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Found 84 Skills
This skill initializes new software projects through a structured discovery, research, and documentation workflow. It should be used when the user wants to create/initialize a new project, start a new app, scaffold a new codebase, or plan a new software product. Triggered by requests like 'create a new project', 'initialize a new app', 'start a new project for X', 'I want to build X'. The skill does NOT generate code — it produces a project directory with comprehensive documentation (SPEC.md, STYLES.md, ROADMAP.md) that serves as the blueprint for implementation.
Project setup wizard for AI agents. Use when user requests setup or when .agents/CONTEXT.md is missing or incomplete and setup recovery is needed. Generates .agents/CONTEXT.md with stack, structure, coding rules, and skill mapping.
Initialize a new Adobe App Builder project end-to-end without manual template selection. Maps user intent to the correct template, runs non-interactive initialization, and guides post-init customization. Use this skill whenever the user mentions creating an App Builder app, scaffolding a project, initializing with aio, setting up an Experience Cloud extension, adding actions or web assets to an existing project, or anything related to 'aio app init', even if they don't explicitly say 'App Builder'. Also use when users mention SPA templates, AEM extensions, API Mesh setup, Asset Compute workers, or MCP server projects. Also handles debugging and troubleshooting init failures — use when users report template not found errors, aio app init hanging or timing out, Node version mismatches, npm install failures after init, build errors right after project setup, wrong directory structure from extension templates, aio login or token issues, or aio app run showing nothing.
Orchestrates complete project initialization by coordinating agent-folder-init, linter-formatter-init, husky-test-coverage, and other setup skills. Use this skill when starting a new project that needs full AI-first development infrastructure with code quality enforcement.
Initialize projects with AI Dev Flow framework using domain-aware setup
Initialize AscendC operator project and create compilable operator skeleton. Trigger scenarios: (1) User requests to create a new operator; (2) Keywords: ascendc operator, new operator, operator directory, operator initialization; (3) Need to quickly implement based on ascend-kernel template. This skill not only creates directories, but also outputs standard files and checklists for "continuous development".
Initialize Claude Code project settings with standard hooks and language-specific permissions. Use when setting up a new project for Claude Code or adding standard configuration to an existing project.
Initialize or enrich a 1C project with AI workspace skills, docs, templates. Use when user says "инициализируем проект", "init project", or asks to set up a 1C project.
Interactive project initialization with git setup, workflows, hooks, and build configuration. project setup, initialization, scaffold, bootstrap, new project.
Scaffolds new projects with README.md, AGENTS.md, and CI/CD (GitLab CI, GitHub Actions). Handles project type (generic / Flask backend / React frontend / Taro miniapp), tech stack, coding standards, quality level, and SDD (OpenSpec, SpecKit, GSD). All init flows (Flask, React, Taro) and conventions (backend-python-cicd, frontend-codegen, flask-backend-codegen, QA/testing, agent-roles/subagents) are built-in; no separate skills. Docs default to Chinese. Use when creating a project, initializing a repo, or setting up CI/CD/SDD.
Bootstraps a new AI-assisted project through a structured 4-phase conversation, then generates PROJECT.md, JOURNAL.md, .gitignore, and tmp/README.md. Also searches skills.sh and installs relevant skills for the approved tech stack. Use when starting a new project from scratch or when no PROJECT.md exists in the current directory. Do NOT trigger if PROJECT.md already exists — redirect to /project-sync instead. Invoke with /project-init — never auto-trigger.
Initialize a full ML research project control root with independent paper, code, and optional slide repositories, shared project memory, root-level agent guidance, code-owned worktree policy, and component handoffs. Use when starting a new research project, setting up a project root for agents, connecting paper/code/slides repos, or replacing a simple paper+code workspace with a lifecycle-aware research project structure.