Runs all necessary checks (lint, tests) and pushes to GitHub. Use this as the final safety gate.
Prepares the codebase for a commit by formatting code and helping identify temporary comments.
Fetches comments and reviews from the current GitHub Pull Request and formats them as Markdown.
Runs unit tests to quickly verify changes during the development loop.
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".
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.
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.
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.
Fix Dependabot security alerts by updating vulnerable npm dependencies. Use when the user mentions "dependabot", "security alerts", "vulnerability", "CVE", or wants to update packages with security issues.
Generate local PR/branch review artifacts for the current branch vs its base - an in-repo canonical spec (if present) or an inferred review `spec.md`, plus `system-design-review.md`, `code-review.md`, and `walkthrough.md` (via `.agents/skills/drive-pr-walkthrough/SKILL.md`). Writes artifacts to disk (next to the in-repo spec when present, otherwise under `wip/`). Use when the user asks for a local PR/branch review, a code review, a system design review, to "review this branch", or to produce written review docs. Do not modify implementation code.
Create a new transient project workspace under projects/<project>/ (folders + optional stub docs), then hand off to drive-create-spec / drive-generate-plan.
Write an intent-first walkthrough (semantic narrative) of a PR/branch or commit range - the overall purpose, the sequence of conceptual steps, the concrete behavior changes, and links to both implementation touchpoints and tests as evidence. Use during branch/PR review when the user asks for a walkthrough, narrative of changes, semantic diff, intent of commits, or “what changed and why” (not a file-by-file diff recap).
Generate and iteratively refine engineering specs from a description, conversation summary, or blank template. Use when the user wants to create a spec, PRD, product requirements document, write up requirements, or spec out a feature. Also use when summarising a conversation into a spec.
Bumps `biome` package versions (e.g. `@biomejs/biome`) using `pnpm`, aligns `biome.jsonc` files with the new version/s across the repository and runs biome-related checks. Use when required to update `biome` to a newer version - explicitly or implicitly (e.g. after running `pnpm up`, `pnpm update`, `pnpm upgrade` without specific package names).
Creates a GitHub PR with a conventional-commit title and a narrative description for prisma-next. Use when the user wants to create a pull request, open a PR, or submit changes for review.
Generate an execution plan from a spec, with milestones, tasks, and test coverage for all acceptance criteria. Use when the user wants to plan a project, break a spec into milestones, generate tasks from a spec, or create an execution plan. Optionally creates a Linear project.
Migrates a monorepo package from `tsup` to `tsdown`. Use when asked to migrate a package to `tsdown` and the package has used `tsup` thus far.
Detects and fixes accessibility issues in React/Fluent UI webviews. Use when reviewing code for screen reader compatibility, fixing ARIA labels, ensuring keyboard navigation, adding live regions for status messages, or managing focus in dialogs.
Azure Cosmos DB performance optimization and best practices guidelines for NoSQL, partitioning, queries, and SDK usage. Use when writing, reviewing, or refactoring code that interacts with Azure Cosmos DB, designing data models, optimizing queries, or implementing high-performance database operations.
AI agent governance, trust scoring, and policy enforcement powered by AgentMesh. Activate when: (1) user wants to enforce token limits, tool restrictions, or content policies on agent actions, (2) checking an agent's trust score before delegation or collaboration, (3) verifying agent identity with Ed25519 cryptographic DIDs, (4) auditing agent actions with tamper-evident hash chain logs, (5) user asks about agent safety, governance, compliance, or trust. Enterprise-grade: 1,600+ tests, merged into Dify (65K★), LlamaIndex (47K★), Microsoft Agent-Lightning (15K★).