A reusable end-to-end workflow for designing, validating, and iterating mechanistic hypotheses in catalytic activation and functionalization research through observable-driven discovery and control-rich discrimination.
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核聚变物理的数据库 MCP
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A reusable end-to-end workflow for designing, validating, and iterating mechanistic hypotheses in catalytic activation and functionalization research through observable-driven discovery and control-rich discrimination.
Guide end-to-end research design to discover, characterize, and validate molecular mechanisms of adaptive immune recognition through hypothesis generation, orthogonal validation, and graded mechanistic inference.
A reusable end-to-end workflow for generating, testing, and iterating mechanistic hypotheses from adaptive immune receptor repertoire convergence patterns while separating biological selection from technical bias and confounding.
A reusable end-to-end workflow to discriminate adaptive immune activation mechanisms from repertoire sequence features, including recovery-phase stratification and mechanism validation.
A reusable end-to-end workflow for generating, validating, and iterating mechanistic hypotheses about adaptive immune repertoire ontogeny from sequence data, covering germline recombination, compensatory diversity, selection bias, and lineage footprint inference.
A reusable end-to-end workflow to compare immune receptor repertoires across biological compartments or conditions, link clonal usage patterns to phenotypes, and iterate from observation to mechanistic hypothesis with appropriate controls and boundary checks.
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A reusable end-to-end workflow for designing, validating, and iterating mechanistic hypotheses in catalytic activation and functionalization research through observable-driven discovery and control-rich discrimination.
Guide end-to-end research design to discover, characterize, and validate molecular mechanisms of adaptive immune recognition through hypothesis generation, orthogonal validation, and graded mechanistic inference.
A reusable end-to-end workflow for generating, testing, and iterating mechanistic hypotheses from adaptive immune receptor repertoire convergence patterns while separating biological selection from technical bias and confounding.
A reusable end-to-end workflow to discriminate adaptive immune activation mechanisms from repertoire sequence features, including recovery-phase stratification and mechanism validation.
A reusable end-to-end workflow for generating, validating, and iterating mechanistic hypotheses about adaptive immune repertoire ontogeny from sequence data, covering germline recombination, compensatory diversity, selection bias, and lineage footprint inference.
A reusable end-to-end workflow to compare immune receptor repertoires across biological compartments or conditions, link clonal usage patterns to phenotypes, and iterate from observation to mechanistic hypothesis with appropriate controls and boundary checks.
Use when adding a new benchmark to AHE, deciding between generic-code command mode and a custom benchmark backend, implementing Backend subclasses, or defining metric-driven benchmark outputs for scientific evaluation.
Use when writing or reviewing AHE YAML configuration files, target agent configs, evolver configs, debugger overlays, generic-code command-mode configs, or run_ahe_evolution tool-call arguments with ahe_root_path and explicit config roles.
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