Checks return eligibility and routes outcomes
The source instruction checks policy eligibility, distinguishes final-sale and item-condition cases, then routes the request to refund, store credit, exchange, or escalation.
Vendor Management is an ecommerce AI skill for Alireza Rezvani, built for teams working with Codex, Claude Code, OpenClaw. Use it to prioritize stocking,…
You need to prioritize stocking, replenishment, or purchasing decisions. Validate one concrete path first: Quarterly vendor scorecard preparation for leadership with KEEP/REVIEW/REPLACE…; Tier-1 vendor incident review quantifying SLA gaps with credit-claim eligibility. Once it… Start with a small test around “Quarterly vendor scorecard preparation for leadership with KEEP/REVIEW/REPLACE verdicts”, then check whether “Tier-1 vendor incident review quantifying SLA gaps with credit-claim eligibility” fits the way your team actually works.
The source instruction checks policy eligibility, distinguishes final-sale and item-condition cases, then routes the request to refund, store credit, exchange, or escalation.
The source asks the model to group, classify, answer, or prioritize review feedback rather than treating every comment as an isolated case.
The source asks for analysis, classification, ranking, or scoring before it reaches a conclusion or next action.
Provide the current eligibility window, final-sale rules, condition rules, and the approved refund, exchange, or store-credit outcomes. The source instruction depends on these rules.
This instruction refers to file or tabular input. Prepare the requested file and confirm that the model you use can read it.
The source includes a Python command or script. A compatible local Python environment is required for that part of the workflow.
The complete source is shown below. Copy it from the top right to use it.
You are a BizOps/VMO operator. Provide vendor catalog JSON with name, category, annual_spend, criticality (tier-1/2/3), uptime_pct, support_response_hours_p90, incident_count, security_certs, renewal_terms. Run vendor_scorer.py --profile <saas|fintech|healthcare|enterprise> for 0-100 scoring. Verdict: KEEP ≥75, REVIEW 50-74, REPLACE <50. Run sla_compliance_tracker.py for compliance %, trend, credit-claim eligibility (breach_count ≥2 OR actual < target by >0.5pp). Run vendor_risk_classifier.py across data sensitivity, financial exposure, operational dependency, and regulatory exposure. Synthesize: top 3 KEEP, REVIEW, REPLACE; claimable credits; Critical-risk vendors without mitigation.Starter prompts for the main use cases—copy and use them directly.
Do not begin with a store-wide rollout. Pick one reversible task where Vendor Management can help you plan inventory, purchasing, capacity, suppliers, and replenishment. Use this when the input boundary, owner, and one primary measure from forecast error, stockout rate, excess stock, cash tied up, and service level are written down.
Use the Skill above to help me with this task: Start with one real task.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Do not begin with a store-wide rollout. Pick one reversible task where Vendor Management can help you plan inventory, purchasing, capacity, suppliers, and replenishment.
Return a practical result and clearly flag anything that needs human review.[TASK_DETAILS][CONSTRAINTS]Collect only the clean SKU history, lead times, current stock, purchase constraints, and margin assumptions needed for this test. Remove unrelated personal data and state which actions must never run automatically. Use this when every input has a known source, sensitive fields are minimized, and the approver knows what the trial can read or change.
Use the Skill above to help me with this task: Prepare the input and guardrails.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Collect only the clean SKU history, lead times, current stock, purchase constraints, and margin assumptions needed for this test. Remove unrelated personal data and state which actions must never run automatically.
Return a practical result and clearly flag anything that needs human review.[TASK_DETAILS][CONSTRAINTS]Read the source, installation method, and permission notes before adding Vendor Management to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a inventory or purchasing recommendation that a responsible operator can inspect, and it stayed inside the approved boundary.
Use the Skill above to help me with this task: Inspect the source Skill, then run it.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Read the source, installation method, and permission notes before adding Vendor Management to a separate test project. Keep commands and Skill text exactly as published.
Return a practical result and clearly flag anything that needs human review.[TASK_DETAILS][CONSTRAINTS]Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time. Use this when forecast error, stockout rate, excess stock, cash tied up, and service level has a pre-test baseline, and errors and exceptions are logged separately.
Use the Skill above to help me with this task: Review it against a baseline.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time.
Return a practical result and clearly flag anything that needs human review.[TASK_DETAILS][CONSTRAINTS]The instruction includes example eligibility and resolution paths. Replace its time window, final-sale handling, refund, exchange, and store-credit rules with the policy that is currently approved for your store.
When this instruction drafts marketing or product content, review specifications, comparisons, performance claims, and testimonials against approved evidence before publishing.
The instruction can group and prioritize feedback, but repeated wording is not proof of a product defect or customer-wide preference. Check the underlying sample before changing a product, policy, or campaign.
HealthTech CTO and open-source maintainer focused on applied AI, agentic coding, and practical skills for product, research, growth, and operations teams.
Review third-party permission scopes before providing store data. Never paste payment credentials, customer passwords, or unnecessary personal data into a model. Outputs must be checked by the operator responsible for the workflow.
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