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Found 2,443 Skills
Implements and debugs browser Web Neural Network API integrations in JavaScript or TypeScript web apps. Use when adding navigator.ml checks, MLContext creation, MLGraphBuilder flows, device selection, tensor dispatch and readback, or explicit fallback paths to ONNX Runtime Web or other local runtimes. Don't use for model training, server-side ML inference, or cloud AI APIs.
Explore any Hexagone Web space via Playwright headless browser, capture screenshots, and produce a PO-oriented Markdown document.
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for live container runtime analysis, mounted secrets, sidecars, namespaces, init containers, entrypoint drift, and route-to-container resolution. Use when the user asks why a live container differs from manifests, where a mounted secret is consumed, how a sidecar or init container changes runtime state, or which route resolves to which live container. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Kubernetes API analysis, service-account trust, RBAC edges, admission and controller behavior, cluster secrets, workload mutation, and namespace-scoped drift. Use when the user asks to inspect kube API permissions, service-account tokens, RoleBinding or ClusterRoleBinding edges, admission webhooks, controller-created pods, secret exposure, or why live workloads differ from manifests. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
A skill that uses GLM-V native grounding capabilities for coordinate conversion, bounding-box visualization, and more. GLM-V native grounding can locate any target specified by the prompt in an image and output relative coordinates normalized to 0-1000 based on image size. Coordinate formats include 2D bounding box (default), 2D points, and 3D bounding box. GLM-V also supports spatiotemporal localization and tracking of multiple prompt-specified targets in videos, outputting 2D bounding boxes per second.
Fetch any URL as clean markdown. ALWAYS use this skill instead of the WebFetch tool when you need to read a URL's content — it has a 5-layer fallback (Jina Reader, defuddle.md, markdown.new, OpenCLI, raw HTML) that produces better results and handles JS-rendered pages (Twitter/X, SPAs), login-required platforms (zhihu, reddit, weibo, xiaohongshu), and complex web pages that WebFetch cannot parse. Invoke whenever the user provides a URL and wants to read, extract, summarize, analyze, or convert its content to markdown. Keywords: 'fetch page', 'read URL', 'grab content from', 'summarize article', 'extract text from webpage', '抓取网页', '读链接', '网页转 markdown'. NOT for: web search without URL, file downloads, screenshots, form filling, or accessibility checks.
Surgically updates a specific section of .marrow.md without re-running the full extraction. Accepts a new value, color, image, or description and patches only the relevant section — leaving everything else intact. Use this skill when the user wants to update one part of the extracted soul, change the brand accent color, update spacing rules, replace a typeface, refine the brand personality, or extract color from a new reference image. Triggers on: /marrow-update, or prompts like "update the accent color to X", "change the brand color", "update spacing in marrow", "the font changed to X", "update marrow with this new color", "patch marrow", "update just the accent", "marrow-update accent #FF6B6B", "update the palette from this image". Requires .marrow.md to exist. If not found, instructs user to run /marrow first.
Designs and orchestrates a realistic interview simulation platform with voice AI, whiteboard evaluation, gaze-tracking proctoring, and mobile spaced repetition. Use for building mock interview infrastructure, configuring sessions, and adaptive difficulty. Activate on "interview simulator", "mock interview", "practice session", "voice mock". NOT for individual round-type coaching, resume writing, or prep timeline coordination.
Complete skill for the Analyzify Shopify analytics and tracking app. Covers all Analyzify MCP features and workflows. Trigger when the user wants to: check store info, view workspace details, query Google Analytics 4 data, run GA4 reports, check GA4 traffic, query Google Search Console data, view search performance, top queries, query Google Ads campaigns and performance, view ad spend and ROAS, access Shopify store data via Admin API, list products, collections, orders, query historical analytics reports, campaign attribution, traffic trends, check connected accounts, view API key capabilities, or any Analyzify-related task. Covers questions like: "what is my store", "what is my space ID", "show my GA4 traffic", "top search queries this week", "how are my Google Ads performing", "list my products", "show campaign attribution", "compare organic vs paid traffic", "what accounts are connected", "what plan am I on", "show my store dashboard", "daily sessions this month". All operations use MCP tools: execute_graphql, execute_report_graphql, introspect_schema, introspect_report_schema.
Claude Code skill that designs and builds high-converting questionnaire-style app onboarding flows modelled on proven conversion patterns from top subscription apps like Noom, Headspace, and Duolingo.
Install, start, stop, inspect, and configure duoduo host-mode channels, especially Feishu and compatible npm or tarball channel plugins. Use when the user asks to run duoduo channel install/list/start/stop/status/logs, set Feishu credentials, package or install a WeChat channel plugin, configure stdio or Feishu prompts, adjust channel workspaces or streaming, or edit channel defaults in kind descriptors or instance descriptors. Also trigger for Chinese requests such as 帮我拉起 feishu 通道, 帮我拉起微信 channel, 配置 channel 提示词, 改 stdio 的 workspace, or 查看 channel 状态.
Professional-level refinement and verification for Chinese SRT subtitles for launch. Used to clean ASR-based raw subtitles into a publishable version, only performing subtitle-level cleaning and correction without formal rewriting, summarization, or expansion; meanwhile, strictly maintaining synchronization with the original audio, splitting entries only within the original subtitle time range when necessary, outputting a complete clean SRT, and then using the accompanying verification script for final rule checks and timeline review. Suitable for tasks such as documentaries, interviews, oral broadcasts, screen recordings that require correcting recognition errors, deleting meaningless filler words, adding pause spaces, limiting single-entry word count, and avoiding accidental deletion of meaningful subtitles.