580 件 ・ 関連度順
概要と使いどころ
Analyze malicious Linux ELF binaries — botnets, cryptominers, ransomware, and rootkits targeting Linux servers, containers, and cloud infrastructure — through static analysis, dynamic tracing, and reverse engineering of x86_64 and ARM samples. Use when investigating Linux malware, triaging a suspicious ELF binary, assessing a compromised Linux server, or analyzing container-targeted malware.
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。
Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom protocols to reverse-engineer beacon patterns, command structures, data encoding, and infrastructure (primary servers, fallback domains, dead drops). Use after reverse engineering reveals network traffic needing protocol analysis or when building detection signatures for a framework like Cobalt Strike, Metasploit, or Sliver.
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 Iron Proxy for external accounts and APIs, including GitHub through gh. Connect credentials through the operator console and diagnose blocked requests without exposing tokens or adding unnecessary MCP servers.
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。
Customize your own agent — add capabilities, install packages, add MCP servers, edit code or CLAUDE.md. Use when the user asks you to add a feature, install a tool, or modify how you work. For non-trivial code changes, delegate to a builder agent via create_agent.
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, extend, debug, or contribute to PicoClaw itself. Use when the task is about PicoClaw CLI commands, config.json, gateway, auth, models, skills, MCP servers, cron, routing, sessions, self-evolution, built-in slash commands, or repository internals. Use PicoClaw-native workflows, terminology, paths, and configuration.
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。
Set up your bio-research environment and explore available tools. Use when first getting oriented with the plugin, checking which literature, drug-discovery, or visualization MCP servers are connected, or surveying available analysis skills before starting a new 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 the user needs help configuring Crush — writing crushrc (the Bash config format) or crush.json, setting up providers, models, LSPs, MCP servers, hooks, skills, permissions, or changing Crush 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。
Answer any question about Qwen Code usage, features, configuration, and troubleshooting by referencing the official user documentation. Also helps users view or modify their settings.json. Invoke with `/qc-helper` followed by a question, e.g. `/qc-helper how do I configure MCP servers?` or `/qc-helper change approval mode to yolo`.
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 ship production-ready MCP (Model Context Protocol) servers from OpenAPI contracts instead of hand-written tool wrappers. Python and TypeScript support, schema validation, safe evolution. Use when exposing an existing API as an MCP server, building tool integrations for Claude or Codex or Cursor, or scaffolding an MCP project from scratch.
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。
Index of 37 advanced engineering agent skills for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Use when browsing or choosing among the POWERFUL-tier engineering skills: agent design, RAG, MCP servers, CI/CD, database design, observability, security auditing, changelog/release automation, reliability (SLO/chaos/flags/operators), platform ops.
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。
Phase 1 of building a Claude Managed Agent — interview the founder about the one job the agent should do, then produce a build sheet (CMA primitives table + v1/v2 deferrals + eval plan) WITHOUT needing their API key yet. Use when the user says "help me scope an agent", "I have an idea for an agent", "what should this agent be", or when the orchestrator routes phase=interview. Drives the six intake slots (job, trigger, inputs, actions, definition-of-done, recurrence) via AskUserQuestion, maps them to primitives with interview_planner.py, assembles build-sheet.json with build_sheet_builder.py, and validates limits with primitives_validator.py. Connectors are mockable in v0 (schema-true custom tools); real MCP servers become v1 deferrals. Distinct from stage-launch (which turns the sheet into payloads).
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 writing or reviewing asynchronous C++ networking code with Boost.Asio or standalone Asio — TCP/UDP servers and clients, SSL/TLS, timers, strands, io_context, co_spawn, awaitable, async_read/async_write, asio::spawn, yield_context, or pre-C++20 completion-handler callbacks.
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 and manage trusted local Paseo plugins. Use when the user asks to create, edit, install, reload, enable, disable, remove, or troubleshoot a Paseo plugin; add lifecycle hooks; transform agent configuration, environment, MCP servers, or workspace creation; automate permissions or turn follow-ups; add a screen, sidebar header or footer item, or workspace panel; add Command Center items or slash commands; add composer pills or attachment sources; transform, render, or append agent timeline items; contribute a theme; use Paseo from plugin code; or add plugin RPCs.
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。
Canonical reference for Kortix projects, Apps (sites, servers, backends with volumes) and internal business apps, the CLI, sessions, sandboxes, change requests, triggers, connectors, secrets, system skills, and the two session harnesses, pi and OpenCode. Covers `kortix.yaml` versions 1 and 2, serverless App deployments, harness configuration (`harnesses/pi/`, `harnesses/opencode/`), session identity, credential boundaries, and the complete pi and OpenCode references. Load when the user asks how Kortix works, what Kortix can do, how to deploy an App, how an agent discovers platform instructions, how to customize or configure this project's agents (create or fix an agent, subagent, skill, tool, plugin, extension, pi package, command, MCP server, model or permission rule; edit `opencode.jsonc`), how to edit `kortix.yaml`, how to use the `kortix` CLI, how to land work through a change request, or how to schedule and automate work.
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, deploy and operate Kortix Apps through the pre-authenticated CLI or SDK: static sites and SPAs, Dockerfile or OCI servers, workers, and stateful backends (a database, a Convex backend) with persistent volumes, snapshots and built-in Kortix sign-in. Use when the user asks to publish, host, deploy, preview, inspect, scale, wake, suspend, roll back, debug, or remove an App; asks for a database, an API, a backend, a data model, auth, or 'store this'; asks for an internal tool, business app, CRM, tracker, dashboard, portal, or any app the team logs in to; before writing any Convex code; or asks what an App costs or how it runs.
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。
Export raw OpenTelemetry traces from an AI application to Confident AI's Observatory. TRIGGER when the user wants to send OpenTelemetry or OTLP traces/spans from an LLM app, agent, RAG pipeline, or chatbot to Confident AI; configure the Confident AI OTLP endpoint; set confident.span.* or confident.trace.* attributes; export AI-app traces without the deepeval Python package; wire an OTLPSpanExporter, OpenTelemetry Collector, or vendor-neutral OTel SDK to Confident AI; or pick the US vs EU OTLP endpoint. Language-agnostic: the mechanism is OTLP attribute keys plus an exporter endpoint. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill); for instrumenting with the DeepEval SDK's @observe decorator or framework integrations (use the `deepeval-tracing` skill); or for non-AI software such as web servers, CRUD backends, or infrastructure: the confident.* attributes describe AI components only.
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。
MCP Apps integration for json-render. Use when building MCP servers that render interactive UIs in Claude, ChatGPT, Cursor, or VS Code, or when integrating json-render with the Model Context Protocol.
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。
Browse and search the LangBot Space marketplaces (plugins, MCP servers, skills) through the Space MCP server. Use when an AI agent needs to discover LangBot extensions on space.langbot.app over MCP. Covers the /mcp endpoint, Personal Access Token (PAT) auth, the tool surface, and client configuration. Triggers on "langbot space mcp", "search langbot plugins", "langbot marketplace mcp", "space.langbot.app mcp".
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。
Operate a LangBot instance through its built-in MCP (Model Context Protocol) server. Use when an AI agent needs to manage LangBot — list/create/update/delete bots, agents, pipelines, models, knowledge bases, MCP servers, and skills — over MCP instead of raw HTTP. Covers the /mcp endpoint, API-key auth (web-UI lbk_ keys and the config.yaml global key), the tool surface, and client configuration. Triggers on "langbot mcp", "manage langbot via mcp", "langbot /mcp", "langbot mcp server".
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。
Deploy and run ML experiments on local or remote GPU servers. Use when user says "run experiment", "deploy to server", "跑实验", or needs to launch training jobs.
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 and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to custom datasets, converting JAX checkpoints to PyTorch, running policy inference servers, or debugging norm stats and 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。
Use when building, debugging, or extending MCP servers or clients that connect AI systems with external tools and data sources. Invoke to implement tool handlers, configure resource providers, set up stdio/HTTP/SSE transport layers, validate schemas with Zod or Pydantic, debug protocol compliance issues, or scaffold complete MCP server/client projects using TypeScript or Python SDKs.
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。
Provides idiomatic Kotlin implementation patterns including coroutine concurrency, Flow stream handling, multiplatform architecture, Compose UI construction, Ktor server setup, and type-safe DSL design. Use when building Kotlin applications requiring coroutines, multiplatform development, or Android with Compose. Invoke for Flow API, KMP projects, Ktor servers, DSL design, sealed classes, suspend function, Android Kotlin, Kotlin Multiplatform.
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 modifying an already-installed workspace capability — adding or editing its MCP servers, skills, integrations, or agent guidance. Use when the user wants to customize, change, or extend an existing capability.
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。