'dj' 검색 결과

Use when designing evaluation suites for agentic systems, choosing agent benchmarks, measuring multi-agent performance, or critiquing single-score evaluation of agents. Based on arXiv:2605.26112v1. Trigger phrases: "evaluate my agent", "benchmark agent", "measure agent reliability", "pass^k evaluation", "agent metrics beyond success rate", "trajectory quality", "memory hygiene metric", "longitudinal agent eval", "why my agent scores well on benchmarks but fails in production".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when evaluating MCP-enabled agents, tool-using LLM systems, multi-server workflows, tool-selection quality, trajectory quality, task completion, or MCP security regressions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Merges mature lessons from a domain memory file into its instruction file. Syntax: `/memory-merger >domain [scope]` where scope is `global` (default), `user`, `workspace`, or `ws`. Use when a domain memory file has accumulated lessons that should become permanent instructions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Builds AI-powered trading and financial analysis agents with multi-agent LLM architectures: research pipelines, sentiment signals, portfolio optimization, backtesting, risk guardrails, and market monitoring. Use when designing or implementing an LLM-based trading or market-analysis system.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generates a complete, working application from an OpenAPI specification using the active framework's conventions: models, routes, validation, and tests. Use when scaffolding a service from an existing OpenAPI/Swagger spec rather than hand-writing endpoints.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Also use for async Python patterns, Pydantic schemas for LLM structured outputs, retry strategies for LLM calls, background AI jobs, or any service combining FastAPI and LLMs. If Python + AI backend is in scope — use this skill

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user asks to work on a Python FastAPI project. Triggers on keywords Python, FastAPI, Pydantic, SQLAlchemy.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create, update, refactor, explain, or review Semantic Kernel solutions using shared guidance plus language-specific references for .NET and Python. Use when working on applications, plugins, or function-calling flows built on Semantic Kernel.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Runs an autonomous experiment loop on any programming task: you define the goal, metric, and eval command; the agent edits code, measures, and keeps or discards each change until interrupted. Use when optimizing a measurable target (speed, accuracy, size, score) by iteration.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when diagnosing agent benchmarks or runtime failures from trajectories instead of aggregate pass rates. Builds shared action-observation decision landscapes, identifies productive cores and trap regions, profiles Access/Trap/Repair behavior, and designs conservative trap-triggered recovery experiments.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when an autonomous agent run needs a plan before execution — decomposing the goal, setting explicit boundaries and stop conditions, and defining what the agent may decide alone versus escalate.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when executing a previously agreed autonomous agent plan — running the steps within declared boundaries, checkpointing progress, and escalating when a stop condition is hit.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Turns a structured-autonomy PR plan (plans/{feature}/plan.md) into copy-paste-ready implementation documentation with complete code for every step, saved as implementation.md. Use after a plan exists and before implementation, when each step needs fully specified changes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill should be used when the user asks to "design multi-agent system", "implement supervisor pattern", "create swarm architecture", "coordinate multiple agents", or mentions multi-agent patterns, context isolation, agent handoffs, sub-agents, or parallel agent execution.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Also use for agentic data engineering pipelines, code generation agents, agent-powered CLI tools, MCP server development, or any system where an AI model calls tools, makes decisions, or loops

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when turning expert materials, execution traces, repository evidence, or long-form documents into an inspectable, correctable, and empirically verified agent skill. Covers source governance, capability-vs-behavior separation, contrastive induction from successes and failures, intervention testing, versioning, rollback, and task-conditioned vs. reusable-library evaluation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Verify supply chain integrity for AI agent plugins, tools, and dependencies. Use this skill when: - Generating SHA-256 integrity manifests for agent plugins or tool packages - Verifying that installed plugins match their published manifests - Detecting tampered, modified, or untracked files in agent tool directories - Auditing dependency pinning and version policies for agent components - Building provenance chains for agent plugin promotion (dev → staging → production) - Any request like "verify plugin integrity", "generate manifest", "check supply chain", or "sign this plugin"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when designing long-horizon agents that need subagents, isolated context windows, filesystem-backed work, persistence, human approval, MCP tools, or production tracing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Bootstraps and runs a multi-agent development team with roles (dev, QA, DevOps), parallel work, and sprint plans that survive context resets. Use when kicking off a project with parallel AI agents, writing brainstorm prompts that produce real debate, planning sprints across chats, or recovering from context overflow mid-sprint.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when improving, evolving, benchmarking, or maintaining agent skills over time. Treats skills as long-lived structured artifacts: execute tasks, diagnose trajectory evidence, accumulate textual momentum, patch at the correct abstraction layer, test per model and harness, canary changes, and roll back regressions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generates a complete AGENTS.md at the repository root following the agents.md format: setup, build, test, style, and PR conventions coding agents need. Use when a repo has no AGENTS.md, when onboarding coding agents to a codebase, or when the existing agent instructions are stale.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when designing retryable or crash-resilient agent workflows, especially when tools can send, charge, publish, provision, or otherwise create duplicate side effects.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.