「agents」の検索結果

Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.

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 custom AI subagents with proper plugin structure, persona generation, and companion routing 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。

Instrument AI agents with tracing, token metrics, latency, and cost visibility. Use for reliability and debugging.

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。

Complete B2B SaaS marketing agency powered by 79 AI agents across 17 specialties. This is the entry point for ALL marketing requests. Routes to the right specialist team: content marketing, SEO, paid media, social media, email marketing, design, sales enablement (including technical presales — demos, proofs of concept, security questionnaires), product marketing, account-based marketing (ABM), growth, analytics, project management, or client operations. Use this skill when the request spans multiple disciplines or when unsure which specialist to invoke. Also triggers on: marketing help, marketing team, marketing agency, what can you do, marketing capabilities.

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 when context is filling up: large outputs, long files, repeated reads, fan-out planning. Route bulk to subagents; keep summaries in the main thread, not raw 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。

Apply to any non-trivial work, not just bulk work: edits, migrations, analyses, checks. Build the tool that does it or proves it (codemod, script, generator, or a skill your subagents follow) instead of working by hand. The tool is the artifact a reviewer can rerun.

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 creating, editing, reviewing, packaging, or organizing AI-agent skills and deciding whether a skill should be text-only, script-backed, or backed by an ML/specialist pipeline.

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 OpenAI Codex CLI as a peer collaboration partner — same standing as Claude, not a one-shot tool. Bring Codex in EARLY (ideation, framing decisions, stuck moments), not only for late peer-review. Hand it strategic context (target, research, draft, reasoning), iterate conversationally over ~3-8 turns, converge before surfacing to the human. Invoke when the user says "check / iterate / ideate with Codex" or "ask Codex" — and proactively when stuck between two reasonable framings, pressure-testing a load-bearing claim or structure, or about to ask the human to pick between options two agents could resolve.

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。

Quietly reuse and improve verified coding lessons across agents. Use for a nontrivial debugging or implementation problem where an earlier solution could help, when a Blaze offer or receipt appears, or when the user asks to inspect, install, update, contribute to, or link Blaze. Check applicability, verify locally, and close the lookup with an honest outcome and contribution disposition without adding routine user-visible narration.

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。

Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes: Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/spec asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use when the user wants to review a branch, a PR, work-in-progress changes, or asks to "review since X".

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。

Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes.

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Build AI applications using the Azure AI Projects Python SDK (azure-ai-projects). Use when working with Foundry project clients, creating versioned agents with PromptAgentDefinition, running evalua...

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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, evaluation...

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。

Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates 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。

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector s...

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。

Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessar...

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。

Manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.

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。

Email infrastructure for AI agents. Create accounts, send/receive emails, manage webhooks, and check karma balance via the AgentMail API.

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。

Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.

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。

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

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 production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

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。

Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

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 any code, documentation, config, dependency, test, review, or maintenance task in the Nauthilus repository that must follow AGENTS.md, POLICY.md, focused reproducer-first debugging, strict DRY/OOP boundaries, English comments, GOEXPERIMENT=runtimesecret test discipline, Makefile-driven validation, prompt-sync checks, and dirty-tree preservation.

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。

Govern FriendlyBet agent values, professional standards, anti-hallucination behavior, truthfulness, risk reporting, AI resource discipline, feedback loops, skill improvement, performance reviews, decision-rights hygiene, and team quality. Use when creating, reviewing, promoting, correcting, or retiring agents; reducing hallucinations or mistakes; reviewing AI/tool usage; handling agent incidents; or aligning work with Eyal's philanthropic user-first approach.

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。