Skill: fangwei-frank/retail-agent-setup (version 1.0.0)

--- name: retail-agent-setup description: > Onboarding wizard for retail digital employee agents — guides businesses through a 12-step setup to configure a fully operational AI store assistant. Use when a retail business wants to set up, configure, or customize their digital employee agent. Triggers on: retail agent setup, digital employee setup, 数字员工, 零售助手配置, retail onboarding, store assistant setup, configure retail agent, 线下零售配置, set up my store AI, retail AI employee. metadata: openclaw: emoji: 🏪 --- # 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](references/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](references/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](scripts/parse_products.py) — Excel/CSV → structured JSON **Script:** [scripts/parse_policy.py](scripts/parse_policy.py) — PDF/Word → rule tree **Script:** [scripts/score_knowledge.py](scripts/score_knowledge.py) — completeness scoring **Reference:** [step-03-data-import.md](references/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](references/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](references/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](scripts/gen_test_cases.py) — generate test questions by vertical **Script:** [scripts/score_knowledge.py](scripts/score_knowledge.py) — run and score responses **Reference:** [step-06-knowledge.md](references/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](references/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](references/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](references/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](scripts/gen_test_cases.py) **Reference:** [step-10-test.md](references/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](references/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](references/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`: ```json { "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.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)