standup
The standup skill generates a structured daily standup update by aggregating recent work activity across connected tools like source control, project trackers, and chat platforms. Use it each morning to automatically compile commits, pull requests, ticket changes, and discussions into a concise yesterday/today/blockers format, or manually input work details when tools aren't connected for quick formatting into a shareable standup summary.
git clone --depth 1 https://github.com/openyak/openyak /tmp/standup && cp -r /tmp/standup/backend/app/data/plugins/engineering/skills/standup ~/.claude/skills/standupSKILL.md
# /standup > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md). Generate a standup update by pulling together recent activity across your tools. ## How It Works ``` ┌─────────────────────────────────────────────────────────────────┐ │ STANDUP │ ├─────────────────────────────────────────────────────────────────┤ │ STANDALONE (always works) │ │ ✓ Tell me what you worked on and I'll structure it │ │ ✓ Format for daily standup (yesterday / today / blockers) │ │ ✓ Keep it concise and action-oriented │ ├─────────────────────────────────────────────────────────────────┤ │ SUPERCHARGED (when you connect your tools) │ │ + Source control: Recent commits and PRs │ │ + Project tracker: Ticket status changes │ │ + Chat: Relevant discussions and decisions │ │ + CI/CD: Build and deploy status │ └─────────────────────────────────────────────────────────────────┘ ``` ## What I Need From You **Option A: Let me pull it** If your tools are connected, just say `/standup` and I'll gather everything automatically. **Option B: Tell me what you did** "Worked on the auth migration, reviewed 3 PRs, got blocked on the API rate limiting issue." ## Output ```markdown ## Standup — [Date] ### Yesterday - [Completed item with ticket reference if available] - [Completed item] ### Today - [Planned item with ticket reference] - [Planned item] ### Blockers - [Blocker with context and who can help] ``` ## If Connectors Available If **~~source control** is connected: - Pull recent commits and PRs (opened, reviewed, merged) - Summarize code changes at a high level If **~~project tracker** is connected: - Pull tickets moved to "in progress" or "done" - Show upcoming sprint items If **~~chat** is connected: - Scan for relevant discussions and decisions - Flag threads needing your response ## Tips 1. **Run it every morning** — Build a habit and never scramble for standup notes. 2. **Add context** — After I generate, add any nuance about blockers or priorities. 3. **Share format** — Ask me to format for Slack, email, or your team's standup tool.
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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