Fetch content from Reddit via its JSON API using a browser session (DuckDuckGo-hop unlock). Use when accessing Reddit URLs, researching topics on Reddit, or when Reddit returns 403/blocked errors.

Fetch and summarize Hacker News / hckrnews.com top stories, articles, and their comment threads. Use when asked to summarize HN front-page stories, a specific HN story plus its discussion, or "the top N from hckrnews".

Run the PinchTab stealth-score sweep against 15 bot-detection / fingerprint sites (sannysoft, rebrowser, deviceandbrowserinfo, iphey, whoer, browserscan, pixelscan, fingerprint-scan, incolumitas, fvision, amiunique, browserleaks, creepjs, coveryourtracks, fingerprint-demo). Starts a Docker PinchTab container per browser (chrome / cloak / both), spawns a blind agent that drives PinchTab through plain-English per-site playbooks, captures structured metrics, prints a side-by-side comparison highlighting divergent metrics, and appends to history.jsonl for cross-session tracking. Use when asked to 'run stealth score', 'compare cloak vs chrome detection', 'measure stealth', or '/pinchtab-stealth-score'.

UnityBridge workflow for AI operations in Unity Editor. Use when checking UnityBridge connectivity, Unity compile or PlayMode state, running deferred UnityBridge commands, operating assets/scenes/GameObjects/Inspector/Prefabs/GameView/screenshots, running Editor tests, or troubleshooting UnityBridge responses.

ET async and EntityRef safety workflow. Use when adding, modifying, or reviewing async/await, ETTask, generic ETTask results, ETCancellationToken, NewContext, concurrent waits, handler Run async safety, or any Entity access after await in WOW/ET code or tests.

Build, test, and maintain the Uno DevServer (CLI tool, RemoteControl Host, MCP proxy, add-in discovery). Use when working under src/Uno.UI.DevServer.Cli, src/Uno.UI.RemoteControl.Host, or src/Uno.UI.RemoteControl.DevServer.Tests, or on DevServer CLI commands, the MCP proxy, IDE channel, or add-in resolution.

Use when launching cloud VMs, Kubernetes pods, or Slurm jobs for GPU/TPU/CPU workloads, training or fine-tuning models on cloud GPUs, deploying inference servers (vllm, TGI, etc.) with autoscaling, writing or debugging SkyPilot task YAML files, using spot/preemptible instances for cost savings, comparing GPU prices across clouds, managing compute across 25+ clouds, Kubernetes, Slurm, and on-prem clusters with failover between them, troubleshooting resource availability or SkyPilot errors, or optimizing cost and GPU availability.

Use when generating target-language dubbing with KrillinAI CLI from SRT subtitles, including TTS audio creation and optional dubbed video generation.

Use when rendering portrait videos with KrillinAI CLI, including converting source video to vertical format, adding short bilingual subtitles, rendering dubbed vertical videos, and checking vertical subtitle readability.

Build, audit, and improve harnesses that make AI coding agents reliable: AGENTS.md/CLAUDE.md instruction files, feature/state tracking, verification gates, scope boundaries, session handoff, memory persistence, context budgets, tool-permission safety, and multi-agent coordination. Use this whenever a coding agent is unreliable across sessions — forgets context, drifts out of scope, claims "done" before tests pass, or starts each session inconsistently — or when creating or assessing AGENTS.md, CLAUDE.md, feature_list.json, init.sh, progress.md, or session-handoff files. Reach for it even if the user never says the word "harness."

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.

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.

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.

Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. Use this skill before any other AWS work — deploying to SageMaker, creating resources, calling AWS APIs, or anything that touches an AWS account. Use it especially when the user has not specified a region or profile explicitly, when they say things like "use my AWS account", "deploy to AWS", "use my profile", or when about to make any AWS CLI or SDK call. Never guess the region or account ID — always use this skill to read it from the local configuration first.

Build and customize EmDash CMS sites on Astro. Use when creating pages, defining collections, writing seed files, querying content, rendering Portable Text, setting up menus/taxonomies/widgets, configuring deployment, or any task involving an EmDash-powered Astro site. Assumes basic Astro knowledge but provides all EmDash-specific patterns.

Adversarial code review that assumes bugs exist and hunts for them. Use when asked to review code, find bugs, audit for correctness, stress-test a PR, or when someone says "tear this apart" or "what's wrong with this". Give no benefit of the doubt — every line is guilty until proven innocent.

Write correct code with the `compromise` JavaScript NLP library (the npm package `compromise`, imported as `nlp`). Use this whenever you are writing or editing JS/TS that imports compromise, calls `nlp(...)`, or chains methods like `.match()`, `.tag()`, `.people()`, `.verbs()`, `.nouns()`, `.numbers()`, `.normalize()`, or `.replace()` — and also whenever doing rule-based natural-language tasks in JavaScript (extracting entities/dates/numbers, matching text patterns, changing verb tense, pluralizing, redacting/anonymizing text, building a simple chatbot intent matcher) where compromise is or could be the tool. compromise's match-syntax and tagset are non-obvious and easy to get wrong from memory; consult this skill before guessing.

Write, rewrite, and polish academic papers (ML/CV/NLP style). Use when the user drafts or revises Abstract, Introduction, Related Work, Method, Experiments, or Conclusion; asks "does this flow / 这段通顺吗 / polish this paragraph"; turns bullet points or a Chinese draft into publication-quality English; runs a pre-submission self-review or reviewer-style critique; fixes paper figures/tables/LaTeX formatting; or compiles/converts the paper to PDF (LaTeX build, 编译PDF, 转成PDF). Trigger on mentions of paper, draft, camera-ready, rebuttal-facing revision, CVPR/ICCV/NeurIPS/ICLR/ACL-style venues, or .tex files being edited for a paper.

Produces a structured design specification (DESIGN.md + structural layout + Decision Trace) before any visual artifact is built — the "blueprint" phase that keeps AI-generated design from feeling templated. Use this skill whenever the user asks to design, plan, mock up, or restructure any visual output — PPT / slides / decks, landing pages, dashboards, posters, charts, infographics, marketing pages, UI components, prototypes, illustrations — even when they only say "make a slide about X" or "help me put together a page for Y". Also trigger on requests to critique or improve an existing design when the user wants a principled, spec-driven pass rather than just cosmetic tweaks. Do NOT trigger when the user has already handed you a completed DESIGN.md and only wants code implementation (defer to frontend-design or implement directly).

Run, configure, and troubleshoot OpenAI Codex CLI in non-interactive headless environments. Use for Codex automation in Bash or PowerShell, native Windows or WSL2, shell scripts, CI/CD, Docker, Kubernetes, remote servers, agent harnesses, or batch jobs; for constructing `codex exec` commands; selecting sandbox and approval modes; consuming JSONL events or structured output; resuming sessions; passing prompts through stdin; and handling failures caused by unavailable interactive input such as `request_user_input`.