Seasonal Inventory Planner
PassAudited by VirusTotal on May 7, 2026.
Overview
Type: OpenClaw Skill Name: seasonal-inventory-planner Version: 1.1.0 The Seasonal Inventory Planner skill bundle is a well-structured set of instructions and reference materials for an AI agent to perform inventory management tasks. The content is entirely focused on business logic, including demand forecasting, seasonal decomposition, and markdown strategies. There is no executable code, no evidence of data exfiltration, and no malicious prompt injection attempts within the SKILL.md or supporting documentation (e.g., references/seasonal-decomposition.md). The bundle is aligned with its stated purpose and lacks any high-risk or suspicious behaviors.
Findings (0)
Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.
If followed without review, the plan could cause costly over-ordering, under-ordering, or premature markdowns.
The template can lead to real procurement actions and cash commitments if a user follows it. The wording frames this as a next step requiring approval, and the artifacts do not include tools or credentials to place orders automatically.
Approve pre-season buy quantities and place initial orders by [date]
Use the skill for planning, but require human review and separate approval before placing orders, changing prices, or committing budget.
Sensitive business metrics may be processed by the agent, and bad or manipulated input data could lead to poor purchasing recommendations.
The skill expects detailed proprietary sales and revenue data to be placed in the agent context. This is purpose-aligned, and no persistence or sharing is shown, but the data is business-sensitive and inaccurate inputs can distort downstream recommendations.
Required fields: product/SKU, time period, units sold, and revenue. Optional but valuable: units lost to stockouts ... returns by period, and channel/location breakdowns.
Provide only necessary data, verify source data quality, avoid unnecessary identifiers, and check agent memory/export settings if the data is confidential.