Retail Agent Setup

Onboarding wizard for retail digital employee agents — guides businesses through a 12-step setup to configure a fully operational AI store assistant. Use whe...

This skill is consistent with a retail onboarding wizard, but take these precautions before installing or running it: - Review the included Python scripts (scripts/parse_*.py, gen_test_cases.py, score_knowledge.py) before execution to ensure they do not transmit data externally or perform unexpected network activity. - The bundle includes scripts/requirements.txt but no install spec — you or your operator must provision a Python runtime and install dependencies (e.g., pandas, pdfplumber) before using the parsing features. Prefer running in a sandboxed environment first. - Do not upload raw customer PII to the knowledge base. The SKILL.md warns about this; follow it: use API connections for PII or anonymize data prior to import. - Expect to provide channel credentials (WeCom, WeChat MP, WhatsApp/BSP, inventory API keys) during setup if you enable those connectors. The skill references env var names and webhook URLs; validate where those credentials will be stored and who can access them. - Verify where agent memory and any temporary files (parsed JSON, OCR outputs) are stored and how long they persist. If your organization has data residency or retention policies, confirm compatibility. - If you plan to enable autonomous model invocation or allow the agent to run tests/configuration unattended, consider a staged rollout: test with non-sensitive sample data, confirm the test report and guardrails behave as expected, and only then run against production data. Overall: the skill is plausibly benign in intent, but the missing install/dependency instructions and the presence of executable parsing scripts mean you should inspect and test the code and runtime environment before trusting it with production data or credentials.

Type: OpenClaw Skill Name: retail-agent-setup Version: 1.0.0 The retail-agent-setup skill bundle is a comprehensive onboarding wizard designed to configure retail digital employees. It utilizes several Python scripts (scripts/parse_products.py, scripts/parse_policy.py, scripts/score_knowledge.py) to process product catalogs and policy documents into structured JSON for the agent's knowledge base. While the setup process involves collecting sensitive API credentials for channel integrations (e.g., WeChat, WeCom), the instructions explicitly mandate using environment variables for storage and lack any evidence of data exfiltration or malicious intent. The scripts and markdown instructions are well-documented, follow platform best practices, and align strictly with the stated goal of retail agent configuration.