This skill should be used when the user says "taskify", "create task list", "create tasks", "convert plan to tasks", "generate tasks from plan", "create tasks from plan", "turn plan into tasks", "arness code taskify", or wants to convert a structured project plan's TASKS.md into an executable Claude Code task list with proper dependency management.

This skill should be used when the user says "assessing", "arness assessing", "assess", "assess codebase", "technical review", "codebase assessment", "find improvements", "what should I improve", "tech debt review", "pattern compliance check", "codebase health check", "improvement plan", "review my codebase", "what needs fixing", "code quality check", "audit my code", "run an assessment", "arn-assessing", or wants a comprehensive technical assessment of the codebase against stored patterns followed by prioritized improvement execution. Chains to arn-implementing if improvements are identified.

This skill should be used when the user says "save infra plan", "save infrastructure plan", "structure infra plan", "create infra project", "finalize infra plan", "arn infra save plan", "infra save plan", "save change plan", "structure infrastructure plan", "finalize infrastructure plan", "arn-infra-save-plan", or wants to convert a PLAN_PREVIEW_INFRA_*.md into a structured project directory with infrastructure-specific templates, report templates, and progress tracking.

This skill should be used when the user says "document project", "generate docs", "create documentation", "write docs", "arness code document", "arn-code-document-project", "document this feature", "document this fix", or wants to generate developer documentation for a completed feature or bug fix. Reads plan artifacts, spec files, execution reports, and git diff to produce accurate documentation. Do NOT use this for general-purpose documentation — this is specifically for Arness pipeline projects.

This skill should be used when the user says "document infra change", "infrastructure documentation", "generate runbook", "infra docs", "arn infra document", "create infra changelog", "document infrastructure", "generate infrastructure docs", "infra documentation", "create runbook", "generate changelog", "arn-infra-document-change", or wants to generate operational documentation (runbooks, architecture updates, changelogs, environment docs) from completed infrastructure changes.

This skill should be used when the user says "review implementation", "review the project", "check implementation", "quality review", "validate implementation", "implementation review", or wants a post-execution quality gate to verify that the implementation follows the project's stored code and testing patterns and matches the plan. Reports issues as ERRORS, WARNINGS, INFO with a verdict. Do NOT use this for reviewing PRs (use arn-code-review-pr) or validating plans (use arn-code-review-plan).

This skill should be used when the user says "batch merge", "merge batch", "arness batch merge", "arn-code-batch-merge", "merge all PRs", "merge batch PRs", "merge the batch", "merge implemented features", "batch merge PRs", "merge open PRs", "merge all feature PRs", "combine batch PRs", "land the batch", "land all PRs", or wants to discover open batch implementation PRs, analyze them for conflicts, determine an optimal merge order, and execute merges with user-guided conflict resolution. This skill is typically invoked after arn-code-batch-implement completes and chains to arn-code-batch-simplify upon completion.

This skill should be used when the user says "execute task N", "run task N", "implement task N", "re-run task N", "retry task N", "run single task", or wants to execute a single specific task from the task list with optional review. This is for ONE task only — for executing the full plan (all tasks), use arn-code-execute-plan instead.

This skill should be used when the user says "create issue", "file issue", "arness code issue", "arness code create issue", "arn-code-create-issue", "report bug", "request feature", "add to backlog", "create GitHub issue", "create Jira issue", "file a bug", "submit issue", "log issue", "open issue", or wants to create an issue in the current repository with Arness labels for type and priority. Requires an issue tracker (GitHub or Jira) to be configured. Do NOT use this for picking/browsing existing issues — use /arn-code-pick-issue for that.

Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use anndata.

Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS data processing. For simple spectral comparison and metabolite ID use matchms.

Vendor-agnostic lab automation framework. Use when controlling multiple equipment types (Hamilton, Tecan, Opentrons, plate readers, pumps) or needing unified programming across different vendors. Best for complex workflows, multi-vendor setups, simulation. For Opentrons-only protocols with official API, opentrons-integration may be simpler.

Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.

Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.

Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.