Loading...
Loading...
Found 2,408 Skills
Vercel Queues guidance — durable topics with at-least-once delivery, independent consumer groups, retries, delays, and idempotency keys via @vercel/queue (JS) or vercel-queue (Python). Use when deferring background work, buffering traffic, fanning out events, or choosing between Queues and Workflows.
UNNC AIM team's repository and code standards. Covers repository naming (competition / aim- academic-year / reusable), branch and Conventional Commits rules, AI-agent git behavior (never commit / push unless the user explicitly asks in the current task; never call the gh CLI without explicit permission; never add an AI Co-Authored-By trailer; suggest a commit message at the end of a code-changing task), and formatting standards for Python (autopep8 / PEP 8), C++ (clang-format / clang-tidy), TypeScript (Prettier + ESLint, pnpm), Go (gofmt), CMake, and Markdown / YAML / JSON, with ready-to-copy canonical rule files. Use when creating or naming a repository, validating a ROS2 package.xml name, creating a branch, writing a git commit message, on any task that could involve committing, pushing, or calling gh, composing a competition workspace (git submodules under src/), or setting up Python, C++ (.clang-format / setup.cfg / .clang-tidy), TypeScript (Prettier / ESLint / pnpm) or Go (gofmt) tooling, styling a CMakeLists.txt or Markdown / YAML / JSON files, or applying the team's recommended git configuration.
Migrate Python LangChain, LangGraph, or Deep Agents applications to Pydantic AI and, when the source uses harness features, Pydantic AI Harness. Use for LangChain agents, chains, LCEL, direct LangGraph graphs, persistence, interrupts, streaming, and `create_deep_agent` projects with planning, filesystem or sandbox backends, skills, memory, subagents, permissions, approvals, or Deep Agents Code hosts.
Clones, analyzes, plans, and migrates legacy codebases (C++, Fortran, Java, Python, C, Shell) from a GitHub URL or local directory to a modern, high-performance, deterministic, 100% Safe Rust architecture, tracking live telemetry across every task bucket, test failure/fix cycle, and Profile-Guided Optimization (PGO) stage, and producing a first-person AI storytelling report and interactive HTML infographic. Use when migrating a GitHub repository or local codebase to Rust, porting legacy C++, Fortran, Python, or Java workloads to memory-safe Rust, designing Struct-of-Arrays (SoA) or SIMD Rust architectures, or optimizing Rust ports with LLVM PGO. Don't use for unrelated non-Rust refactoring or single-line syntax questions.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
Write the release notes for a Playwright release - the Node.js notes in docs/src/release-notes-js.md together with the Python, Java and .NET translations, and the GitHub markdown for each language's release. Use when asked to write, translate or publish release notes.
Insecure deserialization exploitation across Java, PHP, .NET, Python, Node.js, and Ruby. Covers gadget chain construction with ysoserial/phpggc/ysoserial.net, ObjectInputStream and BinaryFormatter sink identification, pickle __reduce__ RCE, phar:// wrapper abuse, Jackson polymorphic typing, Json.NET TypeNameHandling, ViewState tampering, node-serialize IIFE injection, Ruby Marshal.load and YAML.load gadgets, framework-specific chains for Spring/Hibernate/Laravel/Symfony, modern attack surfaces including Kubernetes admission webhooks and message queue consumers, WAF bypass through encoding layers and content-type manipulation, and serialVersionUID/JMX/RMI vectors. Activate when the engagement involves deserialization sinks, serialized data in cookies or request bodies, gadget chain development, magic method abuse, ysoserial payload generation, or any review of marshalling and unmarshalling logic in target applications.
Migrate Python LangChain Deep Agents applications to Pydantic AI and Pydantic AI Harness. Use when the source uses the upstream `deepagents` package or demonstrably reproduces its middleware, backends, skills, memory, subagents, or sandbox contracts. Use `migrating-langchain-to-pydantic-ai` for plain LangChain, LangGraph, or LCEL migrations and `pydantic-ai-harness` for greenfield Harness usage.
Diagnoses and root-causes Amazon MWAA workflow failures across Provisioned (Python DAG) and Serverless (YAML workflow) environments. Provisioned uses aws mwaa invoke-rest-api, CloudWatch log groups, and get-environment; Serverless uses aws mwaa-serverless API (GetWorkflowRun, ListWorkflowRuns, GetTaskInstance) and CloudWatch logs. Covers failed runs and tasks, DAGs not appearing, import errors, worker OOM, IAM denials, and dependency drift. Triggers on: DAG failed, task failed, workflow run failed, MWAA error, debug my DAG, why did my workflow fail, DAG not showing up, MWAA import error, requirements failing, worker crashed, serverless run failed. Not applicable to authoring workflows (handled by authoring-mwaa-workflow), running or smoke-testing a workflow (handled by testing-mwaa-workflow), or CI-CD deploy failures.
Authors and deploys MWAA workflow artifacts: Python Airflow DAGs for provisioned environments or YAML workflow files for Serverless. Covers operator selection, timeout design, retry strategy, scheduling, failure notifications, idempotency, and MWAA Serverless schema compliance. Deploys the artifact (S3 DAG upload or Serverless CreateWorkflow/UpdateWorkflow), creates an environment inline when approved, and redeploys fixes, then optionally hands off to testing-mwaa-workflow. Triggers on: create a DAG, write a pipeline, build a workflow, orchestrate tasks, Airflow DAG, data pipeline, schedule a job, deploy a DAG, deploy a workflow, YAML workflow. Not applicable to converting or migrating existing DAGs between provisioned and serverless (conversion is out of scope), running or smoke-testing a deployed workflow (handled by testing-mwaa-workflow) or diagnosing a failed run (handled by debugging-mwaa-workflow).
Derive height, tangent-space normal and packed MER (metalness, emissive, roughness) maps from an albedo/base-color texture for Blockbench PBR materials, using PyPBR (Python) or vgpu + Dawn WebGPU (Node.js), with optional Depth Anything V2 height estimation and MER inferred from the albedo plus the normal's curvature. Use after a color texture exists (painted, imported, or generated with blockbench-gpt-image-textures) when a material needs normal, heightmap or MER channels, a Bedrock RTX texture_set, or when asked to make a normal map, bump map, height map, roughness, metalness or emissive map from a texture. Triggers on albedo to normal, normal map from texture, height map from albedo, MER map, roughness from texture, metalness map, emissive map, Depth Anything, PyPBR, vgpu, Sobel normal, DirectX vs OpenGL normal, texture_set normal, heightmap or metalness_emissive_roughness layer.
Migrate Python OpenAI Agents SDK applications to Pydantic AI and, when warranted, Pydantic AI Harness. Use for `agents.Agent`, `Runner`, function tools, handoffs, guardrails, sessions, human approval, streaming, or `SandboxAgent`. Do not use for applications built directly on the OpenAI Responses API without the Agents SDK runtime.