Bundle stock planner

Inventory mismanagement kills small product businesses. When you plan a batch of gift boxes or kits, shared components and hidden reservations make manual calculations prone to error. This skill provides a rigorous, offline workflow for checking bundle feasibility against a physical stock snapshot. It prevents overselling by calculating total component demand across a proposed batch and identifying exact shortages before you commit to production.

What it does

• Component demand ledger calculates the total units required for shared parts across multiple bundle types.
• Stock normalization translates messy CSV or table data into a structured JSON contract while preserving unknown quantities.
• Conflict detection identifies when reservations and unavailable units overlap or exceed physical on-hand stock.
• Capacity reporting determines how many bundles can be built independently versus as a combined batch.
• Owner review generation produces a final summary with feasibility status, evidence logs, and specific SKU-level findings.

How it works

1. The user provides a stock snapshot (SKUs, on-hand, reservations) and bundle recipes.
2. The agent normalizes these into a plan-input.json file, confirming the stock pool and units.
3. The agent runs the check_bundle_plan.py helper script to process demand formulas and status precedence.
4. The agent inspects the CSV and JSON outputs to generate an owner-review.md assessment.

Frameworks & tools

This skill uses a Python 3.10+ standard-library script for offline logic. It requires no external APIs, network access, or credentials, operating entirely on local JSON and CSV files.

Why this beats prompting it yourself

Generic prompts often fail to account for shared components across different kits, leading to "ghost stock" errors. This skill uses a hard-coded Python ledger to ensure math is consistent, handles null quantities correctly, and enforces a strict decision-matrix that prevents unauthorized inventory assumptions.

Use cases

• Holiday kit planning to see if shared packaging supports a specific mix of gift boxes.
• Shortage auditing to identify exactly which low-stock components prevent a production run.
• Pre-assembly checks to verify physical stock matches store reservations before starting a build.

Known limitations

Supports only single-level bundles with whole units from a single stock pool. It does not handle nested recipes, lot/expiry tracking, or live inventory synchronization.