4,860 件 ・ 関連度順
概要と使いどころ
This skill should be used when designing hosted or background agent infrastructure: sandboxed execution, remote coding environments, warm pools, session persistence, multiplayer collaboration, self-spawning agents, or Modal-style sandboxes.
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 designing multi-agent systems that need context isolation, supervisor or swarm coordination, explicit handoffs, parallel execution, or a decision on whether multiple agents are justified.
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。
Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.
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-deployment security audit of Solidity smart contracts in a Foundry project. Combines static analysis (Slither, Aderyn), symbolic execution (Mythril), and property-based testing (forge fuzz + invariant tests with handlers) to catch reentrancy, access-control, oracle/price manipulation, and arithmetic bugs BEFORE deploying to an EVM chain. Also enforces key hygiene (no plaintext private keys, encrypted cast keystore) and a secure deploy workflow. Use when writing, reviewing, testing, or deploying Solidity/Foundry contracts, building a dApp, or working with forge/cast/anvil, MetaMask, or Web3/DeFi 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。
Use when main results pass result-to-claim (`claim_supported = yes` or `partial`) and ablation studies are needed for paper submission. A secondary Codex agent designs ablations from a reviewer's perspective; the local executor reviews feasibility and implements.
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。
Search research papers via Gemini for broad literature discovery. Use when user says "gemini search", "gemini papers", "search with gemini", or wants AI-powered literature discovery beyond arXiv/Semantic Scholar indexes.
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。
Workflow 1.5: Bridge between idea discovery and auto review. Reads EXPERIMENT_PLAN.md, implements experiment code, deploys to GPU, collects initial results. Use when user says "实现实验", "implement experiments", "bridge", "从计划到跑实验", "deploy the plan", or has an experiment plan ready to execute.
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。
Turn an exploration (threads, findings, charts) into a single Markdown report — note, blog post, executive summary, KPI dashboard, slide brief, or multi-section analytical report, with embedded charts.
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。
Agent-native memory for OpenClaw that structures memory from agent trace, execution history, decisions, tool calls, and conversations into durable long-term memory primitives.
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。
Run large codebase migrations and multi-file refactors. Uses the Composio CLI to coordinate issue tracking, batched PRs, and CI verification while the agent executes the transforms locally across hundreds of files.
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 agents through using the OpenShell CLI (openshell) for sandbox management, gateway registration, provider configuration and refresh, profile management, policy iteration, settings, service exposure, BYOC workflows, and attached-provider inference. Covers basic through advanced multi-step workflows. Trigger keywords - openshell, sandbox create, sandbox exec, sandbox connect, logs, provider create, profile list, profile describe, provider refresh, policy set, policy get, settings, service expose, forward, port forward, BYOC, bring your own container, inference, use openshell, run openshell, CLI usage, manage sandbox, manage provider, gateway add, gateway select.
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。
Run state-of-the-art machine learning models directly in JavaScript. `@huggingface/transformers` supports text, vision, audio, and multimodal tasks in browsers and Node.js / Bun / Deno, with WebGPU or WASM 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。
MNN CPU 后端性能归因分支（`skills/cpu/` 下，另一分支是 `cpu/kernel` kernel 开发）。按五层（Runtime 线程 / Executor 调度 / Layout 内存 / Dispatch 函数表 / Kernel ISA）定位瓶颈，含 bound 类型判定、op 级实验回路、跨框架 op 对 op 对比、跨层不一致的事后定位，以及 ARM / x86_64 / RISC-V 三侧「我到底跑在哪条 ISA 路径上」的诊断面。CPU 上算子或 LLM prefill/decode 慢、线程数或内存占用异常、出现性能回归、要与外部推理框架逐算子对比时使用。
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。
Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff.
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。
Log a CLI in with a browser/device-code flow (aws sso, gh, glab, gcloud, …) and save the result to the keychain so later commands can request it through execute.credentials.
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。
Show the app connection picker or setup widget when users ask to connect apps, reopen setup, or need an app that isn't connected yet. Use QM’s authenticated backend for app discovery, consent, and execution without exposing project keys.
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。
Records research provenance as a post-task epilogue, scanning conversation history at the end of a coding or research session to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara/ directory with user-vs-AI provenance tags. Use as a session epilogue — never during execution — to maintain a faithful, auditable trace of how a research project actually evolved.
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。
Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.
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。
Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.
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。
Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.
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。
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
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。
Use when writing Spark jobs, debugging performance issues, or configuring cluster settings for Apache Spark applications, distributed data processing pipelines, or big data workloads. Invoke to write DataFrame transformations, optimize Spark SQL queries, implement RDD pipelines, tune shuffle operations, configure executor memory, process .parquet files, handle data partitioning, or build structured streaming analytics.
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。
Optimizes database queries and improves performance across PostgreSQL and MySQL systems. Use when investigating slow queries, analyzing execution plans, or optimizing database performance. Invoke for index design, query rewrites, configuration tuning, partitioning strategies, lock contention resolution.
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。