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概要と使いどころ
Start a new private GitHub project the AOS way: create the private repo, bootstrap AOS (project card, AGENTS.md, wiki, memory, CI choice), push after GO, and confirm the first CI run is green and the repo is still private. Use for "new project", "start a repo", "bootstrap a project".
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 implementing, integrating, upgrading, debugging, or answering anything involving third-party APIs, libraries, frameworks, CLIs, cloud services, model or provider SDKs, fast-moving product behavior, unfamiliar repo docs or specs, errors that may indicate API drift, or high-stakes auth, security, billing, data, migration, deployment, compliance, or privacy behavior. Forces a docs pass before coding from 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。
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application 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。
Work like Manus: Use persistent markdown files as your "working memory on disk."
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。
BDB local-first long-term memory engine (memB). Use when querying, remembering, or adapting preferences, code architectures, and developer patterns across 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。
Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
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 deep scan and ingest project files (.md, .json, agent.md, .openwiki) and past conversation logs into the local memB vector memory engine.
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。
Interview a project folder into AOS and write the result: a stable slug and domain in .aos/project.json, an OpenWiki wiki that is actually watched and does not index junk, memB memory bound to the slug, a Synapse map, AGENTS.md with its harness symlinks, and optionally CI and issue triage.
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。
Audit and improve SwiftUI runtime performance. Use when diagnosing slow rendering, janky scrolling, high CPU, memory usage, excessive view updates, layout thrash, body evaluation cost, identity churn, view lifetime issues, lazy loading, Instruments profiling guidance, and performance audit requests.
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。
Integrate on-device AI using Foundation Models framework, Core ML, and open-source LLM runtimes on Apple Silicon. Covers Foundation Models (LanguageModelSession, @Generable, @Guide, SystemLanguageModel, structured output, tool calling), Core ML (coremltools, model conversion, quantization, palettization, pruning, Neural Engine, MLTensor), MLX Swift (transformer inference, unified memory), and llama.cpp (GGUF, cross-platform LLM). Use when building tool-calling AI features, working with guided generation schemas, converting models, or running on-device inference.
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。
Memory management, profiling, optimization techniques, and Instruments usage for high-performance iOS apps
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 a project's past Codex sessions, memory files, and existing local skills to recommend the highest-value skills to create or update. Use when a user asks what skills a project needs, wants skill ideas grounded in real project history, wants an audit of current project-local skills, or wants recommendations for updating stale or incomplete skills instead of creating duplicates.
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。
Audit and improve SwiftUI runtime performance from code review and architecture. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps, and to provide guidance for user-run Instruments profiling when code review alone is insufficient.
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。
This skill should be used when the user asks how to "use VibeTags", "add VibeTags annotations", "set up AI guardrails", "protect code from AI", "configure AI platforms", asks about @AILocked, @AIContext, @AIDraft, @AIAudit, @AIIgnore, @AIPrivacy, @AICore, @AIPerformance, @AIContract, @AITestDriven, @AIThreadSafe, @AIImmutable, @AIDeprecated, @AIObservability, @AIRegulation, @AIArchitecture, @AILegacyBridge, @AIStrictClasspath, @AIInternationalized, @AIPublicAPI, @AISchemaSafe, @AIStrictExceptions, @AIStrictTypes, @AIParallelTests, @AIIdempotent, @AIFeatureFlag, @AISecure, @AICallersOnly, @AISandboxOnly, @AIMemoryBudget, @AIPure, @AIDomainModel, @AIExtensible, @AIInputSanitized, @AISecureLogging, @AIExplain, @AIPrototype, @AISunset, @AITemporary annotations, or wants to control how AI tools interact with Java 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。
Create a new Hindsight-powered subagent with long-term memory. Use when the user wants a specialized agent that learns and remembers across sessions.
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: Good night Team, EOD, Wrapping up, or end-of-day consolidation. Archives session context, promotes durable patterns, and compacts live agent memory files for the next session.
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 agent captures a correction from the team lead, hits an unexpected error, discovers a capability gap, or finds a better practice. Logs to .learnings/ and promotes durable patterns to permanent project 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。
Token budgeting discipline for all agents. Treats tokens as a resource from session start — not just when the buffer hits 60%. Covers prompt compression, context hygiene, handoff packing, and HOT memory pruning.
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。
Capture and inspect iOS leaks and memgraphs. Use when debugging leaked objects, retain cycles, memory growth, or before/after leak evidence.
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。
Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based 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。
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.
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 efficient C code with proper memory management, pointer
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。
Integrate AFL++ coverage-guided fuzz testing into CI/CD pipelines to discover memory corruption, input handling, and logic vulnerabilities in C/C++ and compiled applications.
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Provides digital forensics and signal analysis techniques for CTF challenges. Use when analyzing disk images, memory dumps, event logs, network captures, cryptocurrency transactions, steganography, PDF analysis, Windows registry, Volatility, PCAP, Docker images, coredumps, side-c
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。