Support Agent
Un agent support 24 h/24 et 7 j/7 qui répond à partir de votre base de connaissances.
Un agent support disponible 24 h/24 et 7 j/7 qui répond à partir de votre base de connaissances, réserve des rendez-vous et transmet tout sujet délicat à un humain avec le contexte complet.

Installez-la en 3 étapes
Sans inscription. Sans outils en ligne de commande. Choisissez votre IA, copiez une ligne, collez-la.
Vous préférez le faire vous-même ? Achetez la compétence ($4.99), téléchargez le ZIP depuis votre confirmation d'achat, décompressez-le dans le répertoire indiqué ci-dessus, redémarrez votre session IA — même résultat. Vous l'avez déjà achetée ? Votre assistant IA peut la télécharger pour vous.
Preuves concrètes
Des personnes réelles ont accompli exactement ce travail avec des agents IA.
Handle sales and support calls with a voice agent
Lire le cas →Cas d’usageAnswer customer questions and escalate to humans
Lire le cas →Cas d’usageGenerate customer-service agents from a URL that book appointments
Lire le cas →Contenu du pack
Un fichier SKILL.md (le guide pratique) + un modèle de départ. Aperçu :
Support Agent
When to use
- Customers wait hours for answers your FAQ already covers
- Night and weekend calls ring unanswered — and cost you orders
- Appointment requests come in through chat, WhatsApp, or the phone and pile up
- Order-status lookups and payment instructions need instant answers
- Your team can't afford a full support desk yet but the inbox says otherwise
Workflow
- Feed it the business (input: website URL + FAQs + policies → output: knowledge pack)
Start with discovery: products, prices, payment methods, support policies. The fastest route is feeding the agent your website URL and letting it learn the site in about a minute — then fill the gaps by hand (the website never covers everything: refunds, edge cases, your actual policies). Whatever the knowledge base can't teach it, the agent must never guess. - Connect the tools (input: knowledge pack + calendar / order system → output: working agent)
Hook up what the agent needs to act on: the calendar for booking, the order system for status lookups, the CRM for logging. Before touching real channels, test with 3 sample questions plus 1 sample booking and confirm every answer against your real data. If a tool can't be connected, say so in the handoff — don't fake the function. - Open the channels (input: tested agent → output: live on chat / WhatsApp / voice)
Start with low-friction channels: night coverage, FAQs, order queries. Then add WhatsApp or a website widget. Voice comes last: run test calls, approve every script, and set up the recording disclosure where the law requires it. Each new channel gets the same test-then-approve treatment. - Answer, book, escalate (input: customer message → output: answer / booking / handoff)
Answer from the knowledge pack; give payment info when asked and notify you when payment proof arrives — the human confirms the payment, never the agent. When it can't answer — refunds, complaints, legal questions, anything outside the knowledge base — it hands off to a human with the full conversation attached, never a dead "I can't help." - Log every conversation (input: chat/call transcript → output: CRM or ticket entry)
Write structured notes back to the CRM or ticket system: name, contact, issue, outcome. The human who takes over never has to ask "what did the customer already say" twice. - Review and retrain (input: escalation log → output: knowledge-base updates + script fixes)
Spot-check escalated conversations every week. Each failed answer becomes a knowledge-base fix or a new escalation rule. You own the escalation rules and the fallback scripts; the agent follows them.
Output spec
Aperçu du modèle
Support Agent — Setup & Escalation Template
Fill once at setup; review the escalation rules monthly.
Knowledge pack sources
Known gaps (agent must escalate, never guess):
- {e.g. "custom quotes for enterprise"}
- {e.g. "warranty claims beyond 30 days"}
Channels
Voice test calls: [ ] passed [ ] scripts approved [ ] recording disclosure configured
Sample test results (3 questions + 1 booking):
Escalation rules
Fallback script for handoff: > "I've passed your full conversation to {name}; they'll reply within {X hours}. Your ticket ref is #{n}."
Conversation log (per case)
Weekly review checklist
Le pack de compétence lui-même (SKILL.md + modèle) est rédigé en anglais.
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