debug
The debug skill guides users through a structured four-step process to identify and resolve software issues: reproduce the problem and understand its scope, isolate the affected component by reviewing recent changes and logs, diagnose the root cause through hypothesis testing and code tracing, and finally propose a fix with preventive measures. Use it when encountering error messages, stack traces, environment-specific failures, post-deployment issues, or unexpected behavior where the cause is unclear.
git clone --depth 1 https://github.com/openyak/openyak /tmp/debug && cp -r /tmp/debug/backend/app/data/plugins/engineering/skills/debug ~/.claude/skills/debugSKILL.md
# /debug > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md). Run a structured debugging session to find and fix issues systematically. ## Usage ``` /debug $ARGUMENTS ``` ## How It Works ``` ┌─────────────────────────────────────────────────────────────────┐ │ DEBUG │ ├─────────────────────────────────────────────────────────────────┤ │ Step 1: REPRODUCE │ │ ✓ Understand the expected vs. actual behavior │ │ ✓ Identify exact reproduction steps │ │ ✓ Determine scope (when did it start? who is affected?) │ │ │ │ Step 2: ISOLATE │ │ ✓ Narrow down the component, service, or code path │ │ ✓ Check recent changes (deploys, config changes, dependencies) │ │ ✓ Review logs and error messages │ │ │ │ Step 3: DIAGNOSE │ │ ✓ Form hypotheses and test them │ │ ✓ Trace the code path │ │ ✓ Identify root cause (not just symptoms) │ │ │ │ Step 4: FIX │ │ ✓ Propose a fix with explanation │ │ ✓ Consider side effects and edge cases │ │ ✓ Suggest tests to prevent regression │ └─────────────────────────────────────────────────────────────────┘ ``` ## What I Need From You Tell me about the problem. Any of these help: - Error message or stack trace - Steps to reproduce - What changed recently - Logs or screenshots - Expected vs. actual behavior ## Output ```markdown ## Debug Report: [Issue Summary] ### Reproduction - **Expected**: [What should happen] - **Actual**: [What happens instead] - **Steps**: [How to reproduce] ### Root Cause [Explanation of why the bug occurs] ### Fix [Code changes or configuration fixes needed] ### Prevention - [Test to add] - [Guard to put in place] ``` ## If Connectors Available If **~~monitoring** is connected: - Pull logs, error rates, and metrics around the time of the issue - Show recent deploys and config changes that may correlate If **~~source control** is connected: - Identify recent commits and PRs that touched affected code paths - Check if the issue correlates with a specific change If **~~project tracker** is connected: - Search for related bug reports or known issues - Create a ticket for the fix once identified ## Tips 1. **Share error messages exactly** — Don't paraphrase. The exact text matters. 2. **Mention what changed** — Recent deploys, dependency updates, and config changes are top suspects. 3. **Include context** — "This works in staging but not prod" or "Only affects large payloads" narrows things fast.
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.
This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
Set up your bio-research environment and explore available tools. Use when first getting oriented with the plugin, checking which literature, drug-discovery, or visualization MCP servers are connected, or surveying available analysis skills before starting a new project.
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