141건 · 관련도순
Build and deploy Flutter apps to App Store (TestFlight) and Google Play (internal/production). Auto-detects project structure, pulls signing secrets from AWS Secrets Manager, installs certificates, bumps build numbers, and runs Fastlane. Fully autonomous.
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Analyzes infrastructure costs at 1K-100K user scales by reading the actual codebase, auto-detecting cloud providers, modeling per-action costs, and projecting total monthly spend with optimization recommendations.
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Complete browser automation with Playwright. Auto-detects dev servers, writes clean test scripts to /tmp. Test pages, fill forms, take screenshots, check responsive design, validate UX, test login flows, check links, automate any browser task. Use when user wants to test websites, automate browser interactions, validate web functionality, or perform any browser-based testing.
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Cold-start brownfield reconnaissance — reconstruct the WHOLE game design from an existing codebase when there is no GDD yet. Optionally accepts a free-text game description, notes, or reference links to guide and enrich the scan. Use when the user wants to build the game design from the code, e.g. "create a GDD from this codebase", "reconstruct the design from our code — it's a roguelike deck-builder", "build a design doc from the existing project, here are some refs". Scans the project (engine auto-detected), extracts a system roster + dependency graph and content-type schemas / resources / entities into one passive .unikit/gamedesign/RECON.md, tags every fact provenance:extracted-from-code, and writes a mandatory Intent Gap for what code cannot know (pillars, fantasy, the "why"). Writes nothing else and calls no skill. STRICTLY whole-project cold-start — if a GDD already exists and the user wants a specific part investigated, use /unikit-gd-explore (code lens); to author the GDD use /unikit-gd-spec.
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Generate the architecture guidelines for a game project. Scans the project structure and tech stack, then produces .unikit/ARCHITECTURE.md describing the folder layout, dependency rules, and how modules communicate. Name a specific pattern or let it auto-detect. Use when the user wants to define or document the project architecture, e.g. "generate architecture", "describe the architecture", "which architecture should I use", "set up project architecture", or mentions module structure, module boundaries, project organization, or dependency rules.
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Autonomous comprehensive review. Once-through pipeline over /review-health, /review-arch, /review-security, /review-perf, /review-a11y, /review-test, /review-release. Operator-configurable ticket creation at startup — when ON, auto-approves sub-skill ticket proposals per the orchestrator-family contract; when OFF, uniformly declines and surfaces findings in the consolidated report. Auto-detects sub-skills that do not apply (no web content → skip /review-a11y; no tests → skip /review-test).
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Translate subtitle files (SRT/VTT) between languages. Auto-detect source language, batch translate with count validation, optional bilingual merge output. Use when user asks to "translate subtitles", "翻译字幕", "subtitle translation", or needs subtitles converted to another language.
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Burn subtitles into local video files. Supports SRT/VTT/ASS formats, auto-detect subtitles and language, bilingual subtitle merging and translation. Use when user needs to hardcode subtitles into video. Keywords: 字幕烧录、burn subtitles、本地视频、hardcode subtitles
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Capture architectural decisions made during Claude Code sessions as structured ADRs. Auto-detects decision moments, records context, alternatives considered, and rationale. Maintains an ADR log so future developers understand why the codebase is shaped the way it is.
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One-command pre-execution review pipeline. Runs scope review → architecture review → security review → design review (auto-detects which apply) before executing any workflow. Surfaces only taste decisions for user approval. Auto-fixes obvious issues. Use before executing multi-step workflows, deploying, or when user says "review this plan", "check before running", "pre-flight", or "is this ready?". Runs CEO → eng → security automatically with encoded decision principles.
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Worker that runs existing tests to catch regressions. Auto-detects framework, reports pass/fail. No status changes or task creation.
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Audits project physical structure: file hygiene, ignore file quality, framework convention compliance, domain/layer organization, naming conventions. Stack-adaptive via auto-detection.
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Smart dependency management for any language. Auto-detects project type, applies safe updates automatically, prompts for major versions, diagnoses and fixes dependency issues.
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Safely auto-detect the current machine's hardware (GPU, VRAM, system RAM, OS) with read-only commands, then recommend the top 3 local AI models to run — most powerful that fits, best for coding, and best small-and-fast — each with download links. Use when someone asks "what AI models can my computer run" or "what's the best local model for my rig".
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Ship workflow with explicit scopes: prepare stages and commits locally; release pushes and creates a PR; publish manages issues and versioning. Use when asked to "ship", "deploy", "push to main", "create a PR", or "merge and push". Proactively suggest when the user says code is ready or asks about deploying. Supports official (→ main) and beta (→ dev/develop) ship modes with auto-detection.
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Detects task agent and complexity tier at Phase 0 orient. Invoked first in every workflow. Provider adapters select any model/runtime policy. NOT for runtime agent routing inside a single skill (see orchestration-rules.md).
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Deploy applications to Vercel with auto-detection and preview URLs
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Auto-detect project context and optimize harness — deactivate unused agents/skills, suggest missing experts, generate project profile
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AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g
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First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.
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Execute git commit with conventional commit message analysis, intelligent staging, and message generation. Use when user asks to commit changes, create a git commit, or mentions "/commit". Supports: (1) Auto-detecting type and scope from changes, (2) Generating conventional commit messages from diff, (3) Interactive commit with optional type/scope/description overrides, (4) Intelligent file staging for logical grouping
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Generate a .idd.yml by auto-detecting a repo's stack, test runner, and size. Use to init, setup, or configure IDD Stack, or set up IDD. Don't use for editing an existing .idd.yml, creating issues (use /issue-creator), or plain git/npm init.
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End-to-end orchestrator: from a business idea, app idea, or existing app to running Azure deployment with cost estimates and pre-deploy approval. Analyzes your app, auto-detects the right Azure services, scaffolds infrastructure code, and deploys — tailored to your app, not a template.
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First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.