2,080 件 ・ 関連度順
概要と使いどころ
Use when diagnosing or improving frontend performance, Core Web Vitals, bundle size, runtime rendering cost, network waterfalls, memory leaks, long tasks, Lighthouse findings, or performance budgets; Chinese triggers include performance optimization, page freeze, slow first screen, bundle volume, Web Vitals.
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 generating or editing diagrams, charts, visual assets, posters, UI mockups, product images, infographics, academic figures, comics, avatars, storyboards, brand boards, or image-edit workflows, especially when exported PNGs need visual QA and bounded self-repair. Prefer deterministic Mermaid/SVG/HTML/canvas sources for text-heavy diagrams; use HTML technical diagrams for browser-ready system blueprints, architecture, deployment topology, agent runtime, memory flow, before-after architecture, workflow, sequence, data-flow, lifecycle, runbook, PII/data-lineage, and state-machine diagrams. Do not use for ordinary UI polish without generated imagery.
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 a task needs the judgment of an Embedded/Firmware Engineer — analyzing worst-case execution time for a hard real-time deadline, choosing static over dynamic memory allocation to avoid fragmentation on a long-uptime device, keeping an interrupt service routine minimal and deferring work to the main loop, synchronizing a variable shared between an ISR and main context to prevent a torn read, or verifying a hardware register interaction against the datasheet's exact timing rather than observed bench behavior.
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。
Access ENCODE uniform analysis pipelines, generate user-specific Nextflow/WDL pipelines, manage compute resources, and integrate with cloud platforms. Use when the user wants to understand ENCODE pipelines, run pipelines on their own data, generate custom Nextflow workflows from ENCODE pipeline code, check compute requirements (CPU/GPU/memory), run pipelines in background, or integrate with Google Cloud, AWS, or other cloud platforms. Also use when the user asks about ENCODE pipeline outputs, processing standards, software versions, or wants to replicate ENCODE processing. Covers local execution, HPC, and cloud deployment with resource-aware scheduling. Use this skill for ANY pipeline execution, workflow generation, or compute resource management task involving ENCODE data.
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。
根据当前科研数字分身状态，生成结构化提示词，供用户提交给 ChatGPT 等带记忆功能的 AI，从中提取与画像构建相关的信息。不询问用户使用哪个 AI 工具，提示词对所有平台通用。
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。
解析用户从 ChatGPT 等 AI 获取的记忆回复（支持多个 AI 依次粘贴），与现有画像对比后，有据可查的条目内部整合直接写入并标注置信度；模糊/冲突/当前需求条目以选择题形式逐条确认；❌记忆不足条目不丢弃，转入步骤五追问队列；写入后执行字段级完整度扫描，列出缺口清单并逐批追问，达到必填门槛后才进入量表阶段。
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。
Keep long conversations coherent — summarization checkpoints, entity tracking, topic stacks, and graceful context handoffs. Use when dialogues run long or span sessions and the agent keeps losing the thread.
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。
Professional C: memory ownership, defensive programming, build hygiene, and systems-level correctness. Use when writing, reviewing, or maintaining C code.
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。
Build LLM applications with chain-style orchestration frameworks — prompts, chains, tool-calling agents, memory, and retrieval pipelines. Use when structuring multi-step LLM programs, adding tools to a model, or composing RAG systems in code.
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。
Coordinate teams of role-based agents — role definitions, task delegation, sequential and hierarchical workflows, and shared memory. Use when a job naturally splits into specialist roles collaborating.
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 and build LLM agents: ReAct loops, tool schemas, planning, memory, guardrails, evals, and human-in-the-loop. Use when building autonomous multi-step AI systems.
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。
Architect autonomous agent systems — perception/action loops, tool design, planning strategies, memory, and multi-agent orchestration patterns. Use when designing an agent from scratch or scaling a prototype to production.
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, write, or manage Meta ads and assets: ad copy, campaigns, spend, reports, audiences, catalogs, product feeds, feed refresh schedules, experiments, and policy. Always load for any request to create or write an ad, or to advertise a product or service, even when no platform is named; this includes sensitive or restricted categories. Always load for any question asking what a Meta, Facebook, or Instagram advertising policy means, allows, prohibits, or requires, including a standalone policy-definition question with no account context. Those questions must use ads_policy_tool, never browser search or memory. Load for a specifically named catalog or feed with an upload or refresh-schedule request; use Ads reads to resolve ownership before writing. Generic unnamed feeds need context. Whether an image, claim or piece of copy may be used in an ad is ALWAYS a Meta Ads task — 'can I use this in an ad', 'is it allowed', 'is this against policy', and any rights, likeness, celebrity, logo or trademark question about advertising with an image, including a follow-up about one just generated. Those are ads-policy questions, not general legal ones. An ad request uses the campaign workflow unless explicitly only an image or organic post.
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 for questions about a specific flight’s departure or arrival time, including “when’s my flight?” and confirmation of remembered times, plus flight status, delays, and cancellations. Verify the exact dated flight before answering; memory identifies the itinerary but does not verify its current schedule. Use FlightAware to monitor operational changes for an upcoming booked flight when the user directly asks for ongoing monitoring.
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。
Observes runtime behavior of binaries via QEMU emulation, GDB debugging, and Frida hooking — syscall tracing, breakpoints, memory inspection, and function interception. Use when running, tracing, or debugging a binary, verifying hypotheses from static analysis, or watching actual syscalls, network calls, or file access; requires human approval before any execution.
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。
Full-stack app pipeline — build, test, review, and deploy across one or ALL repos. Scans CLAUDE.md, MEMORY.md, TODOs, open PRs, and issues for shippable work. Supports 'ship all' for multi-repo. Use when: 'build and ship', 'ship it', 'ship all', 'deploy everything', 'full pipeline', 'what needs shipping', 'end to end'.
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。
Pre-launch quality gate for games: audit rendering and memory performance against platform budgets, run QA for crash-causing defects and platform certification blockers, review accessibility for CVAA and platform compliance, test security against cheating and save tampering.
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。
Feature discovery agent — mines project docs, memory, user feedback, and competitor gaps to surface the highest-leverage features to build next. Outputs a prioritized feature backlog.
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。
Profile and audit mobile app performance -- cold/warm/hot startup time, memory leaks, battery drain, network efficiency, frame rate jank, and binary size. Triggers: diagnosing slow launches, investigating ANRs, reducing app size, or benchmarking rendering performance.
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。
micro-ROS: agent, rclc client API, micro_ros_setup, custom transports, static memory.
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, update or resume a GitHub issue as durable resolution memory when requested.
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。
Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.
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 on SAM 3 video segmentation, vaila_sam.py, checkpoint handling, or GPU memory 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。
Inspects Google Cloud Filestore capacity and utilization, evaluates storage scaling rules, and performs capacity autoscaling (scale UP for low free space or scale DOWN for cost optimization). Use when monitoring Filestore instance headroom, resizing instance shares, configuring automated growth/shrink thresholds (custom thresholds apply globally across projects in session memory), or preventing out-of-space outages. Don't use for Cloud Storage (GCS) buckets, Persistent Disk block storage, or NetApp Volumes.
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。