8,630 件 ・ 関連度順
概要と使いどころ
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.
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。
Per-screen design spec generator for AI design tools (Stitch / Figma AI / Claude 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.
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。
Optimize a single Jetpack Compose @Composable function. Use when the user wants to improve, polish, animate, or refactor ONE Composable — keywords like "optimize this composable", "make this smoother", "add animation to this", "split this composable", "fix recomposition", "this composable is laggy", "polish this UI", or when user pastes/points to a single @Composable function. Analyzes the target for recomposition issues, missing animations, size/concern splits, and stability problems, then iterates with the user via AskUserQuestion to pick which optimizations to apply, then delegates implementation to the kta-compose-developer agent and summarizes changes. Input MUST be exactly one Composable (file path + function name, or function name alone).
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。
TanStack Pacer best practices for execution control in React — debouncing, throttling, rate limiting, queuing, and batching. Use when implementing search inputs, scroll handlers, API rate limits, task queues, bulk operations, or any scenario requiring controlled execution timing with reactive state.
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 deploying or managing an app that uses Prisma Composer (`@prisma/composer`): wiring its services and Modules, running it locally, testing composed services, or standing up / tearing down an environment. Triggers on "prisma composer", "@prisma/composer", "prisma app", `prisma deploy`, `prisma dev`, `compute()`, `module()`, `contract()`, `service.load()`, `mockService`, `bootstrapService`.
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。
Fetch and read transcripts from YouTube videos. Use when you need to summarize a video, answer questions about its content, or extract information from it.
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。
Write a 5-part mini spec (goal, constraints, acceptance criteria, verification, done gate) before starting any multi-step, externally-delivered, or risky task. Triggers when: a request has 3+ steps, touches external delivery, or is easy to declare done too early. NOT for: single lookups, one-line edits, routine acks, or simple factual answers.
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。
Structured build workflow: branch, delegate to coding agent, test, screenshot, and commit. Triggers on: "$devprocess", "build this properly", "use the coding agent workflow", or any feature/refactor/infrastructure change that is too large for a direct edit. NOT for: one-line fixes, doc edits, prompt tweaks, or simple config changes — do those directly.
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。
Configures autonomous heartbeat and cron-based proactive routines (e.g., morning briefing setup, unanswered-message scan cadence). Invoke when user is SETTING UP or MODIFYING a proactive routine, not when the routine itself fires. Cron crons execute their own logic — they do NOT load this skill. Triggers: "set up proactive check", "configure heartbeat", "add morning briefing", "tune cron cadence".
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 Confluence Deep — deep Confluence management covering spaces, pages, content analytics, permissions, and workspace health. Use when performing deep audits of Confluence usage, analyzing content freshness, reviewing space permissions, or assessing documentation coverage across an Atlassian Confluence instance.
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 Bmc Helix — bMC Helix ITSM platform management covering incident management, problem management, change management, and CMDB operations. Use when creating or updating incidents with categorization and assignment, managing problem investigation workflows, processing change requests through CAB approval, or querying CMDB for infrastructure configuration items and relationships.
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 Boards Deep — deep Azure Boards management covering work items, sprints, backlogs, queries, and team velocity analytics. Use when performing deep audits of Azure DevOps Boards, analyzing sprint health, reviewing backlog grooming, or assessing team capacity and velocity across Azure DevOps projects.
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 Aws Control Tower — aWS Control Tower landing zone and account management. Covers landing zone status, enrolled accounts, guardrails (controls), organizational units, baseline configurations, and drift detection. Use when auditing landing zone health, reviewing guardrail compliance, inspecting account enrollment, or troubleshooting Control Tower drift.
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 Anecdotes — anecdotes compliance automation platform for managing security programs, controls, evidence, and multi-framework compliance. Covers control monitoring, evidence automation, plugin integrations, gap analysis, and audit readiness. Use when reviewing compliance status across frameworks, tracking evidence collection, analyzing control gaps, or preparing for security compliance audits.
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 Anthropic Api — anthropic API platform management covering models, usage tracking, rate limits, and message analytics. Use when monitoring API usage and costs, analyzing model request patterns, reviewing rate limit health, or troubleshooting Anthropic Claude API issues.
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 Aws — mANDATORY parallel execution patterns (30x speedup), CloudWatch statistics syntax, Cost Explorer aggregation, output token limits, and common pitfalls.
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 AWS API Gateway — analyzing REST or HTTP APIs, debugging latency or 5xx errors, auditing throttling and stage configuration, reviewing usage plans, or checking authorization setup. Covers API inventory, CloudWatch metrics, stage audits, and integration health for both REST (v1) and HTTP (v2) APIs.
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 Prowler — prowler cloud security assessment tool. Covers AWS, Azure, and GCP security posture assessment, CIS benchmark evaluation, compliance frameworks (PCI-DSS, HIPAA, SOC2), multi-account scanning, and remediation guidance. Use when assessing cloud security posture, running compliance audits, or investigating security findings across cloud providers.
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。
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
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。
Plan, execute, and document validation, verification, and transfer of analytical procedures under the governing framework - ICH Q2(R2) and Q14, USP <1220>/<1225>/<1226>, ICH M10 bioanalytical, CLSI EP, or ISO/IEC 17025. Use for HPLC, LC-MS/MS, GC, CE, ICP-MS, dissolution, qNMR, qPCR, NIR, and ligand binding or cell-based assays whenever the question is whether a procedure is fit for its intended purpose. Triggers include "method validation", "analytical method validation", "AMV", "validation protocol", "acceptance criteria", "linearity", "reportable range", "accuracy and precision", "repeatability", "intermediate precision", "recovery", "LOD", "LOQ", "detection limit", "quantitation limit", "specificity", "robustness", "method transfer", "method comparison", "Deming", "Passing-Bablok", "Bland-Altman", "equivalence testing", "OOS investigation", "ICH Q2", "Q2(R2)", "Q14", "USP 1225", "ICH M10", "incurred sample reanalysis", "ISR", "CLSI EP", and any request to show that an assay works.
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 doing ANY task involving Supabase. Triggers: Supabase products (Database, Auth, Edge Functions, Realtime, Storage, Vectors, Cron, Queues); client libraries and SSR integrations (supabase-js, @supabase/ssr) in Next.js, React, SvelteKit, Astro, Remix; auth issues (login, logout, sessions, JWT, cookies, getSession, getUser, getClaims, RLS); Supabase CLI or MCP server; schema changes, migrations, declarative schemas, security audits, Postgres extensions (pg_graphql, pg_cron, pg_vector); debugging and troubleshooting errors or unexpected behavior on Supabase projects (HTTP errors, Postgres errors, RLS surprises, permission denied, schema cache issues, timeouts, Edge Function crashes, Realtime drops, Storage failures) and reading or querying logs (Logs Explorer, ClickHouse).
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。
Configure GitHub secret scanning and push protection, triage secret alerts, and run local pre-commit secret audits. Use when enabling secret scanning, handling blocked pushes, defining custom patterns, or checking a repo for accidental credentials before commit.
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 OpenAI, Anthropic, Google Gemini, or OpenRouter models from Netlify Functions or Edge Functions without managing provider API keys or accounts — the gateway injects credentials automatically. Reach for this when you add an AI chatbot or completion endpoint, generate images or text with Gemini/GPT/Claude, summarize form submissions with AI, build an LLM-backed API route, stream a long AI generation, or wire up any server-side AI provider call on Netlify. Covers provider SDK setup, injected env vars, model availability, rate limits, credit costs, streaming for long generations, and local dev with netlify dev or the Vite plugin.
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 the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, latest/current/default-model prompting guidance, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.
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。