「artifacts」の検索結果

Per-screen design spec generator for AI design tools (Stitch / Figma AI / Codex artifacts). Consumes per-feature PRDs from kta-prd-pipeline. Emits tool-agnostic markdown specs. Triggers — design spec, AI design prompt, screen mockup brief, Figma AI input, Stitch prompt, design system tokens, post-PRD design phase, generate design specs, design brief, mockup spec. DOES NOT generate Compose code, Kotlin code, or @Preview stubs.

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Create Kotlin Compose design tokens from one of three supported sources — Google Stitch (MCP), Figma (MCP or JSON), or Claude Design (kta-design-spec output, Claude HTML/markdown artifacts). Three-layer architecture (Primitive/Semantic/Component) with code generation. Use when user asks to "generate design tokens", "create theme from Figma/Stitch/Claude design", "build Compose tokens from design spec", or "convert design source to tokens". Refuses any other input source and asks user for correct seed data.

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Triage failed Agent QA runs with MCP evidence, artifacts, logs, fixed failure categories, confidence, and actionable next steps.

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Debug, patch, and verify failed Agent QA runs from MCP evidence, artifacts, logs, and local code without hiding product or infrastructure defects.

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Build and run the owner's self-branding system in a structured way across LinkedIn, Substack, dev.to, X, profile rewrites, recurring series, and comment strategy. Use when the owner asks for positioning, a branding plan, profile updates, content drafts, a publishing sequence, comment ideas, or help maintaining a consistent public voice. Optimize for operator credibility: specific experience, defensible numbers, concrete artifacts, and category ownership.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Dbt Cloud — dbt Cloud management — monitor projects, environments, job runs, model status, and data freshness. Use when reviewing job run health, debugging failed models, inspecting run artifacts, or auditing project configuration.

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Use when working with Bentoml — bentoML model serving and packaging management. Covers service management, model packaging, deployment status, API testing, Bento building, and runner configuration. Use when packaging ML models for serving, deploying BentoML services, debugging inference issues, or managing model artifacts.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Azure Devops — azure DevOps comprehensive management. Covers pipelines, boards, repositories, artifacts, test plans, work item tracking, and release management. Use when checking pipeline status, managing work items, investigating build failures, or auditing Azure DevOps project configurations.

日本語の概要は準備中です。原文の説明を表示しています。

Design one-off HTML artifacts (landing, deck, prototype).

日本語の概要は準備中です。原文の説明を表示しています。

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory, coverage-bounded integrity checks, two-stage peer review, and auditable quality-assurance artifacts. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로.

日本語の概要は準備中です。原文の説明を表示しています。

Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.

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Guide developers through capturing diagnostic artifacts to diagnose production .NET performance issues. Use when the user needs help choosing diagnostic tools, collecting performance data, or understanding tool trade-offs across different environments (Windows/Linux, .NET Framework/modern .NET, container/non-container).

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Establish build performance baselines and apply systematic optimization techniques. Only activate in MSBuild/.NET build context. USE FOR: diagnosing slow builds, establishing before/after measurements (cold, warm, no-op scenarios), applying optimization strategies like MSBuild Server, static graph builds, artifacts output, and dependency graph trimming. Start here before diving into build-perf-diagnostics, incremental-build, or build-parallelism. DO NOT USE FOR: non-MSBuild build systems, detailed bottleneck analysis (use build-perf-diagnostics after baselining).

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Develops an implementation plan ready for wave-orchestration to execute. Takes the user's goal or rough plan as input, mines the codebase for facts, grills the user one question at a time for the decisions, drafts the planning/ artifacts (task DAG, wave table, worker prompt template, state file), then reviews its own plan on two axes before seeking approval. Use when the user wants to turn a feature idea, master plan, or backlog into a wave-orchestration execution plan.

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Use when the user just says "plan", "planen" or "roadmap": offer a template via aos-plan-canvas templates. Open plans and HTML artifacts in a local browser canvas where the human annotates elements, chats, and approves or requests changes without leaving the page. Use for planning, whenever a user says "plan", "roadmap", "visualize the plan", "review the plan" or /plan-canvas, right after a brainstorm or grill interview has produced a plan, and whenever any skill's artifact (plan, design, comparison, report, diagram) can be reviewed visually instead of with typed feedback.

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Linear multi-agent build pipeline with file hand-offs in production_artifacts/, run after /bdbrainstorm or /grill-me. This is the linear variant — no state.json, no repair loop, no dispatcher graph — distinct from startcycle-graph, which adds durable state and automated escalation. Use when an approved plan should be built, when the user types /startcycle or "run a startcycle", or when you pick a startcycle variant from the situation yourself (no user prompt needed).

日本語の概要は準備中です。原文の説明を表示しています。

Use when running the autonomous multi-agent build pipeline after /bdbrainstorm or /grill-me, when the user types /startcycle-graph or "run a startcycle", or when a task needs durable state, a review repair loop and an automated quality gate. Routes to the startcycle-dispatch Dynamic Workflow, which reads production_artifacts/state.json and invokes Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, and Shipping in turn per .agents/graph.md's edge table — the agents never invoke each other.

日本語の概要は準備中です。原文の説明を表示しています。

Benchmark Xcode clean and incremental builds with repeatable inputs, timing summaries, and timestamped `.build-benchmark/` artifacts. Use when a developer wants a baseline, wants to compare before and after changes, asks to measure build performance, mentions build times, build duration, how long builds take, or wants to know if builds got faster or slower.

日本語の概要は準備中です。原文の説明を表示しています。

Create distinctive, production-grade frontend interfaces with high design quality. Generates creative, polished code that avoids generic AI aesthetics. Use when the user asks to build web components, pages, artifacts, posters, or applications, or when any design skill requires project context.

日本語の概要は準備中です。原文の説明を表示しています。

self-review step of mspec workflow — sanity-check all artifacts via subagent

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Use when: 'intake this repo/skill', 'evaluate external skill', 'should we adopt X', a candidate repo URL plus an adoption question, or any external skill/workflow/tool surfaces as a possible fit for Agistra. Structured front-door evaluation of external skills, workflows, and similar capability artifacts before any install, promotion, or adaptation decision. Ends in exactly one classification: Install candidate, Adapt concept, or Reject. Never install first — intake first.

日本語の概要は準備中です。原文の説明を表示しています。

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

日本語の概要は準備中です。原文の説明を表示しています。

Azure Container Registry SDK for Python. Use for managing container images, artifacts, and repositories.

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Propose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.

日本語の概要は準備中です。原文の説明を表示しています。