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- retail-agent-setup
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Retail Agent Setup — Onboarding Wizard
Overview
This skill transforms a blank OpenClaw agent into a fully configured retail digital employee tailored to a specific store or chain. Each step produces a concrete artifact that persists in the agent's memory, making the setup cumulative and resumable.
Setup takes 20–40 minutes end-to-end. Each step can be paused and resumed. Run retail agent setup or 数字员工配置 to start or continue.
Execution Protocol
- Run steps in order — each step depends on outputs from the previous
- Pause after each step — show the artifact, ask "Confirm and continue?" before proceeding
- Resumable — if a step was previously completed, show its saved output and ask whether to redo or skip
- Save state — write each step's output to agent memory before moving to the next
- Zero-config entry — if the user just says "set up my retail agent," start at Step 1
The 12 Steps
Step 01 — System Inventory
"What retail systems are you currently using?"
Identify the store's existing tech stack across 5 categories: POS, ERP/WMS, CRM/membership, e-commerce platforms, and supply chain tools.
Map each system to its API availability (real-time / batch / none). Reference: step-01-systems.md Artifact: System inventory card + API availability matrix
Step 02 — Data Infrastructure Assessment
"Where does your data live, and what format is it in?"
Evaluate data across 6 dimensions: products, inventory, sales, staff, customers, and policy docs. Score completeness and freshness. Prioritize what to connect first. Reference: step-02-data-infra.md Artifact: Data map + connection priority list
Step 03 — Data Import & Auto-Structuring
"Send me your data — I'll organize it into a format the agent can use."
Accept uploads (Excel/CSV/PDF/Word/image), API connections, or pasted text. Auto-parse into structured knowledge base entries. Flag gaps and prompt to fill them. Script: scripts/parse_products.py — Excel/CSV → structured JSON Script: scripts/parse_policy.py — PDF/Word → rule tree Script: scripts/score_knowledge.py — completeness scoring Reference: step-03-data-import.md Artifact: Structured knowledge base + completeness score (0–100)
Step 04 — Role Selection
"What role should this digital employee play?"
Choose from 6 preset roles or define a custom role. Each role activates a specific skill bundle and response style. One agent = one primary role (multi-role is advanced config). Reference: step-04-role-select.md Artifact: Role definition file + activated skill bundle list
Step 05 — Skills Configuration
"Which capabilities should this agent have?"
Review recommended skills for the chosen role. Toggle on/off. Configure each enabled skill (thresholds, data sources, escalation rules). Reference: step-05-skills-config.md Artifact: skills-config.json — active skills with their parameters
Step 06 — Knowledge Base Validation
"Let me test what your agent knows."
Auto-generate 10 test questions covering products, inventory, policies, and recommendations. Run them against the knowledge base. Flag failures. Guide the user to fill gaps. Script: scripts/gen_test_cases.py — generate test questions by vertical Script: scripts/score_knowledge.py — run and score responses Reference: step-06-knowledge.md Artifact: Knowledge base score + gap report
Step 07 — Digital Employee Persona
"Give your digital employee a name and personality."
Configure: name, personality type, tone, reply style, customer address form, brand keywords. Generate 3 sample dialogues for preview. Confirm before saving. Reference: step-07-persona.md Artifact: persona-config.json + 3 preview dialogues
Step 08 — Channel Integration
"How will staff and customers reach this agent?"
Select and configure delivery channels: WeCom (企业微信), WeChat MP/Mini Program, Lark (飞书), Web kiosk UI, WhatsApp, or SMS/IVR. Each channel has a dedicated setup guide with step-by-step auth instructions. Reference: step-08-channels.md Artifact: Channel connection status + test message confirmation
Step 09 — Permissions & Escalation
"What can the agent decide alone, and what needs a human?"
Define 4-level permission matrix: L0 auto-handle, L1 suggest+confirm, L2 submit for approval, L3 force escalate to human. Set escalation targets and on-call schedules. Reference: step-09-permissions.md Artifact: permissions-matrix.json + escalation routing config
Step 10 — Pre-Launch Testing
"Let's run real-scenario tests before going live."
Run a full scenario test suite based on the store's vertical and configured skills. Score readiness 0–100. Must reach 80+ to proceed to launch. Script: scripts/gen_test_cases.py Reference: step-10-test.md Artifact: Test report + launch-readiness score
Step 11 — Launch & Handoff
"You're ready. Let's go live."
Activate the agent on all configured channels. Generate staff onboarding card (one-pager). Send welcome message. Schedule first check-in reminder (7 days out). Reference: step-11-handoff.md Artifact: Staff guide PDF + activation confirmation
Step 12 — Continuous Improvement
"Going live is the beginning, not the end."
Set up weekly unanswered-question digests and monthly usage reports. Configure knowledge-gap alerts. Schedule quarterly persona review. Reference: step-12-iterate.md Artifact: Cron jobs for digest + alert thresholds set
State Management
Track onboarding progress in agent memory under key retail_setup_state:
{
"version": "1.0",
"started_at": "<ISO timestamp>",
"completed_steps": [1, 2, 3],
"current_step": 4,
"artifacts": {
"systems": { ... },
"data_map": { ... },
"knowledge_base": { ... },
"role": "...",
"skills_config": { ... },
"persona": { ... },
"channels": [ ... ],
"permissions": { ... }
}
}On any new message, check this state first. If setup is incomplete, offer to resume.
Supported Retail Verticals
Apparel · Footwear · Beauty & Skincare · Consumer Electronics · Home & Furniture · Maternal & Infant · Convenience Store · Supermarket · Specialty Food · Jewelry · Sporting Goods · Books & Stationery · Pet Supplies · Pharmacy · Toy & Hobby
For verticals not listed, use "General Retail" defaults and customize in Step 4.