348 件 ・ 関連度順
概要と使いどころ
Use when the user wants Cursor to learn from recent local sessions, asks for an offline sleep or dream cycle, wants to consolidate recurring work into a Cursor skill, or requests SkillOpt-Sleep status, harvest, dry-run, run, scheduling, review, or adoption. Drives the validation-gated skillopt_sleep engine with Cursor transcripts and the optional Cursor Agent CLI backend.
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 wants Codex to self-improve from past usage, asks about a nightly/offline 'sleep' or 'dream' cycle, wants Codex to review past sessions, learn preferences, consolidate memory/skills, run dry-run/run/adopt/status for SkillOpt-Sleep, or schedule background self-optimization. Drives the skillopt_sleep engine: harvest past sessions -> mine recurring tasks -> replay through a selected backend -> consolidate validated memory + skills behind a held-out gate.
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 a bounded, extractive, structurally idempotent rollup of recent Obsidian wiki log entries, with dry-run preview by default and one optional transaction apply. Use for manual log compression without modifying child pages. Triggers: fold the log, run a fold, run wiki-fold, log rollup, roll up log entries, commit the fold.
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 validating or documenting a multi-stage KrillinAI CLI output plan; the current pipeline command supports dry-run planning only, not end-to-end 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。
Build the effects of a close-combat energy skill as one visual language in Three.js. Lightning that reads as lightning, dynamic but never in your face: trees of strips (trunk, forks of forks, hair-fine branchlets) with a fine white core in a violet-blue glow, each channel reaching out as a stepped leader, flaring as it connects, holding its shape and fading through an afterglow, one discharge at a time; a storm-cloud orb (a lit billow surface that churns in place, 10-15 plasma-globe arcs from a small white core to its inner wall, crawlers on the rim, a hairline fresnel); ground strikes that flash when the leader arrives, crawl along the stone's own cracks and reflect in wet stone (the demo's floor is a scanned CC0 texture); a torn black shadow with dry-brush edges that hangs off the orb and streams behind it, air bursts felt as refraction, orange sparks, fractured rocks that land or float in a storm domain, and impact frames that hold, invert in two-tone ink with tapered speed lines, shake and bend the lens, limited to three flashes a second. Use for anime or dark-fantasy skill effects, lightning or thunder fists, plasma or energy balls, charge-ups, dashes with shadow trails, ground strikes, storm or domain rings, ultimates, hit-stop, impact frames, negative flashes, screen shake, "black lightning", "thunderstorm fist", "realistic lightning", "afterimage" or "skill effects only". Includes a reusable module and a five-beat demo with failure-mode switches.
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。
安全查找并清理已合并或过期的 Git 分支，支持 dry-run 模式与自定义基准/保护分支
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。
Safely find and clean up merged or stale Git branches with dry-run mode and custom base/protected branches support
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。
Smart contract security audit — 10 DeFi bug classes (accounting desync, access control, incomplete path, off-by-one, oracle, ERC4626, reentrancy, flash loan, signature replay, proxy), pre-dive kill signals (TVL < $500K etc), Foundry PoC template, grep patterns for each class, and real Immunefi paid examples. Use for any Solidity/Rust contract audit or when deciding whether a DeFi target is worth hunting.
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。
Smart contract security audit — 10 DeFi bug classes (accounting desync, access control, incomplete path, off-by-one, oracle, ERC4626, reentrancy, flash loan, signature replay, proxy), pre-dive kill signals (TVL < $500K etc), Foundry PoC template, grep patterns for each class, and real Immunefi paid examples. Use for any Solidity/Rust contract audit or when deciding whether a DeFi target is worth hunting.
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。
Causal inference design audit for the sewage-house-prices project. Runs a 4-phase review (claim identification, design validity, inference, polish) covering hedonic pricing, repeat sales, long difference, DiD/event studies, upstream/downstream, and dry spill strategies. This skill should be used when asked to "check the econometrics", "audit the identification", "review the strategy", or when verifying that code matches the stated design.
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。
小红书评论互动运营：列出我的笔记、抓取某条笔记下的评论、按画像语气逐条回复、以及删除评论 （含自己发的回复）。基于 Playwright + 持久化登录态，headless 即可运行，与 skill-xhs-publisher 共用同一登录态。评论抓取走接口响应拦截（稳）；回复/删除走 DOM 文本定位（无坐标）+ 逐字符输入反检测； 带已回复去重、逐条间隔防风控、dry-run 预演。用 notes 列笔记或 fetch --url 贴链接，免手动拆 token。 触发：抓小红书评论 / 回复评论 / 删除评论 / 删评 / 维护评论区 / 看我某条笔记的评论。
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。
Queue processing with fresh context per phase. Processes N tasks from the queue, spawning isolated subagents to prevent context contamination. Supports serial, parallel, batch filter, and dry run modes. Triggers on "/ralph", "/ralph N", "process queue", "run pipeline tasks".
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 GitHub issues, spawn sub-agents to implement fixes and open PRs, then monitor and address PR review comments. Usage: /gh-issues [owner/repo] [--label bug] [--limit 5] [--milestone v1.0] [--assignee @me] [--fork user/repo] [--watch] [--interval 5] [--reviews-only] [--cron] [--dry-run] [--model glm-5] [--notify-channel -1002381931352]
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。
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
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。
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file upload, calibrate grader, deploy fine-tuned model, evaluate fine-tuned model. DO NOT USE FOR: general model deployment without fine-tuning (use deploy-model), agent creation (use agents), prompt optimization without training (use prompt-optimizer).
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 Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, configure AI backend, token metrics, AI cost control, convert API to MCP, import OpenAPI to gateway.
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 AI applications using Azure AI Projects SDK for JavaScript (@azure/ai-projects). Use when working with Foundry project clients, agents, connections, deployments, datasets, indexes, evaluations, or getting OpenAI clients.
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 Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
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。
Guide for creating effective skills for AI coding agents working with Azure SDKs and Microsoft Foundry services. Use when creating new skills or updating existing skills.
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。
Analyze actual Azure spend, bill changes, and AKS or AI costs. WHEN: "Azure cost breakdown", "most expensive resources", "why did my bill increase", "cost spike", "unexpected charge", "Cosmos DB charges", "Foundry model costs", "AKS cluster cost", "namespace cost", "enable AKS cost analysis", "idle AKS capacity". DO NOT USE FOR: forecasts, pricing estimates, rightsizing, commitments, or budgets.
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。
Apply Hunt and Thomas-inspired pragmatic programming rules when improving engineering judgment, feedback loops, DRY knowledge, orthogonality, automation, or adaptability.
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。
Apply meta-principles of software craftsmanship: DRY, orthogonality, tracer bullets, and design by contract. Use when the user mentions "best practices", "pragmatic approach", "broken windows", "tracer bullet", "software craftsmanship", "avoid technical debt", "code ownership", or "how do I become a better developer". Also trigger when evaluating build-vs-buy decisions, designing estimation approaches, or choosing between reversible and irreversible architectural decisions. Covers estimation, domain languages, and reversibility. For code-level quality, see clean-code. For refactoring techniques, see refactoring-patterns.
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 code review with DRY, YAGNI, abstraction, and test coverage principle enforcement
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 Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, configure AI backend, token metrics, AI cost control, convert API to MCP, import OpenAPI to gateway.
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。