요구사항 발굴 및 증거 기반 진단을 통해 정당성이 입증된 AI agent 아키텍처를 설계하거나 기존 아키텍처의 결함을 감사합니다.
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agent-architecture skill은 무엇을 하나요?
agent-architecture은(는) agent skill입니다. 요구사항 발굴 및 증거 기반 진단을 통해 정당성이 입증된 AI agent 아키텍처를 설계하거나 기존 아키텍처의 결함을 감사합니다.
skill의 SKILL.md에서 생성된 요약입니다.
agent-architecture skill 정보
이 SKILL은 정당성이 입증된 AI agent 아키텍처를 설계하거나 기존 아키텍처의 결함을 감사하는 기능을 제공합니다. agent의 구조적 기반을 구축해야 하는 개발자 및 아키텍트, 또는 현재 시스템이 왜 실수를 하거나 성능이 저하되는지, 혹은 완료되지 않은 작업을 완료했다고 잘못 보고하는지 진단해야 하는 전문가를 위해 설계되었습니다. 이 프로세스는 구현 코드를 작성하거나 일반적인 코드 리뷰를 수행하는 대신, 아키텍처 결정 및 검증 방법에만 엄격히 집중합니다.
agent가 이 SKILL을 로드하면 신규 agent를 위한 요구사항 발굴 또는 기존 agent 감사를 위한 실제 경로 재구성 등 사용자의 필요에 따라 특정 경로를 따릅니다. 깊이 있는 분석과 포괄적인 범위를 보장하기 위해 discovery-protocol.md 및 evaluation-design.md와 같은 특화된 프로토콜을 활용합니다. 이 SKILL은 고품질의 아키텍처 패키지를 생성하며, 기본적으로 PDF 보고서와 Mermaid 또는 C4 다이어그램을 사용한 결과물 시각화 자료를 포함합니다. 또한 단순성을 우선시하며, 구체적인 필요성이 확인된 경우에만 RAG, 지속성 메모리 또는 멀티 agent와 같은 복잡한 구성 요소를 제안합니다.
agent가 수행할 수 있는 작업
- 1신규 agent 아키텍처 설계초기 요구사항이 명확하지 않은 상태에서 신규 agent를 위한 정당성 있는 아키텍처 계획을 수립합니다.
- 2기존 agent 감사이미 작성된 agent를 검토하여 아키텍처 결함을 찾아냅니다.
- 3agent 성능 문제 진단agent가 왜 실수를 하는지, 또는 작업이 완료되지 않았음에도 완료되었다고 보고하는지 원인을 파악합니다.
- 4아키텍처 문서 생성아키텍처 프로젝트를 위한 다이어그램과 결정 로그가 포함된 공식 PDF 보고서를 생성합니다.
skill의 SKILL.md를 기반으로 작성되었습니다. 모든 사용 사례는 해당 문서의 인용구와 대조 확인되었습니다.
agent-architecture skill 설치 방법
각 명령은 skill 폴더를 클라이언트의 skills 디렉터리에 복사합니다. 클라이언트를 재시작하거나 새 세션을 시작하여 로드하세요.Claude Code에 agent-architecture skill 설치
~/.claude/skills/agent-architecture/SKILL.md
$ npx degit github/awesome-copilot/skills/agent-architecture ~/.claude/skills/agent-architectureClaude Code (project)에 agent-architecture skill 설치
.claude/skills/agent-architecture/SKILL.md
$ npx degit github/awesome-copilot/skills/agent-architecture .claude/skills/agent-architectureCodex에 agent-architecture skill 설치
~/.agents/skills/agent-architecture/SKILL.md
$ npx degit github/awesome-copilot/skills/agent-architecture ~/.agents/skills/agent-architectureGemini CLI에 agent-architecture skill 설치
~/.gemini/skills/agent-architecture/SKILL.md
$ npx degit github/awesome-copilot/skills/agent-architecture ~/.gemini/skills/agent-architectureClaude.ai에 agent-architecture skill 설치
npx degit github/awesome-copilot/skills/agent-architecture agent-architecture && zip -r agent-architecture.zip agent-architecture # claude.ai → Customize → Skills → + → Create skill → Upload a skill → agent-architecture.zip
자동 검사
2026년 11월 15일에 실행됨전체 보고서 →자동 검사를 통과했습니다. 보안 감사는 아닙니다. 에이전트가 실행하기 전에 스크립트를 읽어 보세요. 검사 방식 · 점수를 높이기 위해 수정할 항목
- 소스 저장소 연결 및 접근 가능github/awesome-copilot/skills/agent-architecture is public and reachablegithub.com/github/awesome-copilot/tree/HEAD/skills/agent-arc ↗통과
- 오픈 소스 라이선스 (OSI)MITspdx.org/licenses/MIT.html ↗통과
- 최근 1년 내 커밋 또는 릴리스Last commit 2026-11-11github.com/github/awesome-copilot/commits ↗통과
- 스크립트에 다운로드 후 실행이나 자격 증명 탈취가 없음No scripts in the skill folder통과
- SKILL.md에 숨겨진 지침 없음No override phrases, safeguard switches, credential requests or hidden charactersgithub.com/github/awesome-copilot/blob/HEAD/skills/agent-arc ↗통과
- 사전 허용 도구가 제한됨No pre-approved tools; the agent asks before using onegithub.com/github/awesome-copilot/blob/HEAD/skills/agent-arc ↗통과
- SKILL.md가 Agent Skills 형식을 따름the description does not say when to use the skillgithub.com/github/awesome-copilot/blob/HEAD/skills/agent-arc ↗결함 발견
SKILL.md
GitHub에서 보기AI Agent Architecture
Help the user obtain a justified architecture for their task or an evidence-based audit of an existing agent. Deliver architectural decisions and ways to verify them, without implementing the agent. By default, completed work includes a PDF report and a visualization of the results. An “ideal architecture” fits the requirements, cost of failure, and team resources; it does not maximize the number of components.
Choose a route
| Request | Route | Read |
|---|---|---|
| New agent, requirements are not yet clear | Design: working cases → early design → requirements and decision coverage → delivery | design.md, architecture-contract.md |
| Architecture from an existing specification | Design: fill in what is known and clarify only gaps | The same files; do not restart the interview |
| Review an agent already written | Audit: reconstruct actual paths → verify → deliver findings | audit.md, and architecture-contract.md as criteria |
| Agent makes mistakes, has degraded, or falsely reports “done” | Diagnosis within the audit: case → hypotheses → discriminating checks → correction and closure criterion | audit.md and diagnostic-review.md |
| Review and redesign | Audit first; its demonstrated problems become design inputs | audit.md first, then design.md |
In either mode, read source-map.md once: it explains the origins of the principles and the textbook's limitations. The original PDF is not needed for ordinary skill use. scenarios.md is needed only to test the skill itself.
When choosing or revisiting the execution approach, use architecture-selection.md; when designing acceptance or reviewing quality claims, use evaluation-design.md. Develop the validation loop and completion evidence using validation-loop.md; for long-running/background work, pauses, recovery, and competing sessions, use execution-continuity.md, including storage, RTO/RPO, budgets, the human decision queue, and scheduling. Develop delegation, mutable memory, execution isolation, and long-running/streaming interaction only when the task has these properties. A section's existence does not make its question mandatory: material gaps under discovery-protocol.md determine depth.
Shared decision rules
- First read the available specification, local instructions, architectural decisions, and relevant materials. Use code to reconstruct architecture, not to make unsolicited fixes. Do not run an application with external effects for an audit.
- Maintain a brief register: source-confirmed / user requirement / proposal / assumption / open question / not applicable. Identify where requirements came from. A user decision and an architect's hypothesis have different statuses.
- Corporate contracts and accepted decisions apply only within their own project. The textbook is an engineering reference, not a source of authority or a replacement for local canon. Identify conflicts rather than resolving them silently.
- First consider ordinary automation without an LLM, a single call, and a predefined workflow. Introduce an agent loop, RAG, persistent memory, MCP, or multiple agents only for a concrete need. For each added complexity, identify its benefit, cost, verification method, and simpler alternative.
- Do not select a model or framework before understanding the task. For a concrete selection, check current official documentation and version constraints. A documented capability is not yet demonstrated quality on the user's data.
- Separate probabilistic model decisions from programmatically enforced rules. Describe where permissions, parameters, budget, and action admissibility are checked before an external effect, including bypass paths and resumption.
- For a timed-out external write, a readback that finds nothing does not by itself prove that no effect occurred. Permit a retry only under an established downstream idempotency contract or authoritative proof of non-execution; otherwise retain
effect unknownand reconcile or escalate. Apply this rule in concrete flows and examples as well as in the risk section. - An audit or design does not authorize writing code, changing agent settings, publishing, or initiating external actions. On a subsequent explicit implementation request, hand the architecture to the appropriate process; this skill does not continue into implementation itself.
How to work
Before an interview or audit planning, read discovery-protocol.md. Show a clear route and maintain a coverage map. By default, devote each turn to one decision or working episode; do not hide several independent topics inside one question. Material gaps and evidence determine depth. There is no fixed total round limit.
Deliver the first useful design as soon as context is sufficient, otherwise no later than the third answer; the count does not reset on continuation. This limits the wait for an early result, not the completeness of the interview. If the task is too unclear, show a map of what is understood and conditional options. After the sketch, continue investigating material gaps under the protocol; two or three rounds alone do not justify declaring readiness.
The first design includes the goal and boundaries, main capabilities and their outputs, recommended components, main flow and external actions, key constraints, assumptions, and open decisions. It is a sketch for early feedback. The interview budget limits the wait for a sketch, not design depth: develop it into an architecture package from what is already known, without waiting for a separate instruction to elaborate. If context suffices, deliver the package immediately. If the user explicitly asks only for a sketch, respect and label that depth.
Phrases such as “that's enough,” “let's go with this for now,” “the rest later,” or “enough questions” end requirements gathering: deliver the architecture from accumulated context in the same answer. Do not require a separate “now design it” instruction or end at “interview complete.” If a design has already been delivered, show its current final version or a substantive update. An explicit request to stop all work (“don't continue,” “that's all for today, stop”) means stop, rather than deliver a new design.
If the user does not know an answer, propose a justified option and label its status. Represent unknowns as assumptions and open decisions. Unclear authority blocks the corresponding external action in the proposed architecture, but not delivery of the architecture itself. Silence and ending the interview do not approve proposals.
After a significant answer, update the working summary of requirements and decisions. Save it in an agreed document if artifact creation is within the request; otherwise maintain it in the conversation. On continuation, start with that summary and changed information.
After the first design, clarify specific branches and uncovered material requirements, including real exceptions, human work, and feasibility. Explain which decision the answer will change; propose internal mechanisms yourself. Do not confine gap discovery to components already drawn or restart a questionnaire. Finish when the declared scope has sufficient coverage; if further confirmation is unavailable, deliver a conditional package with owners and checks for gaps.
Complete design with the architecture package from architecture-contract.md: domain capabilities and methods, output contracts, the structure of instructions/skills/materials, allocation between the existing platform and additions, a populated end-to-end example, and checks. Read capability-design.md for this part; in an audit, use it to check required capabilities. Describe the agent's main work deeply enough that a developer does not have to invent its method again. A platform name and a list of stages do not accomplish that.
Always cover limits on iterations, time, tokens/money, and tool calls, stopping rules, and what the user receives on stopping. Mark unknown values as open or proposed rather than inventing an agreed limit. An architecture package with skill specifications remains a design: it does not imply skill installation, code implementation, or verification of a running agent.
Complete an audit with demonstrated problems, separately identifying unknowns and accepted tradeoffs. Do not claim production readiness from reading code. Architectural readiness for implementation and demonstrated operational quality are different outcomes.
Final artifacts
When completing design, audit, or diagnosis, read result-delivery.md and create a PDF of the results with a rendered Mermaid or C4 diagram as appropriate; retain editable text and diagram source. Do this as part of completion without a separate user request to “make the PDF now.” An early sketch and intermediate answers do not require repeated export. Explicit user constraints (“chat only,” “no files/PDF”) and a request to stop all work take precedence. Creating the report does not authorize implementing or changing the reviewed agent.
Package metadata
This package is distributed under the MIT license. Optional client metadata supports compatible Agent Skills clients; Copilot uses SKILL.md and the linked references.
이 스킬의 파일
16agent-architecture skill 질문
Claude Code에 agent-architecture skill을 어떻게 설치하나요?
npx degit github/awesome-copilot/skills/agent-architecture ~/.claude/skills/agent-architecture를 실행합니다. Claude Code는 ~/.claude/skills(모든 프로젝트) 또는 .claude/skills(한 프로젝트)에서 skills를 로드하며, 설명이 작업과 일치할 때 skill을 사용합니다.
agent-architecture skill을 사용할 수 있는 클라이언트는 무엇인가요?
Agent Skills 형식을 읽는 모든 클라이언트(Claude Code, Codex, Gemini CLI, claude.ai(ZIP으로 업로드))가 사용할 수 있습니다. skill 내 스크립트는 클라이언트가 코드 실행을 허용하는 경우에만 실행됩니다.
이 SKILL에 agent 코드를 작성하는 기능이 포함되어 있나요?
아니요, 이 SKILL은 아키텍처 설계에 집중하며 구현 및 일반적인 코드 리뷰는 제외합니다.
설계 요청 시 어떤 결과물을 받을 수 있나요?
기본적으로 PDF 보고서와 Mermaid 또는 C4 다이어그램과 같은 결과물 시각화 자료가 포함됩니다.
도구가 특정 LLM이나 프레임워크를 대신 선택해 주나요?
이 SKILL은 작업을 완전히 이해하기 전에 모델이나 프레임워크를 선택하는 것을 지양하며, 구체적인 선택을 위해서는 공식 문서를 확인할 것을 강조합니다.