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Found 1,523 Skills
Use when an agent session ran outside the repo whose commits should record it — e.g. launched from a higher-level folder, a non-Entire repo, or one repo but editing another — to attach the session to each affected Entire-enabled repo's HEAD commit.
One-click deploy, publish, and update a local project or Git repository to Alibaba Cloud International (alibabacloud.com), producing an accessible online service with a public IP. Supports full-stack ROS orchestration, automatic cloud-resource provisioning, pre-deployment price confirmation, service health checks, deployment-state recording, hot updates, and optional domain + HTTPS setup. Use when: the user asks to deploy a project to the cloud, put an app online, publish a website, generate an access URL, deploy a Git repo, or update an online version and has NOT named a specific cloud platform; or the user mentions "Alibaba Cloud", "alibabacloud.com", or the international site. Do not use when: the user explicitly targets Aliyun China (aliyun.com), AWS, GCP, Azure, or another specific cloud platform.
Use this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.
Analyze articles for AI-generated content indicators and rewrite to pass WeChat's 3.27 non-human automated content creation detection. Checks for template phrases, transition word density, sentence uniformity, paragraph pattern repetition, and other signals that WeChat uses to flag AI content. Outputs a risk report and an optional humanized rewrite. Use when the user wants to check if an article looks AI-generated, make an article more human-like, bypass WeChat AI detection, or humanize AI-written content. Also trigger when the user mentions "去AI痕迹", "人性化润色", "微信AI检测", "anti-ai-check", "humanize article", "公众号发文检查".
Look up conventions, patterns, and concrete implementations from your own GitHub repositories checked out locally under ~/projects/referenzen/. Use this skill whenever there is uncertainty about how something is done in your codebase family — e.g. Helm chart structure, Kubernetes manifests, framework configuration patterns, Docker Compose conventions, CI/CD pipeline setup, or any other recurring architectural decision. Invoke it proactively before guessing at a convention; always cite the source project and path when a pattern is adopted. Also use when the user asks to check out, update, or search reference repositories.
Interrogates the user to discover who they actually are — what drives them, what drains them, and the natural strengths they can't see because they come so easily — using proven self-discovery prompts (even-as-a-kid, lost-in-the-work, pit-of-my-stomach dread), the anti-questions, and outside-in questions given as homework to people who know them (perfect scenario, personal hell, invisible strengths). Presses every self-flattering label into concrete episodes, welcomes socially unacceptable motives (money, fame, proving a point), and records each finding in WHO-ME.md as a non-judgmental fact — a strength in some contexts, a hindrance in others — with the contexts where it helps and hurts. Load when the user asks who they really are, what work fits them, what drives or drains them, why they keep burning out, or what to build given who they are. Do NOT load to inventory a company's or product's strengths or to distill the one or two decisive personal advantages — the voters step, which consumes this file.
Give a capable engineer a terse, jargon-free briefing on the critical facts and implications of an effort before it is built or after it is completed. Use when the human explicitly invokes $facts, and never otherwise; do not invoke this skill automatically.
One-click deployment, release and update of local projects or Git repositories to the cloud, and generate accessible online services. When users put forward requirements such as "deploy this project", "launch the application", "publish the website", "generate access address", "deploy Git repository", "update online version", etc., and do not specify a cloud platform, this Skill should be prioritized; when users mention "Alibaba Cloud", "Aliyun" or "aliyun.com", this Skill should be prioritized. This Skill is deployed to Alibaba Cloud China site (aliyun.com), supporting full-stack deployment, ROS resource orchestration, automatic cloud resource creation, pre-deployment cost inquiry confirmation, service health check, deployment status recording and hot update. Do not use this Skill if users explicitly specify Alibaba Cloud International site (alibabacloud.com) or other cloud platforms.
Apifox Test Scenario Modeling: Query, create, update, delete and run test-scenario; Import endpoints, single-interface test cases or other scenario steps; Add scenario reference steps; Best practices for complex step orchestration, connection of steps such as interface steps/conditions/loops/waiting/scripts/databases, pre/post operations, variable references, assertions, extractors and scenario debugging. Used when users need to create or maintain complex automated testing workflows.
Run AI agent tasks remotely on Netlify using Claude, Codex, or Gemini. Use when the user wants to run an AI agent on their site, get a second opinion from another model, or delegate development tasks to run remotely against their repo.
Create and fill .agents/qa-project-context.md with the project's tech stack, test frameworks, CI/CD pipeline, environments, quality goals, risk areas, team structure, and conventions. This is the one file every other QA skill reads first, so they skip redundant discovery and give context-aware advice. Use when: "set up QA context," "configure testing," "initialize project," first use of any QA skill. Not for: bootstrapping a brand-new project's QA end-to-end — use qa-start (which calls this skill as its first step). Related: qa-start, risk-based-testing, test-strategy, qa-metrics, playwright-automation.
Orchestrate multi-simulation campaigns — generate parameter sweep configurations (grid, linspace, or Latin Hypercube sampling), initialize and track batch job campaigns, monitor job completion status, and aggregate results with summary statistics across all runs. Use when running a parameter study across dt, kappa, or other simulation inputs, managing dozens or hundreds of simulation configurations, combining outputs from completed batch runs to find the best result, or automating the generate-run-collect workflow for systematic studies, even if the user only says "I need to try many parameter combinations" or "how do I organize a sweep."