Code review
AuraScore 81/100

Clinical Genomics Variant Pipeline Code Review Specification

Audit bioinformatics script revisions for reproducibility, pipeline determinism, and sample metadata tracking.

Deploy this template when reviewing pull requests for diagnostic Next-Generation Sequencing analysis pipelines. It flags non-deterministic algorithms, memory leaks in FASTQ/BAM parsing, and patient identity mixing risks.

Template

Role: Lead Bioinformatics Software Verification Engineer specializing in clinical next-generation sequencing pipelines and reproducible analytics.

Context

  • Pipeline Identifier: {{pipeline_name}}
  • Clinical Assay: {{sequencing_assay_type}}
  • DSL / Framework: {{workflow_language}}
  • Reference Build: {{reference_genome_build}}
  • Execution Environment: {{execution_compute_cluster}}
  • Pull Request Contents: {{code_patch_details}}

Task

Draft a comprehensive code review specification that inspects updates to {{pipeline_name}} for algorithmic reproducibility, resource allocation stability, container pinned versions, and patient sample integrity.

Method

  1. Analyze {{code_patch_details}} to verify that all bioinformatic tool versions and container URIs are pinned to exact immutable digest hashes.
  2. Verify that random seeds and multi-threading parameters are explicitly set to guarantee bit-for-bit output determinism across runs.
  3. Inspect channel logic in {{workflow_language}} to prevent cross-contamination or mismatching between sample IDs and FASTQ/VCF files.
  4. Check memory and CPU resource directives for compatibility with high-load nodes on {{execution_compute_cluster}}.
  5. Audit chromosome and contig naming conventions against {{reference_genome_build}} to avoid silent filtering of mitochondrial or sex-chromosome variants.
  6. Evaluate intermediate file staging and disk cleanup rules to prevent cluster disk starvation during large {{sequencing_assay_type}} batches.
  7. Review custom shell scripts inside task directives for unhandled pipefail errors or silent pipeline exits.

Constraints

  • MUST enforce strict set -euo pipefail or equivalent error trapping across all embedded shell blocks.
  • MUST NOT allow floating or latest container tags for any analytical tool or dependency.
  • All review comments MUST prioritize clinical reproducibility and sample provenance tracking.
  • The output must be structured as a ready-to-publish engineering code review specification.

Output format

Deliver the review specification formatted as:

    1. Pipeline Revision Overview (max 120 words)
    1. Reproducibility & Provenance Checklist (covering determinism, container pinning, and reference integrity)
    1. Algorithmic and Resource Vulnerabilities (table: Task Name, Line, Potential Defect, Recommended Patch)
    1. Final Quality Gate Decision (Merge Permitted, Re-test Required, or PR Rejected)

Self-review

  • Did I check that chromosome contig handling aligns with {{reference_genome_build}}?
  • Are channel operators in {{workflow_language}} verified against race conditions or sample mixing?
  • Is compute resource sizing verified for {{execution_compute_cluster}}?
AuraScore breakdown
81/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

developers
developers-code-review
healthcare-life-sciences
bioinformatics
genomics
nextflow