协作头脑风暴
brainstorming
在实现前澄清需求、探索设计选择,并形成清晰的开发方向。
tables-and-expressions【开发与工程】Build Hail Table and expression pipelines for imports, keys, joins, aggregations, schema inspection, previews, exports, and expression-scope troubleshooting.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this sub-skill for Hail Table pipelines and row-indexed expression work: hl.import_table, hl.read_table, Table.write, Table.export, annotate, filter, select, key_by, keyed joins, group_by, hl.agg, schema inspection, small local previews, missing values, Struct fields, interval lookups, and expression source/index errors.
Before executing Hail queries, use ../setup-and-backends/SKILL.md to choose and initialize the backend. This sub-skill assumes backend setup is already handled.
references/table-workflows.md.references/api-reference.md.references/troubleshooting.md.scripts/table_pipeline_template.py.../genomics-analysis/SKILL.md.../variant-datasets/SKILL.md.hl.init, Spark/local/Batch backend choice, Java/JAR/package asset failures, and root import troubleshooting belong in ../setup-and-backends/SKILL.md.hailtop.batch DAGs, local/service Batch jobs, hailctl, Dataproc, HDInsight, and cloud CLI operations belong in ../batch-and-cli/SKILL.md.ht = ht.filter(...), then reference ht.field from the rebound table.ht["sample id"], ht["case.status"], ht["select"].hl.agg inside supported aggregation contexts such as ht.aggregate(...) and ht.group_by(...).aggregate(...).types={...} for stable text imports; use impute=True only for exploration and review inferred identifier types.describe(), row.dtype, globals.dtype, key, head(n).show(), and small take(n) calls before large writes, exports, or joins.