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/gentest_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/gentest_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:

CODEBLOCK0

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.