hail
Use Hail for scalable genomic Tables, MatrixTables, VariantDatasets, Hail Batch DAGs, and hailctl setup or cloud workflow troubleshooting.
- License 许可
- MIT
- 最近更新
- 2026-09-03
- Stars
- ★ 351
- Forks
- 27
01 前言 Front Matter
- name
- hail
- license
- MIT
- metadata
- { "disco-role": "operating" }
- description
- Use Hail for scalable genomic Tables, MatrixTables, VariantDatasets, Hail Batch DAGs, and hailctl setup or cloud workflow troubleshooting.
- disable-model-invocation
- true
02 资源文件 6
- 参考文档references/inspection-facts.md7.2 KB
- 参考文档references/package-map.md3.7 KB
- 参考文档references/repo-provenance.md2.6 KB
- 参考文档references/repo-routing-metadata.json487 B
- 参考文档references/troubleshooting.md4.3 KB
- 脚本scripts/check_hail_environment.py8.7 KB
03 SKILL.md 正文
Hail
Use this repo skill when a task mentions Hail, hail, hailtop, hailctl, genomic Table or MatrixTable workflows, VCF/PLINK/BGEN imports, sparse GVCF or VariantDataset workflows, Hail Batch DAGs, or Hail backend setup and troubleshooting.
First Checks
- Install the public
haildistribution in Python 3.10 or newer, then verifyimport hail as hl,import hailtop, andpython -m hailtop.hailctl --helpfrom a neutral working directory. - Read
references/repo-provenance.mdbefore deciding whether this skill is current for a local Hail checkout. - Read
references/package-map.mdfor the package layout, public APIs, and how the sub-skills divide responsibilities. - Read
references/inspection-facts.mdfor verified signatures, dependency constraints, packaged resources, andhailctlcommand families. - Run or adapt
scripts/check_hail_environment.pyfor safe import, version, CLI, Java, PySpark, packaged-resource, and optional local-init diagnostics. - Use
references/troubleshooting.mdfor cross-cutting install/import/backend issues before diving into workflow-specific troubleshooting.
Route by User Goal
| User goal or signal | Read |
|---|---|
Install Hail, debug import hail, choose hl.init backend, configure logs/tmp/reference/requester-pays, or fix Java/Spark/JAR startup failures |
sub-skills/setup-and-backends/SKILL.md |
Build Hail Table pipelines, import delimited data, key/join annotations, aggregate rows, inspect schemas, or fix expression-index errors |
sub-skills/tables-and-expressions/SKILL.md |
Work with dense genotype MatrixTables, VCF/PLINK/BGEN import/export, sample/variant QC, splitting, PCA, association tests, VEP, loci, calls, or reference genomes |
sub-skills/genomics-analysis/SKILL.md |
Use hail.vds, sparse GVCF/VariantDataset workflows, local alleles, reference blocks, VDS combiner, to_dense_mt, or VDS QC |
sub-skills/variant-datasets/SKILL.md |
Build hailtop.batch DAGs, choose LocalBackend vs ServiceBackend, use hailctl batch/config/auth/dataproc/hdinsight/describe, or debug Batch/CLI cloud configuration |
sub-skills/batch-and-cli/SKILL.md |
Safe Workflow Defaults
- Start with setup: verify the installed package, working directory, Java/PySpark,
hailctl, and backend choice before debugging workflow code. - For local examples, prefer
backend='local'orhailtop.batch.LocalBackend; treat cloud Batch, Dataproc, HDInsight, requester-pays data, and service billing as explicit user choices. - Convert large external genomic inputs to Hail native formats (
.ht,.mt,.vds) once, then iterate on native reads instead of reparsing VCF/PLINK/BGEN/GVCF inputs. - Keep dense
MatrixTableworkflows and sparse VDS/GVCF workflows separate until a task explicitly asks for conversion. - Use
describe(), schema inspection, tiny previews, dry-run templates, and the bundled diagnostics before running expensive writes, exports, Batch submissions, or densification.
Important Boundaries
- This skill covers user-facing Hail Python, Hailtop, Hail Batch, and packaged
hailctlworkflows. It does not cover Hail monorepo service deployment, release automation, Kubernetes infrastructure, or maintainer-only cloud operations. - Do not tell future agents to open original repository docs, tests, notebooks, or scripts. Use the bundled references and scripts in this skill.
- Do not put credentials, billing projects, bucket names, private paths, or local environment details into reusable examples; use placeholders and ask the user for their values.
- Native repo tests and cloud examples are verification evidence, not runtime dependencies. Run only safe, short, credential-free native checks when verifying this skill.
Bundled Helpers
scripts/check_hail_environment.pyinspects installed Hail/Hailtop imports, version metadata,hailctl, Java, PySpark, and packaged resources without initializing an expensive backend by default.sub-skills/tables-and-expressions/scripts/table_pipeline_template.pyprints or dry-runs a self-contained Table pipeline template.sub-skills/genomics-analysis/scripts/matrixtable_recipe_template.pyprints or dry-runs a dense MatrixTable analysis template.sub-skills/variant-datasets/scripts/vds_recipe_template.pyprints or dry-runs a VDS workflow template.sub-skills/batch-and-cli/scripts/batch_local_smoke.pyconstructs a local-only Batch smoke/template and supports help or dry-run checks before execution.
04 同仓库更多 Skills
passnet-triton-opt
Make a single PassNet Triton kernel fast AND numerically faithful: performance model (when a replacement can win at all), block/grid/warp tuning, autotune policy, launch overhead, and per-op numeric recipes to pass the dtype baseline tolerances. Use when a pass matches and is correct but speedup below expected, or when correctness fails by small numeric margins.
passnet-solve
END-TO-END playbook for solving one PassNet sample: analyze the computation graphs, decide the optimization strategy (what to fuse, what to replace, what to leave alone), drive the iteration loop, and maximize the sample score. This is the ENTRY skill — invoke it first for any PassNet optimization task; it tells you when to use passnet-pattern-fusion, passnet-triton-opt and passnet-feedback.
passnet-skill
PassNet GPU kernel optimization via compiler passes. Design and implement Triton-based optimization passes, create pass files under ./pass_dir/, self-evaluate with pass_evaluator, and iterate to maximize GPU speedup.
passnet-pattern-fusion
Author PassNet pass files: write patterns that actually MATCH the FX graph, pick fusion regions (which ops to absorb into one Triton kernel), and structure multi-pass submissions with the mandatory shared-dispatch architecture. Use when creating or fixing pattern()/replacement_args()/replacement_func() files, when a pass "failed to match", or when deciding how to fuse multiple kernels/ops.
passnet-orchestrate
Round/batch planning and decision support for PassNet graph optimization. Use for multi-sample triage, worker allocation, eval-budget policy, and post-eval keep/revert decisions. For solving one concrete sample end to end, use passnet-solve as the entry skill; consult this skill only when you need broader planning or a specific decision gate.
passnet-feedback
Fast, deterministic feedback for PassNet work: pre-flight pattern/match verification WITHOUT burning a GPU evaluation, per-node bottleneck analysis of a sample's graphs, and eval-log parsing into per-variant status + estimated score + failure classification. Use BEFORE every GPU evaluation (check_pattern), at the START of a sample (analyze_graph), and AFTER every evaluation (parse_eval_log).