, drug/pipeline, and clinical/scientific context retrieval. - Follows a structured workflow covering compound standardization, quality assessment, scaffold/series analysis, evidence assembly, and prioritization. - Clearly defines scope, limitations, evidence hierarchy, and operational integration criteria.