DevOps & Infra

catcorner22 Bundle

Guide users building apps, scripts, CI pipelines, or automations on top of the Cursor TypeScript SDK (`@cursor/sdk`). Use this skill whenever the user mentions integrating, installing, or writing code against the Cursor SDK; whenever they say `Agent.create`, `Agent.prompt`, `Agent.resume`, `agent.send`, `run.stream`, `CursorAgentError`, or `@cursor/sdk`; whenever they ask to run Cursor agents programmatically from a script, CI/CD pipeline, GitHub Action, backend service, or any other code that isn't the Cursor IDE itself; and whenever they want to pick between local and cloud runtime, configure MCP servers for an SDK agent, or handle streaming, cancellation, or errors from an SDK agent. Also trigger when a user is wiring Cursor into an automation, writing a bot that runs Cursor, or porting REST `/v1/agents` calls to the SDK. Scope boundary — this skill is ONLY for driving Cursor's own coding agent from code (`@cursor/sdk`). For building a user-facing AI agent application, use `build-agents` / `eve` instead; d

catcorner22 Bundle

Explain, inspect, configure, and troubleshoot Cloud Agent development environments. Use when the user asks about environment setup, changing/improving the environment, or triggering/testing a build.

catcorner22 Skill

Wait for external events (GitHub CI results, PR activity, Slack messages, Linear issues) by subscribing with the cursor-subscriptions MCP tools instead of polling.

catcorner22 Skill

Build a retrieval pipeline with LangChain: document loaders, RecursiveCharacterTextSplitter, embeddings, and the LangChain vector store wrappers (Chroma, FAISS, Pinecone). Use when the retrieval code is LangChain's — the user names LangChain, or the code imports `langchain_community` loaders, `langchain_text_splitters`, or a `langchain_*` vector store. Scope boundary: RAG built on the Vercel AI SDK (`embed`/`embedMany`) belongs to `ai-sdk`; this skill does not claim a retrieval pipeline that is not LangChain's.

catcorner22 Skill

Vercel deployment and CI/CD expert guidance. Use when deploying, promoting, rolling back, inspecting deployments, building with --prebuilt, or configuring CI workflow files for Vercel.

catcorner22 Skill

Vercel Functions expert guidance — Serverless Functions, Edge Functions, Fluid Compute, streaming, Cron Jobs, and runtime configuration. Use when configuring, debugging, or optimizing server-side code running on Vercel.

catcorner22 Bundle

Initialize an Adobe App Builder project end-to-end without Developer Console UI clicks. Creates the Console project and workspace, subscribes APIs (including those needing a product profile), maps user intent to the right template, runs non-interactive `aio app init`, and guides post-init customization. Use whenever the user mentions creating an App Builder app, scaffolding a project, `aio app init`, setting up an Experience Cloud extension, adding actions or web assets, creating a Console project or workspace, adding APIs, or bootstrapping App Builder — even if they don't say "App Builder". Also for SPA templates, AEM extensions, API Mesh, Asset Compute workers, and MCP server projects. Also handles debugging init failures — template not found, `aio app init` hangs or times out, Node version mismatches, npm install failures, post-init build errors, `aio login` issues, `aio app run` showing nothing, or `aio console project create` / `workspace create` / `workspace api add` errors.

catcorner22 Skill

Prepare the workspace before execution: files, folders, env, tools, references, and templates in place so work never stops for a missing ingredient. Culinary mise en place applied to repos, OneDrive, and study sessions. Use before starting an assignment, coding session, lecture day, or deploy. Scope boundary: during-task loops → `ooda-lean-loop`; folder taxonomy → `onedrive-organization`; defining done → `deliverable-first`.

catcorner22 Bundle

Set up CI/CD pipelines for Adobe App Builder projects. Generates GitHub Actions workflows using adobe/aio-cli-setup-action@3 and adobe/aio-apps-action@3.3.0, plus patterns for Azure DevOps and GitLab CI. Handles OAuth S2S secrets injection, multi-workspace promotion (stage → prod), deploy gating with manifest validation. Use this skill whenever the user mentions CI/CD for App Builder, GitHub Actions for aio deploy, automated deployment pipelines, continuous integration, continuous delivery, deploy automation, multi-environment promotion, aio app add ci, or wants to automate their App Builder build and release process. Also trigger when users mention deploy workflows, release pipelines, or GitHub secrets for App Builder.

catcorner22 Skill

Pre-execution audit before AI generation: required inputs, files, constraints, tools, and prior outputs present. Halt instead of running cold. Use before agent runs, RAG pipelines, or multi-step chains. Scope boundary: human/study prep → `workspace-mise-en-place`; craft routing → `craft-systems-primer`.

catcorner22 Bundle

Test that a Cloud Agent environment will work with prebuilt environment builds and recommend any required changes. Use when the user wants to migrate to builds, test build compatibility, or follow the Builds page setup-agent flow.

catcorner22 Bundle

Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port code onto ZeroGPU, fix a Space that won't build or run, or otherwise work with `hf spaces …`, `@spaces.GPU`, Space README frontmatter, or the `spaces` Python package.

catcorner22 Skill

Four-phase AI pipeline with logged Orient step: observe raw context, orient (filter/prioritize with inspectable log), decide approach, act. Use when debugging why context was ignored. Scope boundary: human OODA → `ooda-lean-loop`.

catcorner22 Bundle

Create, implement, deploy, and debug Adobe Runtime actions with consistent layout, validation, and error handling. Use this skill whenever the user needs to add actions to an App Builder project, understand action structure (params, response format, web/raw actions), configure actions in the manifest, use App Builder SDKs (State, Files, Events, database), deploy and invoke actions via CLI, debug action issues, or implement patterns such as webhook receivers, custom event providers, journaling consumers, large payload redirects, action sequence pipelines, and Asset Compute workers. Also trigger when users mention serverless functions in Adobe context, action logging, IMS authentication for actions, or cron-style scheduled actions.

catcorner22 Skill

Create walkthrough artifacts (screenshots and screen recordings) that prove code changes work. Use when finishing tested changes and uploading demo evidence for the user.

catcorner22 Bundle

Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

catcorner22 Bundle

Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or other AWS automation — including when about to run `pip install` or invoke `boto3` for AWS work, when creating or activating a virtualenv for it, or when the user asks to "set up the environment" for a SageMaker/AWS task. Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs. Scope boundary — AWS/SageMaker work ONLY. Do not claim general Python work: Hugging Face Jobs, TRL, evaluation and vision-training environments are managed by `huggingface-llm-trainer`, `trl-training`, `huggingface-community-evals`, and `huggingface-vision-trainer`, which use their own uv / PEP-723 inline-dependency conventions.

catcorner22 Skill

On pipeline failure, drill five whys to root cause and log permanent system fixes — not prompt whack-a-mole. Use after hallucinations, format errors, or constraint misses. Scope boundary: human kaizen line → `ooda-lean-loop`; routing audit → `skill-library-audit`.

catcorner22 Bundle

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

catcorner22 Skill

Router for the AI-transfer catalog (45 techniques, #1–50 with five merges) — gates, scaffolds, and pipeline stages. Use when hardening agents, RAG, multi-step chains, or when the user mentions transferable skills, discipline patterns, or AI quality plugins. Scope boundary: domain apps (college, M365) → those primers; human craft loops → `craft-systems-primer`.

catcorner22 Bundle

Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.

catcorner22 Bundle

Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call `iam:CreateRole`, or when an AccessDenied error mentions an IAM action. Never blindly call `iam:CreateRole` — always check for existing roles first. This skill prevents the most common SageMaker deployment failure: trying to create IAM resources from an SSO principal that has no IAM write permissions.

catcorner22 Bundle

Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.

catcorner22 Skill

Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

catcorner22 Bundle

Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls `create_endpoint`, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deploy_ic.py for real-time endpoints that scale to zero instances via inference components, and deploy_async.py for async endpoints (also scale-to-zero). This is the last step in the SageMaker deployment workflow. Never generate a bare `create_endpoint` call without these defaults — endpoints without autoscaling or alarms are demos, not deployments.

tractorjuice Skill

[COMMUNITY] Assess sovereign cloud residency under the UAE National Cloud Security Policy v2. Validates per-classification residency, names approved CSP options (Core42 / G42 sovereign / Microsoft UAE North + Central, TDRA FedNet, e& Sovereign Launchpad on AWS), and captures shared-responsibility matrix and exit/portability plan.

tractorjuice Skill

[COMMUNITY] Assess EU Cloud Sovereignty Framework (v1.2.1) posture for cloud procurement — score the eight sovereignty objectives and record SEAL-level evidence

tractorjuice Skill

[COMMUNITY] Generate a Critical Third Parties (CTP) dependency assessment — register of designated CTPs the firm relies on (cloud hyperscalers, payment networks, BaaS providers), materiality assessment per provider, resilience testing plan including exit and substitution drills (BoE/PRA/FCA PS24/16).

jacob-balslev Bundle

Use when reviewing code for security vulnerabilities, threat-modelling a new feature, implementing authentication or authorization, handling user input, hardening dependencies or CI/CD against software-supply-chain compromise, or auditing a codebase against the current OWASP Top 10 (2025, with the 2021 mapping retained). Covers broken access control (incl. SSRF), security misconfiguration, software supply chain failures, cryptographic failures, injection (SQL, NoSQL, command, LDAP, XSS), insecure design, authentication failures, software/data integrity failures, security logging and alerting failures, and mishandling of exceptional conditions (fail-open error paths, error leakage). Do NOT use for general code review (use `code-review` for the holistic per-PR pass), for chasing a known production bug (use `debugging`), for defending an LLM against prompt/RAG injection or agent-tool-authority abuse (use `prompt-injection-defense`), or for writing a security policy doc (use `documentation`).

jacob-balslev Bundle

Use when reasoning about how a database schema changes over time without breaking deployed application code — the multi-release path from the current schema to a target schema: the expand/contract pattern (Ambler & Sadalage / Fowler's parallel change), the zero-downtime change rules, the backwards-and-forwards compatibility envelope (deploy ordering and rollback discipline), the catalog of schema changes (add column, drop column, rename, type change, add constraint, add index) and the safe procedure for each, the dual-write and dual-read transitions (with named source-of-truth and production consistency-check discipline) that make non-trivial changes safe, the lock-acquisition hazard (why even a metadata-only DDL needs a bounded lock-acquisition budget — Postgres lock_timeout + retry, or the engine equivalent — because lock acquisition is a FIFO queue), the cross-engine online-change mechanisms (Postgres CONCURRENTLY / NOT VALID, MySQL INSTANT/INPLACE/COPY Online DDL, gh-ost / pt-osc ghost-table cutover, Vite

jacob-balslev Bundle

Use when declaring, measuring, or enforcing performance thresholds as a quality contract rather than as an aspirational target. Covers the three budget axes (time, size, count), the four governing properties of a real budget (metric, threshold, percentile, consequence), the Core Web Vitals set (LCP, INP, CLS), the RAIL model, Lighthouse budgets.json, lab vs field measurement, and the discipline of treating budget breach as a build or deploy failure rather than a tracked metric. Do NOT use for the activity of profiling and optimizing a specific slow path (use performance-engineering), the choice of rendering model that bounds achievable budgets (use rendering-models), or the design of observability and telemetry signals (use observability-modeling).

nahisaho Skill

科学データ解析パイプラインの基盤スキル。ディレクトリ構造の自動構築、再現性のためのシード管理、 進捗ログ出力、実行時間計測、JSON サマリー生成、ダッシュボード総括図の作成を行う際に使用。 全 13 実験に共通する足場パターンを統合。

nahisaho Skill

ユーザーのプロンプト（研究テーマ・データ記述）から仮説を立案し、 検証用の解析パイプラインを自動生成するスキル。PICO/PECO フレームワークによる 仮説構造化、適切な統計検定の選択、パイプラインコード生成を行う。 「仮説を立てて」「このデータで何がわかる？」「解析パイプラインを作って」で発火。

codyswanngt Bundle

Install and validate Lisa's…

codyswanngt Bundle

Provision the multi-account AWS…

codyswanngt Bundle

building, running locally, or…