Development Skills

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.

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.

Execute a Carmack Council plan task by task. Use when explicitly asked to implement a plan, do a "council implement", "carmack implement", "council build", or invoke /council-implement. Reads the output of /council-plan and builds each task sequentially, loading the relevant expert's reference document per task. Verifies after each task. Produces an implementation log for /council-review. Stack: Next.js App Router / React / TypeScript / tRPC / Prisma / Neon / Clerk.

Architect a feature with the Carmack Council before writing code. Use when explicitly asked to plan a feature, do a "council plan", "carmack plan", or invoke /council-plan. Carmack's philosophy chairs a council of domain experts — Troy Hunt (security), Martin Fowler (refactoring), Kent C. Dodds (frontend), Matteo Collina (Node.js), Brandur Leach (Postgres), Vercel Performance, Simon Willison (LLM pipeline), Karri Saarinen (UI quality), Vitaly Friedman (UX quality). Interactive feature discovery followed by parallel subagent dispatch. Produces a sequenced, attributed implementation plan with no code. Stack: Next.js App Router / React / TypeScript / tRPC / Prisma / Neon / Clerk.

Perform a rigorous Carmack Council code review. Use when explicitly asked to review code, do a "council review", "carmack review", or invoke /council-review. Carmack's philosophy chairs a council of domain experts — Troy Hunt (security), Martin Fowler (refactoring), Kent C. Dodds (frontend), Matteo Collina (Node.js), Brandur Leach (Postgres), Vercel Performance, Simon Willison (LLM pipelines), Karri Saarinen (UI quality), Vitaly Friedman (UX quality), Kent Beck (test quality). Uses parallel subagents for deep, independent review. Produces prioritised P1/P2/P3 findings. Stack: Next.js App Router / React / TypeScript / tRPC / Prisma / Neon / Clerk.

Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.

Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.

Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.

Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.

Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.

Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.

Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.

Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.

Systematic codebase exploration, architectural critique, and generation of Project Discovery Drafts for SDLC Phase 0.

Phase 6: Coding & Execution. God-Tier Autonomous Engineer implementing code strictly based on approved /spec/ and /plan/.

Custom Instructions: Clean Architecture & Clean Code Expert