Explorar skills

Use when creating new skills, editing existing skills, or verifying skills work before deployment

Generate a comprehensive, impact-framed brag-doc entry for a chosen time window (day, week, biweek, month, or custom). Pulls authored PRs, reviews given, commits, deploys, and linked tickets across GitHub, local git, and configured MCPs, then synthesises a themed narrative — leading with outcomes, quantifying impact, owning the work in first-person past tense, separating IC contributions from ampl

Configure a CDN with optimized caching, SSL/TLS, security headers, and cache invalidation — auto-detects hosting provider and app type, generates CloudFront, Cloudflare, or Vercel config with per-content-type cache rules, edge functions, and compression. Use when deploying a static site, SPA, SSR app, or adding a CDN layer to an existing API.

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

Generate a two-part standup report: Yesterday (everything code-related you shipped, reviewed, or addressed since your last working day — PRs merged, reviews left and answered, comments addressed, deploys that fired, tickets closed) and Today (carry-over threads plus priority-ordered queue items, framed as "likely working today"). Pulls from GitHub, local git, release automation, and any installed

Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

Create and review bash scripts specifically for NinjaOne or Action1 RMM deployment to Linux servers. ONLY use when the user explicitly mentions RMM, NinjaOne, Action1, or background agent deployment targeting Linux. Do NOT use for general shell scripting.

Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.

Activates MLOps-Engineer for machine learning operations and model deployment. Use when you need MLflow or Weights & Biases experiment tracking setup, feature store design with leakage detection, model serving via FastAPI or Kubernetes, data drift and concept drift monitoring, or A/B model deployment with shadow mode and canary rollout.

Generates production-ready Terraform files for AWS infrastructure. Writes complete .tf files for any cloud architecture — no deployment, just file generation.

Serverless GPU cloud platform for running ML workloads. Use when you need on-demand GPU access without infrastructure management, deploying ML models as APIs, or running batch jobs with automatic scaling.

Generate consumer-driven contract tests for APIs. Auto-detects framework (Express, Fastify, NestJS, Django, FastAPI, Go), selects Pact, OpenAPI validation, or schema snapshots, verifies backward compatibility, catches breaking changes before deploy, and configures CI verification. Use when you need to validate API schemas, prevent breaking changes, verify backward compatibility, or set up consumer