Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add khalilbenaz/claude-skills-collection --skill customer-support-agentgit clone --depth 1 https://github.com/khalilbenaz/claude-skills-collectionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/khalilbenaz/claude-skills-collection/customer-support-agent)<a href="https://agentmods.dev/skills/khalilbenaz/claude-skills-collection/customer-support-agent"><img src="https://agentmods.dev/badge/skills/khalilbenaz/claude-skills-collection/customer-support-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/khalilbenaz/claude-skills-collection/customer-support-agent"><img src="https://agentmods.dev/badge/skills/khalilbenaz/claude-skills-collection/customer-support-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00071 | $0.02432 |
| Opus 5 | $0.00036 | $0.01216 |
| Sonnet 5 | $0.00014 | $0.00486 |
| Haiku 4.5 | $0.00007 | $0.00243 |
Grade A, and why
customer-support-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Support Agent
Quand utiliser ce skill
Conçois un agent de support client autonome qui répond aux questions fréquentes via RAG, classe les intentions, gère l'empathie conversationnelle, escalade vers un humain selon des règles explicites, et s'intègre au CRM/ticketing. Applicable à tout secteur à fort volume : SaaS, e-commerce, télécoms, fintech, services.
Stack de référence 2026
| Couche | Options recommandées |
|---|---|
| Orchestration | LangGraph, Rasa Pro, CrewAI |
| RAG | LlamaIndex + pgvector, Weaviate, Pinecone |
| Embeddings | text-embedding-3-small (OpenAI), Cohere embed-v4 |
| LLM réponse rapide | Claude Haiku 3.5 |
| LLM question complexe | Claude Sonnet 4 |
| CRM/Ticketing | Zendesk, Intercom, Freshdesk, HubSpot |
| Canaux | Chat web, Email (Sendgrid), WhatsApp Business, Slack B2B |
Workflow en étapes
1. Définir l'architecture (Jour 1)
Cinq composantes obligatoires :
- RAG : pipeline d'indexation + query sur la knowledge base
- State machine : gestion de l'état de conversation (topic, turns, résolution)
- Classifieur d'intention : sujet + sentiment + urgence
- Moteur d'escalade : règles déterministes + score de confiance
- Connecteur CRM : lecture contexte client + écriture ticket/activité
Choix d'architecture selon le cas d'usage :
| Besoin | Architecture |
|---|---|
| Chat temps réel (< 2 s) | Synchrone, streaming LLM, Haiku en front |
| Email/ticket async | Queue (Redis/SQS) + worker LLM |
| Mix canal | Gateway unifié + state partagé (Redis) |
2. Construire le pipeline RAG
Sources à ingérer : articles d'aide, FAQ, politiques de remboursement, notes de version, guides produit.
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.node_parser import SentenceSplitter
# Chunking sémantique : 800 tokens, overlap 100
parser = SentenceSplitter(chunk_size=800, chunk_overlap=100)
documents = SimpleDirectoryReader("./knowledge_base").load_data()
index = VectorStoreIndex.from_documents(documents, transformations=[parser])
query_engine = index.as_query_engine(
similarity_top_k=5,
response_mode="compact"
)
def retrieve_answer(question: str) -> tuple[str, float]:
response = query_engine.query(question)
score = response.source_nodes[0].score if response.source_nodes else 0.0
return str(response), score
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 225 lines · 71 tokens per session scan A ccff7f88b7a3
customer-support-agent is a skill published in the GitHub repository khalilbenaz/claude-skills-collection (22 stars, last pushed 17d ago), licensed MIT. It adds 71 tokens to every session and 2,432 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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