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Found 6,201 Skills
PRD-driven automated test orchestration skill. It is used to standardize the implementation of test plans, AI self-testing and self-repair, and test reports for each PRD version; applicable to scenarios such as splitting Unit/Integration/E2E according to acceptance criteria, defining boundaries between automation and manual testing, and generating versioned TEST_PLAN/TEST_REPORT.
Goal-driven E2E testing where a browser agent (Playwright MCP / computer-use) reads a natural-language goal and explores the app via the accessibility tree to assert outcomes — no pre-written script. Covers when intent-driven beats scripted, making agent runs deterministic (pinned model, temperature 0, seeded data, bounded steps, explicit success assertion, snapshot-not-pixel), cost/latency control, the accessibility-tree-first interaction model, CI gating, and graduating a stable run into a scripted Playwright test. Use when: "agentic browser test," "goal-driven browser test," "let an agent explore the app," "natural-language E2E," "browser agent smoke test," "Playwright MCP test." Not for: Writing/maintaining deterministic scripted Playwright tests — that is playwright-automation. Testing your product's OWN LLM features — that is ai-system-testing. Related: playwright-automation, ai-system-testing, exploratory-testing, test-reliability, qa-project-context.
Iterate on a local Decentraland SDK7 scene against a running Explorer build through its MCP automation server. Use when the user asks to see, test, verify, walk through, screenshot, or debug a scene in-world, when they name the Explorer or its MCP server, or whenever an `mcp__explorer__*` tool is available in the session.
Use this skill when an agent needs to write reusable code, store it under the configured Hermes Code folder, run it directly through local code execution, or host it behind a Make-managed E2B Code Shell scenario. Use for tool-building, generated business automation scripts, and nested flows where hosted code calls Make API shell scenarios for SaaS access without receiving raw OAuth, Make, or E2B secrets.
Configure cmux through ~/.config/cmux/cmux.json: settings (appearance, sidebar, notifications, automation, browser, shortcuts, set/get/validate by JSON path), customization (tab bar buttons, plus-button click and right-click menus, custom actions/commands/menus, right sidebar), and sidebar workspace groups (anchor workspaces, group CLI/socket ops, per-cwd group config). Triggers: 'cmux config', 'cmux.json', 'change cmux setting', 'set <x> in cmux', 'rebind a cmux shortcut', 'cmux-customize', 'customize cmux', 'tab bar button', 'add cmux action', 'workspace group', 'group sidebar', 'anchor workspace', 'workspaceGroups'.
Guidance for detection engineering in Microsoft Sentinel — building, testing, deploying, and maintaining analytics rules, hunting queries, and SOAR automation. Covers the Content Hub solution model, MITRE ATT&CK mapping, scheduled vs near-real-time (NRT) vs Fusion vs anomalies analytics, KQL detection patterns (joins, summarize, bin, materialize), entity mapping and incident enrichment, custom detections from Defender XDR vs Sentinel-only, automation rules, playbooks (Logic Apps), watchlists, threat intel matching, content as code with Azure DevOps / GitHub repositories integration, and detection lifecycle (validate → tune → version). WHEN: Sentinel analytics rule, KQL detection, MITRE mapping, Sentinel content hub, scheduled analytics, NRT rule, hunting query, Sentinel automation rule, Logic App playbook, custom detection, repositories Sentinel CI/CD, detection-as-code, watchlist, threat intel matching analytics, fusion alerts, anomalies, incident enrichment, entity mapping. DO NOT USE for Sentinel architecture/onboarding (use sentinel), Defender XDR custom detections only (overlap—use the side that owns the data), or generic KQL training.
Lark Slides: Read and manage PPT pages in XML format. Prioritize using `+create` when creating presentations; the XML API is mainly used to read full PPT content, create and delete slide pages. It applies to scenarios where users need to create PPTs, read PPT content, and manage slide pages.
Lark Attendance: Query your own attendance clock-in records
Run any model on RunComfy from the command line. The `runcomfy` CLI is one binary, one auth, hundreds of model endpoints — image generation, image edit, video generation, image-to-video, lip-sync, face swap, video edit, inpainting, outpainting, extend, ControlNet, relight, upscale, LoRA training and more. Submit a request, poll for status, download the output. This skill teaches the agent how to install, authenticate, discover model schemas, invoke models, stream / poll / no-wait, script in JSON output mode, and handle errors. Triggers on "runcomfy cli", "install runcomfy", "runcomfy login", "runcomfy run", "runcomfy whoami", "runcomfy api", or any explicit ask to call a RunComfy model from a script or terminal. Sibling skills (ai-image-generation, ai-video-generation, image-edit, video-edit, face-swap, lipsync, image-to-video, image-inpainting, image-outpainting, video-extend, controlnet-pose, relight) all dispatch through this CLI.
Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.
Use when the user wants to design a mobile app, create screens, build UI, or interact with their Sleek projects — whether high-level ("design an app that does X") or specific ("list my projects", "create a new project", "screenshot that screen").
Rigor Train skill for deep learning research repositories. Use when a documented or selected training command should be run conservatively for startup verification, short-run verification, full kickoff, or resume, with command, config, seed, log, checkpoint, status, and metric evidence written to standardized `train_outputs/`. Do not use for environment setup, exploratory sweeps, speculative idea implementation, or end-to-end orchestration.