Concevez des architectures d'agents IA justifiées ou auditez les architectures existantes pour identifier des failles grâce à la découverte de besoins et un diagnostic basé sur des preuves.
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- 16
- Dernier commit
- 11 nov. 2026
- Licence
- MIT
- Outils autorisés
- —
Que fait la skill agent-architecture ?
agent-architecture est une agent skill. Concevez des architectures d'agents IA justifiées ou auditez les architectures existantes pour identifier des failles grâce à la découverte de besoins et un diagnostic basé sur des preuves.
Résumé généré à partir du SKILL.md de la skill.
À propos du skill agent-architecture
Cette SKILL permet de concevoir des architectures d'agents IA justifiées ou d'auditer les architectures existantes pour y déceler des failles. Elle s'adresse aux développeurs et aux architectes qui doivent établir une base structurelle pour un agent ou diagnostiquer les raisons pour lesquelles un système actuel commet des erreurs, se dégrade ou signale une complétion erronée. Le processus se concentre exclusivement sur les décisions architecturales et les méthodes de vérification, plutôt que sur l'écriture de code d'implémentation ou la réalisation de revues de code générales.
Lorsqu'un agent charge cette SKILL, il suit des parcours spécifiques selon les besoins de l'utilisateur, comme la découverte de besoins pour de nouveaux agents ou la reconstruction de chemins réels pour l'audit d'agents existants. Elle utilise des protocoles spécialisés tels que discovery-protocol.md et evaluation-design.md pour garantir la profondeur et l'exhaustivité de l'analyse. La SKILL produit des packages architecturaux de haute qualité qui incluent, par défaut, un rapport PDF et une visualisation des résultats via des diagrammes Mermaid ou C4. Elle privilégie la simplicité, ne suggérant des composants complexes comme le RAG, la mémoire persistante ou des systèmes multi-agents que lorsqu'un besoin concret est identifié.
Ce qu'un agent peut faire avec
- 1Concevoir l'architecture d'un nouvel agentCréez un plan architectural justifié pour un nouvel agent lorsque les besoins initiaux ne sont pas encore clairement définis.
- 2Auditer un agent existantAnalysez un agent déjà développé pour identifier d'éventuelles failles architecturales.
- 3Diagnostiquer des problèmes de performance d'un agentIdentifiez pourquoi un agent commet des erreurs ou déclare qu'une tâche est terminée alors qu'elle ne l'est pas.
- 4Générer de la documentation architecturaleProduisez un rapport PDF formel contenant des diagrammes et des journaux de décision pour un projet d'architecture.
Rédigé à partir du SKILL.md du skill. Chaque cas d'utilisation est vérifié par rapport à une citation de celui-ci.
Comment installer la skill agent-architecture
Chaque commande copie le dossier de la skill dans le répertoire des skills du client. Redémarrez le client ou démarrez une nouvelle session pour la charger.Installer la skill agent-architecture dans Claude Code
~/.claude/skills/agent-architecture/SKILL.md
$ npx degit github/awesome-copilot/skills/agent-architecture ~/.claude/skills/agent-architectureInstaller la skill agent-architecture dans Claude Code (project)
.claude/skills/agent-architecture/SKILL.md
$ npx degit github/awesome-copilot/skills/agent-architecture .claude/skills/agent-architectureInstaller la skill agent-architecture dans Codex
~/.agents/skills/agent-architecture/SKILL.md
$ npx degit github/awesome-copilot/skills/agent-architecture ~/.agents/skills/agent-architectureInstaller la skill agent-architecture dans Gemini CLI
~/.gemini/skills/agent-architecture/SKILL.md
$ npx degit github/awesome-copilot/skills/agent-architecture ~/.gemini/skills/agent-architectureInstaller la skill agent-architecture dans Claude.ai
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
Vérifications automatisées
Exécuté le 15 nov. 2026Rapport complet →A réussi nos vérifications automatiques. Ce n'est pas un audit de sécurité : lisez les scripts avant de laisser un agent les exécuter. Fonctionnement des vérifications · Ce qu'il faut corriger pour gagner des points
- Dépôt source lié et accessiblegithub/awesome-copilot/skills/agent-architecture is public and reachablegithub.com/github/awesome-copilot/tree/HEAD/skills/agent-arc ↗Réussi
- Licence open source (OSI)MITspdx.org/licenses/MIT.html ↗Réussi
- Commit ou version publiée au cours de l'année écouléeLast commit 2026-11-11github.com/github/awesome-copilot/commits ↗Réussi
- Aucun téléchargement-exécution ni vol d'identifiants dans les scriptsNo scripts in the skill folderRéussi
- Aucune instruction cachée dans SKILL.mdNo override phrases, safeguard switches, credential requests or hidden charactersgithub.com/github/awesome-copilot/blob/HEAD/skills/agent-arc ↗Réussi
- Les outils préautorisés sont limitésNo pre-approved tools; the agent asks before using onegithub.com/github/awesome-copilot/blob/HEAD/skills/agent-arc ↗Réussi
- SKILL.md respecte le format Agent Skillsthe description does not say when to use the skillgithub.com/github/awesome-copilot/blob/HEAD/skills/agent-arc ↗Faille
SKILL.md
Voir sur GitHubAI 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.
Fichiers de cette skill
16Questions sur la skill agent-architecture
Comment installer la skill agent-architecture dans Claude Code ?
Exécutez npx degit github/awesome-copilot/skills/agent-architecture ~/.claude/skills/agent-architecture. Claude Code charge les skills depuis ~/.claude/skills (tous les projets) ou .claude/skills (un seul projet) et utilise une skill lorsque sa description correspond à la tâche.
Quels clients peuvent utiliser la skill agent-architecture ?
Tout client qui lit le format Agent Skills : Claude Code, Codex, Gemini CLI et claude.ai (envoi sous forme de ZIP). Les scripts de la skill ne s'exécutent que si le client autorise l'exécution de code.
Cette SKILL inclut-elle l'écriture de code pour l'agent ?
Non, cette SKILL est strictement dédiée à l'architecture et exclut l'implémentation ainsi que la revue de code générale.
À quels types de livrables puis-je m'attendre lors d'une demande de conception ?
Par défaut, le travail terminé comprend un rapport PDF et une visualisation des résultats, telle qu'un diagramme Mermaid ou C4.
L'outil choisira-t-il un LLM ou un framework spécifique pour moi ?
La SKILL déconseille de choisir un modèle ou un framework avant d'avoir compris la tâche, et recommande de consulter la documentation officielle pour tout choix concret.