Development Claude Skills
8 673 development skills from 759 authors. They write and change code. Half of them fit into 1 830 tokens or less — that is what one costs your context window when the agent loads it. 1 213 ship runnable scripts rather than instructions alone. 42 of them cannot work without an MCP server, most often rube. We also found 1 239 copies of these same skills sitting in other people's repositories — counted once here, not 1 239 times.
8 673 unique 759 authors 10 updated this month 1 368 from vendors
Reads, writes, and manipulates DICOM (Digital Imaging and Communications in Medicine) medical imaging files with the pydicom Python library. Use when reading/writing/modifying DICOM data, extracting pixel data from CT, MRI, X-ray, or ultrasound images, anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM to other formats, handling compressed DICOM, or processing medical imaging datasets for PACS systems, radiology workflows, and healthcare imaging applications. Part of the AlterLab Academic Skills suite.
Survival analysis and time-to-event modeling in Python with scikit-survival. Use when working with censored survival data, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating predictions with concordance index or Brier score, handling competing risks, or implementing any time-to-event workflow. Part of the AlterLab Academic Skills suite.
Process-based discrete-event simulation in Python with SimPy — processes, queues, shared resources, and time-based events. Use when simulating systems where entities contend for shared resources over time, such as manufacturing systems, service operations, network traffic, or logistics. Part of the AlterLab Academic Skills suite.
Symbolic mathematics in Python with SymPy — solve equations algebraically, perform calculus (derivatives, integrals, limits), manipulate algebraic expressions, work with symbolic matrices, and generate executable code from formulas. Use when exact symbolic results are needed rather than numerical approximations, or for physics, number-theory, and geometry computations involving variables and parameters. Part of the AlterLab Academic Skills suite.
Query BindingDB for measured protein-ligand binding affinities (Ki, Kd, IC50, EC50) via its keyless REST API or the full TSV download, searching by target (UniProt ID), compound (SMILES), or pathogen. Use when looking up experimental binding constants, profiling inhibitors of a protein target, doing lead optimization, polypharmacology analysis, or structure-activity relationship (SAR) studies; for curated bioactivity mining or drug-like compound library screening at scale prefer alterlab-chembl instead. Part of the AlterLab Academic Skills suite.
Query cBioPortal via its keyless REST API for cancer genomics across TCGA, GENIE, MSK-IMPACT and hundreds of studies — somatic mutations, copy-number alterations (GISTIC), mRNA/protein expression, structural variants, and patient-level clinical/survival data. Use when asked how often a gene is mutated/amplified/deleted in a tumor type, to profile oncogenes or tumor suppressors across cancers (pan-cancer alteration frequency), to pull patient-level mutations joined to OS/clinical outcomes, or to validate a cancer target from cohort genomics. For germline variant pathogenicity use alterlab-clinvar; for mutational-signature (SBS) decomposition use alterlab-cosmic; for CRISPR/RNAi gene-dependency use alterlab-depmap; for aggregated target-disease evidence use alterlab-opentargets. Part of the AlterLab Academic Skills suite.
Query ChEMBL via the chembl_webresource_client Python client for curated bioactive molecules and drug-like compound libraries at scale — search compounds by structure or physicochemical properties, retrieve bioactivity measurements (IC50, Ki, EC50), and find inhibitors of a target. Use when screening chemical libraries, mining curated bioactivity for a protein, running SAR studies, or sourcing medicinal-chemistry data; for measured protein-ligand binding affinities (Ki/Kd/IC50) prefer alterlab-bindingdb instead. Part of the AlterLab Academic Skills suite.
Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use when identifying cancer-specific genetic vulnerabilities, finding synthetic lethal interactions, checking whether a gene is essential in given cell lines, or validating oncology drug targets. Part of the AlterLab Academic Skills suite.
Provide direct REST API access to UniProt (Swiss-Prot/TrEMBL) for protein sequence searches, FASTA retrieval, functional annotations (GO terms, domains), and cross-database ID mapping. Use when looking up a protein entry, fetching a protein FASTA sequence, or mapping accessions between databases over raw HTTP/REST; for EXPERIMENTAL 3D structures prefer alterlab-pdb, for AI-PREDICTED 3D structures prefer alterlab-alphafold-db, for protein-protein interaction networks prefer alterlab-string-db, and for Python workflows spanning many databases prefer bioservices instead. Part of the AlterLab Academic Skills suite.
Runs computational fluid dynamics simulations with the FluidSim Python framework using pseudospectral FFT methods, with HPC support and output analysis. Use when simulating Navier-Stokes equations (2D/3D), shallow water equations, or stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Part of the AlterLab Academic Skills suite.
Reads, writes, and analyzes geospatial vector data with the GeoPandas Python library (shapefiles, GeoJSON, GeoPackage), with PostGIS support and integration with matplotlib, folium, and cartopy. Use for spatial analysis and geometric operations — buffer analysis, spatial joins and overlays between datasets, dissolving boundaries, clipping, calculating areas and distances, reprojecting coordinate systems, choropleth mapping, or converting between vector file formats. This is for tabular vector data; for raster/satellite/DEM work, spectral indices (NDVI), or spatial ML on earth observation prefer the geomaster skill. Part of the AlterLab Academic Skills suite.
Runs high-performance genomic interval analysis with gtars (databio), a Rust toolkit with Python bindings — the performance-critical backend for the geniml ML library. Use when computing overlaps/jaccard/coverage between BED region sets, indexing intervals with IGD, generating uniwig accumulation/coverage tracks, tokenizing genomic regions for ML, splitting single-cell fragments into pseudobulks, or computing GA4GH refget sequence digests. NOT for training region embeddings (use alterlab-geniml) or non-genomic spatial joins (use alterlab-geopandas). Part of the AlterLab Academic Skills suite.
Covers geospatial science across remote sensing, GIS, spatial analysis, and machine learning for earth observation — satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), raster and DEM operations, spectral indices (NDVI/EVI/NDWI), spatial statistics, point cloud processing, network analysis, and cloud-native workflows (STAC, COG, Planetary Computer), with examples across Python, R, Julia, JavaScript, C++, Java, Go, and Rust. Use for remote sensing workflows, satellite/raster image classification, terrain/slope/hillshade analysis, spatial ML on earth-observation data, hydrological modeling, marine spatial analysis, or atmospheric science. For pure tabular vector work with no raster/EO aspect (plain GeoPandas sjoin, buffer, overlay, dissolve, choropleths) prefer the geopandas skill; for celestial-sphere astronomy coordinates (ICRS/galactic, FITS, WCS) prefer the astropy skill. Part of the AlterLab Academic Skills suite.
Runs Python code in the cloud with Modal — serverless containers, on-demand GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that need GPU acceleration or dynamic scaling. Part of the AlterLab Academic Skills suite.
Scaffolds program-level Assurance-of-Learning (AoL) documentation for AACSB (2020 Standard 5) and ABET (Criterion 3 Student Outcomes, Criterion 4 Continuous Improvement) accreditation — program learning outcomes / competency goals, curriculum-to-outcome mapping matrices, direct- and indirect-assessment plans, rubric design, and closing-the-loop continuous-improvement narratives — and validates the structure of an outcome-mapping matrix with scripts/aol_matrix.py. Use when the user needs AACSB or ABET assurance-of-learning material, a program-learning-outcomes set, a curriculum/outcome map or coverage matrix, a direct/indirect assessment plan, a closing-the-loop report, or accreditation self-study text. For single-course design or course rubrics prefer alterlab-teaching-design; for post-award grant reports prefer alterlab-grant-reporting. Part of the AlterLab Academic Skills suite.
Develops and runs genomics pipelines on the DNAnexus cloud platform using the dxpy Python SDK and dx CLI — build apps/applets, write dxapp.json, upload/download data, and execute jobs/workflows over FASTQ/BAM/VCF files. Use when building or running a DNAnexus app, applet, or workflow, writing dxapp.json, using dx-app-wizard or dx build, calling dxpy (find_data_objects, DXApplet.run, upload_local_file), or uploading/downloading sequencing data on DNAnexus. For LatchBio (Latch SDK @workflow/@task, LatchFile) use alterlab-latchbio instead; for Benchling LIMS use alterlab-benchling. Part of the AlterLab Academic Skills suite.
Integrates the Benchling R&D platform via its REST API and SDK — access the registry (DNA, proteins), inventory, ELN entries and workflows, build Benchling Apps, and query the Benchling Data Warehouse. Use when automating Benchling lab data management, syncing sample registry or inventory records, scripting ELN entries/workflows, or running SQL against the Benchling Data Warehouse. Part of the AlterLab Academic Skills suite.
Manages microscopy image data on an OMERO server via the OMERO Python API (BlitzGateway) — access images, retrieve datasets, read pixel data, manage ROIs and annotations, and batch-process. Use when connecting to an OMERO server, pulling microscopy images or datasets, analyzing pixels, managing ROIs/annotations, or running high-content screening and microscopy workflows. Part of the AlterLab Academic Skills suite.
Programs lab automation with PyLabRobot, a vendor-agnostic Python framework that unifies control across Hamilton, Tecan, Opentrons, plate readers, and pumps, with simulation support. Use when controlling multiple equipment types or needing unified cross-vendor programming for complex, multi-vendor liquid-handling workflows. For Opentrons-only protocols with the official API, alterlab-opentrons may be simpler. Part of the AlterLab Academic Skills suite.
Runs Qualitative Comparative Analysis — crisp-set (csQCA), multi-value (mvQCA), and fuzzy-set (fsQCA) — for small-to-medium-N configurational research: calibrating raw data into set membership, building and refining a truth table, and Boolean minimization into conservative / parsimonious / intermediate solutions with consistency and coverage. Because there is no maintained Python QCA library, it shells out to R's QCA package (calibrate, truthTable, minimize) via Rscript and documents that dependency honestly rather than faking a Python API. Use when the request mentions QCA, fsQCA, csQCA, configurational or set-theoretic analysis, necessary/sufficient conditions, truth tables, or calibration of conditions. For net-effect estimation of a single treatment prefer alterlab-causal-inference; for interpretive analysis prefer alterlab-qualitative-methods. Part of the AlterLab Academic Skills suite.
Builds plots with the matplotlib Python library (pyplot and the object-oriented Figure/Axes API) for full low-level customization, exporting to PNG/PDF/SVG. Use when fine-grained control over individual plot elements is needed — custom line/scatter/bar/histogram/heatmap/contour/box/violin/3D plots, rcParams and style-sheet tuning, or GridSpec subplot layouts inside a scientific Python workflow. Does NOT cover opinionated journal-ready multi-panel figure workflows (Nature/Science/Cell formatting, colorblind-safe palettes, significance annotations); for those prefer alterlab-scientific-viz instead. Part of the AlterLab Academic Skills suite.
| Authors and modifies Gradle build definitions, project structure, build logic, and delivery wiring, including dependencies and catalogs, convention plugins, custom tasks, build lifecycle, Kotlin DSL, managed types and providers, toolchain and Java builds, configurations and variants, binary plugin development, Kotlin compiler options, publishing via the Central Portal, CI, locking, build scans, Worker API, continuous builds, and advanced configuration such as service injection, build services, value sources, and project isolation. ## Positive Triggers (when to activate) ## Negative Triggers (when NOT to activate)
Generate a TestContainers Java test class that spins up ShadowTraffic in-process to populate a Kafka topic with synthetic data during tests. Invokes the kafka-shadowtraffic skill to build the ShadowTraffic config, then adapts it for a containerized test network and scaffolds a JUnit 5 test class with Kafka, optional Schema Registry, and ShadowTraffic containers wired together. Use when the user says "set up a Java TestContainers test with ShadowTraffic", "write a Java test that generates Kafka data", "create a TestContainers test for my Kafka consumer", "I want to test my Kafka consumer with synthetic data in Java", or "scaffold a ShadowTraffic Java test". Do NOT use for non-Java projects, standalone Docker setups (use kafka-shadowtraffic instead), or creating Kafka topics.
Scaffold a production-ready Python Kafka producer and consumer using `confluent-kafka-python`, with Schema Registry, graceful shutdown, idempotent producer, tests and a complete project layout. Discovers the target topic, partition count and registered JSON Schema directly from the live cluster via any attached Kafka MCP server (Lenses MCP, Confluent's MCP, Aiven's, custom) before asking the user. Use when user says "build a Python Kafka consumer", "scaffold a Kafka client in Python", "add Kafka to my Python service", "consume from `<topic>` in Python" or "write a Kafka producer". Do NOT use for Kafka Connect connectors, schema-evolution reviews or non-Python clients.
Review Kafka producer and consumer performance configurations in both the live cluster (via Lenses MCP) and the codebase. Flags un-tuned defaults, anti-patterns and missing best practices. Use when user says "review Kafka performance", "check producer configs", "tune Kafka settings" or asks about throughput, batching or compression. Do NOT use for cluster sizing or capacity planning.
Create new GitLab stack projects from templates with proper directory structure, git configuration, validation hooks, and documentation. Use when initializing new Docker stack projects that follow GitLab stack patterns.
Manages Docker secrets for GitLab stack projects, ensuring secrets are never in .env or docker-compose.yml, properly stored in ./secrets directory, and securely integrated with Docker secrets. Use when users need to create secrets, migrate from environment variables, validate secret configuration, audit secret usage, or ensure secrets are never committed to git.
Comprehensive design architecture review for Python backend applications. Use this skill when users ask you to review, analyze, or provide feedback on backend architecture designs, system design documents, or Python application architecture. Covers scalability, security, performance, database design, API design, microservices patterns, deployment architecture, and best practices.
Executes code snippets by saving them to a temporary file and running them via bash commands (e.g., node, python, go run) to test logic or compute results.
Downloads files, images, or media from URLs using curl, wget, or python scripts. Can handle bulk downloads and save them to a specified directory.
Assists with git operations, such as checking branch status, creating commits, stashing changes, resolving conflicts, or analyzing git history.
Guide users through obtaining and configuring a Vapi API key. Use when the user needs to set up Vapi, when API calls fail due to missing keys, or when the user mentions needing access to Vapi's voice AI platform.
Scaffold a complete Vapi voice-agent project from a ROUGH_DRAFT.md spec. Generates package.json, tsconfig.json, .env.example, .gitignore, and the full TypeScript framework — scenario registry, per-language voice/transcriber stack, prompt composer, assistant builder, and an idempotent bootstrap script — plus one rough first-draft body.md per scenario. Drop this skill in any project's .cursor/skills/ folder (or ~/.cursor/skills/ for global use), write a ROUGH_DRAFT.md at the project root, name the skill, and `bun run bootstrap` puts the entire fleet live in dashboard.vapi.ai. Use when the user asks to scaffold or bootstrap Vapi voice agents from a rough draft, build a Vapi assistant fleet, or invokes this skill by name. Targets Bun + TypeScript + @vapi-ai/server-sdk.
> Deep code audit that finds dead wiring, silent failures, unfinished features, placeholder stubs, bloated files, and unnecessary complexity. Produces an actionable doing a thorough PR review of the entire codebase. "find placeholders", "check for stubs", "prune the code", "code cleanup", "implementation review", "completeness check", "find unused code".
> Use when building, deploying, or using PowerApps Component Framework (PCF) controls. Covers field controls and dataset controls, React virtual controls, control lifecycle, "custom control", "component framework", "dataset control", "virtual control", "pac pcf", "pcf init", "pcf push", "ControlManifest", "StandardControl", "ReactControl", "updateView", "getOutputs".
> Use when programmatically creating, modifying, or querying Dataverse schema and metadata via the Web API (OData v4.0). Covers table/column/relationship definitions, solution ALM, form and view XML construction, app module composition, global option sets, business rules, "entitydefinitions", "web api schema", "create dataverse table", "create dataverse column", "fetchxml", "formxml", "layoutxml", "dataverse solution", "dataverse relationship", "odata dataverse", "metadata api", "publish customizations", "dataverse alm", "grid control", "editable grid", "business rule", "rich text", "auto-number", "file column", "image column", "pcf control", "security role", "column security", "environment variable", "custom api", "data migration", "solution import".
> Use when building, scaffolding, debugging, or deploying Microsoft Power Apps Code Apps using React, Vue, or TypeScript in VS Code. Handles project initialization via pac CLI, Dataverse and connector data access via the @microsoft/power-apps SDK, power.config.json configuration, Vite build pipelines, and deployment to Power Platform environments. "vibe coding", "scaffold power app", "deploy code app".
Record a Chrome browser tab to video via CLI. Use when the user wants to capture a screen recording of a browser tab. Supports list, start, stop, caption, and status subcommands. No debug mode required — uses a Chrome extension.
Parse RSS 2.0/0.9x, Atom 1.0, RSS 1.0 (RDF) and podcast (itunes:*) feeds in Python into typed pydantic v2 models with the `rss-parser` package. Use this skill whenever a task involves reading, polling, validating or normalizing a feed in Python - building a feed reader or aggregator, ingesting podcast episodes, deduplicating items while polling, extracting namespaced tags like dc:creator or media:content, or converting feed XML to JSON - even when the user only says "parse this RSS", names feedparser, or hands over a feed URL without naming a library.
> Build beautiful, editable Feishu / Lark (飞书) whiteboards with deliberate composition, not just good colour. Use whenever the user wants to create or draw a Feishu whiteboard, infographic, diagram, poster, system map, or visual explainer and wants it to actually look designed — strong visual hierarchy, intentional layout, breathing room — rather than a templated grid of boxes. The agent commits to a design brief (composition archetype + colour strategy + type roles + an anti-cliché check) BEFORE drawing, builds against a coordinate skeleton with a fixed type scale and spacing grid, then runs a design-critique pass (hierarchy / balance / density / contrast / alignment) after rendering and fixes the weakest axis until it clears. Returns the doc link and the rendered image, and offers to switch palettes. Requires lark-cli (npm @larksuite/cli) installed and authenticated, and a Feishu/Lark account.
Fetches official documentation for external libraries and frameworks (React, Next.js, Prisma, FastAPI, Express, Tailwind, MongoDB, etc.) with 60-90% token savings via content-type filtering. Use this skill when implementing features using library APIs, debugging library-specific errors, troubleshooting configuration issues, installing or setting up frameworks, integrating third-party packages, upgrading between library versions, or looking up correct API patterns and best practices. Triggers automatically during coding work - fetch docs before writing library code to get correct patterns, not after guessing wrong.
| Creates production-grade, reusable skills that extend Claude's capabilities. This skill should be used when users want to create a new skill, improve an existing skill, or build domain-specific intelligence. Gathers context from codebase, conversation, and authentic sources before creating adaptable skills.
Generates a complete fullstack application template with Python FastAPI backend and React Vite frontend. Includes OpenAI ChatGPT integration, CORS configuration, comprehensive error handling, and a modern Tailwind CSS + shadcn/ui React UI. Use this skill when the user wants to bootstrap a new fullstack web application project with both API backend and web frontend components ready to go.
Use when writing or editing code in an existing codebase — before adding comments, docstrings, try/catch blocks, validation, logging, or TODOs the surrounding file doesn't have, and before explaining your style choices in the reply.
Multi-vendor network device SSH automation using Python Netmiko — connecting, sending commands, parsing output, handling enable mode, error handling, and batch operations across device lists.
Create, schedule, run, and debug MotherDuck Flights — Python jobs that run on MotherDuck compute. Use whenever someone wants to create a flight, schedule a Python script or recurring job on MotherDuck, set up scheduled ingestion from Postgres, dlt sources, S3, BigQuery, Snowflake, or APIs, refresh aggregates or transformations on a cron, or operate flights with get_flight_guide, create_flight, run_flight, flight logs, secrets, schedules, and versions.
MotherDuck REST API control-plane reference. Use when calling api.motherduck.com or MotherDuck MCP admin tools to provision service accounts, manage tokens, configure Ducklings, or mint Dive embed sessions. Not for SQL or data-plane query work.
Create, schedule, run, and debug MotherDuck Flights — Python jobs that run on MotherDuck compute. Use whenever someone wants to create a flight, schedule a Python script or recurring job on MotherDuck, set up scheduled ingestion from Postgres, dlt sources, S3, BigQuery, Snowflake, or APIs, refresh aggregates or transformations on a cron, or operate flights with get_flight_guide, create_flight, run_flight, flight logs, secrets, schedules, and versions.