「agentic」の検索結果

RAG & Knowledge Architect: Retrieval systems: ingestion, chunking, embeddings, hybrid search, reranking, evals.

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。

LLMOps Architect: Run LLM features in production: model routing, evals, observability, caching, cost, safety.

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。

Rules against AI slop in code, comments, commit messages and docs. Use whenever you write or edit code, write documentation, a README, a commit message or a PR description, or review changes for quality.

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。

Cross-chain & L2 Architect: Chain selection, rollups, bridges and cross-chain messaging with their trust assumptions.

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。

DeFi Protocol Architect: Mechanism design for AMMs, lending, vaults and derivatives: oracles, liquidations, attack surfaces.

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。

AI-assisted development, testing, and reverse engineering through structured agent collaboration

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。

Multi-source research with citations using You.com. Use for questions that need several sources compared and synthesized, such as library or framework comparisons, tool and vendor evaluations, market research, or deep-dive reports. Routes between a cost-conscious agentic search loop and the one-shot `you-research` MCP tool.

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 auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level 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。

Python backend development standards for FastAPI, SQLModel, pytest, Ruff, and Pyright. Use when writing or modifying backend code in src/merchant, src/payment, src/apps_sdk, agents, tests, API routes, services, models, or protocol logic.

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。

Dependency vulnerability remediation workflow for this repository. Use when fixing CVE, BDSA, GHSA, OSV, npm, PyPI, Dependabot, Black Duck, Snyk, or other package vulnerability findings, especially when the user asks for minimal package-only updates, validation, commits, branches, or pull 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。

Create high-quality, model-aware system prompts for any LLM (Claude, GPT, Gemini, open-source, etc.). Use this skill whenever the user wants to create, write, build, design, draft, or improve a system prompt, system instructions, or custom instructions for any AI model. Also trigger when the user asks about prompt engineering, prompt design, prompt optimization, or wants to define behavior for an AI assistant, chatbot, agent, or any LLM-powered application — even if they don't explicitly say "system prompt". Covers all use cases including chatbots, agentic systems, tool-use workflows, content generation, data extraction, code assistants, and multi-turn conversations.

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 guide for designing and orchestrating multi-agent systems, agent swarms, 2026 Anthropic agentic design patterns, graph-based workflows (LangGraph, OpenAI Agents SDK, Google ADK, Mastra.ai), shared state memory, and human-in-the-loop guardrails in English and Indonesian.

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 guide for long-term episodic memory integration (Mem0 v2, Letta/MemGPT, Zep v2), memory tier architecture, pgvector HNSW storage, and unified context management for autonomous AI agents / Panduan ahli untuk integrasi memori episodik jangka panjang (Mem0 v2, Letta/MemGPT, Zep v2), arsitektur tier memori, penyimpanan pgvector HNSW, dan manajemen konteks terpadu untuk agen AI otonom.

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 guide for designing Machine-to-Machine (M2M) micro-economies, autonomous agent wallets, and swarm budget allocation / Panduan ahli merancang ekonomi mikro antar-agen (M2M), dompet agen otonom, dan alokasi anggaran swarm.

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 modifying agentic contextual building blocks like skills (SKILL.md), slash commands, agents, hooks, plugins, marketplaces, and instruction files (CLAUDE.md, AGENTS.md, GEMINI.md, QWEN.md, Cursor, Copilot, Cline, or Kiro rules), tool configuration such as an OpenCode `opencode.json`, Muse Code hooks (`.muse/hooks.json`), a Grok Build `.grok/` project layer or a Google Antigravity customization root (`.agents/`, `.agent/`, `_agents/`, `_agent/` — hooks, MCP config, rules, registries and plugins), and committed project memory (`.agents/memory/`). Run skillsaw on the files you touched, apply autofixes, resolve remaining violations with `skillsaw explain` guidance, and re-lint until clean before considering the work complete.

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 the AI coding assistant and agentic tool ecosystem, assess skillsaw's competitive position, identify emerging patterns and missing capabilities, and produce a prioritized strategic report as a GitHub issue. Use when evaluating ecosystem trends and product gaps.

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。

Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination

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。

Bridge layer connecting the emerging agentic coordination protocol ecosystem (MCP, A2A, ANP, AGNTCY, AITP, LMOS, AAIF) to the plurigrid/asi skill graph. Maps protocol-level identity mechanisms (W3C DID, OAuth 2.1, IPSIE, Entra Agent ID, Agent Cards, passport.gay) into GF(3)-classified skills. The central unsolved problem — no unified identity across MCP+A2A+A2UI — is directly addressed by the bisimulation oracle composition.

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。

Bridge layer connecting the Agentic AI Foundation (AAIF) governance structure — Linux Foundation stewardship of MCP, goose, AGENTS.md — to the plurigrid/asi skill graph. Formalizes AAIF-compatible skill registration, IPSIE identity profiles, and the cross-protocol interoperability surface. Maps the AAIF platinum member ecosystem (AWS, Anthropic, Block, Bloomberg, Cloudflare, Google, Microsoft, OpenAI) to specific ASI skill integration points.

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 converting an agentic repository into a ChatGPT/Codex Plugin or when building, repairing, validating, packaging, submitting, publishing, or auditing an existing Plugin.

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 a repository has a valuable agentic workflow, prompt chain, playbook, command, or runbook that should become a portable ChatGPT/Codex Skill without losing its real decision logic.

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 an existing repository contains agents, prompts, workflows, playbooks, commands, or Skills and you need to identify which capabilities are worth converting into a ChatGPT/Codex Plugin.

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。

Agno AI agent framework. Use for building multi-agent systems, AgentOS runtime, MCP server integration, and agentic AI development.

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。

Dependency management guidelines for Jarvy - crate selection criteria, feature flag best practices, version management, security auditing with cargo-audit and cargo-deny.

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。