Entwirf fundierte KI-Agenten-Architekturen oder auditiere bestehende Systeme auf Schwachstellen durch Anforderungsanalyse und evidenzbasierte Diagnose.
- GitHub Stars
- 41.038
- Dateien
- 16
- Letzter Commit
- 11. Nov. 2026
- Lizenz
- MIT
- Erlaubte Tools
- —
Was macht der agent-architecture Skill?
agent-architecture ist ein Agent Skill. Entwirf fundierte KI-Agenten-Architekturen oder auditiere bestehende Systeme auf Schwachstellen durch Anforderungsanalyse und evidenzbasierte Diagnose.
Zusammenfassung erzeugt aus der SKILL.md des Skills.
Über den agent-architecture skill
Dieser Skill ermöglicht es dir, fundierte KI-Agenten-Architekturen zu entwerfen oder bestehende Architekturen auf Fehler zu prüfen. Er richtet sich an Entwickler und Architekten, die ein strukturelles Fundament für einen Agenten aufbauen müssen oder diagnostizieren wollen, warum ein aktuelles System Fehler macht, an Leistung verliert oder den Abschluss einer Aufgabe fälschlicherweise meldet. Der Prozess konzentriert sich strikt auf architektonische Entscheidungen und Verifizierungsmethoden, anstatt Implementierungscode zu schreiben oder allgemeine Code-Reviews durchzuführen.
Wenn ein Agent diesen Skill lädt, folgt er spezifischen Routen basierend auf den Nutzerbedürfnissen, wie etwa der Anforderungsanalyse für neue Agenten oder der Rekonstruktion tatsächlicher Pfade für Audits bestehender Systeme. Er nutzt spezialisierte Protokolle wie discovery-protocol.md und evaluation-design.md, um Tiefe und Vollständigkeit zu gewährleisten. Der Skill liefert hochwertige Architektur-Pakete, die standardmäßig einen PDF-Bericht und eine Visualisierung der Ergebnisse mittels Mermaid- oder C4-Diagrammen enthalten. Dabei wird Wert auf Einfachheit gelegt; komplexe Komponenten wie RAG, persistenter Speicher oder Multi-Agenten-Systeme werden nur vorgeschlagen, wenn ein konkreter Bedarf identifiziert wurde.
Was ein agent damit machen kann
- 1Entwurf einer neuen Agenten-ArchitekturErstelle einen fundierten Architekturplan für einen neuen Agenten, wenn die ursprünglichen Anforderungen noch nicht klar definiert sind.
- 2Audit eines bestehenden AgentenÜberprüfe einen bereits implementierten Agenten, um architektonische Schwachstellen zu finden.
- 3Diagnose von Performance-ProblemenIdentifiziere, warum ein Agent Fehler macht oder meldet, dass eine Aufgabe erledigt ist, obwohl dies nicht der Fall ist.
- 4Erstellung von Architektur-DokumentationErzeuge einen formellen PDF-Bericht, der Diagramme und Decision Logs für ein Architekturprojekt enthält.
Geschrieben basierend auf dem SKILL.md des skills. Jeder use case wurde gegen ein zitat daraus geprüft.
So installierst du den agent-architecture Skill
Jeder Befehl kopiert den Skill-Ordner in das Skills-Verzeichnis des Clients. Starte den Client neu oder beginne eine neue Session, um ihn zu laden.Den agent-architecture Skill in Claude Code installieren
~/.claude/skills/agent-architecture/SKILL.md
$ npx degit github/awesome-copilot/skills/agent-architecture ~/.claude/skills/agent-architectureDen agent-architecture Skill in Claude Code (project) installieren
.claude/skills/agent-architecture/SKILL.md
$ npx degit github/awesome-copilot/skills/agent-architecture .claude/skills/agent-architectureDen agent-architecture Skill in Codex installieren
~/.agents/skills/agent-architecture/SKILL.md
$ npx degit github/awesome-copilot/skills/agent-architecture ~/.agents/skills/agent-architectureDen agent-architecture Skill in Gemini CLI installieren
~/.gemini/skills/agent-architecture/SKILL.md
$ npx degit github/awesome-copilot/skills/agent-architecture ~/.gemini/skills/agent-architectureDen agent-architecture Skill in Claude.ai installieren
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
Automatisierte Checks
Geluften am 15. Nov. 2026Vollständiger Bericht →Hat unsere automatischen Prüfungen bestanden. Das ist kein Sicherheitsaudit: Lesen Sie die Skripte, bevor ein Agent sie ausführt. Wie die Checks funktionieren · Was du für mehr Punkte verbessern kannst
- Source Repository verknüpft und erreichbargithub/awesome-copilot/skills/agent-architecture is public and reachablegithub.com/github/awesome-copilot/tree/HEAD/skills/agent-arc ↗Bestanden
- Open Source Lizenz (OSI)MITspdx.org/licenses/MIT.html ↗Bestanden
- Commit oder Release im letzten JahrLast commit 2026-11-11github.com/github/awesome-copilot/commits ↗Bestanden
- Keine Download-und-Ausführen-Muster oder Zugangsdatendiebstahl in SkriptenNo scripts in the skill folderBestanden
- Keine versteckten Anweisungen in SKILL.mdNo override phrases, safeguard switches, credential requests or hidden charactersgithub.com/github/awesome-copilot/blob/HEAD/skills/agent-arc ↗Bestanden
- Vorab erlaubte Tools sind eingeschränktNo pre-approved tools; the agent asks before using onegithub.com/github/awesome-copilot/blob/HEAD/skills/agent-arc ↗Bestanden
- SKILL.md folgt dem Agent-Skills-Formatthe description does not say when to use the skillgithub.com/github/awesome-copilot/blob/HEAD/skills/agent-arc ↗Befund
SKILL.md
Auf GitHub ansehenAI 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.
Dateien in diesem Skill
16agent-architecture Skill-Fragen
Wie installiere ich den agent-architecture Skill in Claude Code?
Führe npx degit github/awesome-copilot/skills/agent-architecture ~/.claude/skills/agent-architecture aus. Claude Code lädt Skills aus ~/.claude/skills (alle Projekte) oder .claude/skills (ein Projekt) und nutzt einen Skill, wenn seine Beschreibung zur Aufgabe passt.
Welche Clients können den agent-architecture Skill nutzen?
Jeder Client, der das Agent Skills-Format liest: Claude Code, Codex, Gemini CLI und claude.ai (als ZIP hochladen). Skripte im Skill laufen nur dort, wo der Client Codeausführung erlaubt.
Beinhaltet dieser Skill auch das Schreiben von Code für den Agenten?
Nein, der Skill konzentriert sich rein auf die Architektur und schließt die Implementierung sowie allgemeine Code-Reviews aus.
Welche Ergebnisse kann ich bei einer Design-Anfrage erwarten?
Standardmäßig umfasst das Ergebnis einen PDF-Bericht und eine Visualisierung der Resultate, wie zum Beispiel ein Mermaid- oder C4-Diagramm.
Wird das Tool ein spezifisches LLM oder Framework für mich auswählen?
Der Skill rät davon ab, ein Modell oder Framework auszuwählen, bevor die Aufgabe verstanden wurde, und betont, dass für konkrete Entscheidungen die offizielle Dokumentation geprüft werden sollte.