markdown-optimizer

name

markdown-optimizer

description

Recommend a discount percentage and new price for each slow-moving product by running the bundled markdown_optimizer.py script, respecting pricing guardrails that come from the Store Policy Agent (never hardcode them). Use after sales-analysis-chart has identified the slow movers and after fetching the markdown guardrails via the Store Policy Agent's get_markdown_policy. The script applies the max-discount cap and margin floor, computes each new price and margin, flags items that cannot be meaningfully promoted within the floor for clearance instead, and renders a recommended-discount chart.

Markdown Optimizer (BlastBox Omega)

This skill decides how much to mark down each slow-moving product by running the bundled markdown_optimizer.py script. You do not write any Python and you do not hardcode the pricing rules: the guardrails come from the Store Policy Agent via get_markdown_policy(markdown_type) and are passed to the script as CLI flags. This keeps all policy in one place (the Policy Agent owns it).

When to use

After sales-analysis-chart has produced the structured slow_movers list, and after you have fetched the guardrails from the Store Policy Agent. Use it whenever a manager asks what to mark down and by how much within policy.

Inputs (gather these first, then pass them as flags)

1. Slow movers - the slow_movers JSON from sales-analysis-chart: a list of {sku, name, price, cost} (extra keys are ignored).
2. Guardrails - call the Store Policy Agent - get_markdown_policy("promo"). It returns max_discount_pct, margin_floor_pct, and min_useful_promo_discount_pct (e.g. 30, 15, 10). Pass all three. Do not invent or hardcode them. For a clearance pass, call get_markdown_policy("clearance") and use --markdown-type clearance.

Run the bundled script

For a promo markdown, pass the slow movers and the three guardrails:

python3 markdown_optimizer.py \
    --markdown-type promo \
    --max-discount 30 --margin-floor 15 --min-useful 10 \
    --slow-movers '[{"sku":"SKU-VR-NEBULA","name":"Nebula VR Headset","price":199.99,"cost":70.00},
                    {"sku":"SKU-GALAXY-PAD","name":"Galaxy Pad Controller","price":49.99,"cost":18.00}]'

For a clearance pass (no promo math, every item flagged for a manager decision):

python3 markdown_optimizer.py --markdown-type clearance \
    --slow-movers '[{"sku":"SKU-OMEGA-CORE","name":"Omega Core Console","price":399.99,"cost":320.00}]'

The script prints one line per item, then RECOMMENDATIONS_JSON=<json>, and writes markdowns.png (a bar chart of the recommended promo discounts).

What the script does (for your report, not for you to reimplement)

• floor price = cost / (1 - margin_floor_pct/100) - the lowest price that still keeps the required gross margin.
• recommended discount % = min(max_discount_pct, floor-allowed %). When the cap binds you get the headline discount; when the floor binds the new margin equals the floor (binding says which).
• Items whose floor-allowed discount is below min_useful_promo_discount_pct come back with flag_clearance: true - they can't be meaningfully promoted, so they should be cleared out via a clearance pass (a separate decision requiring a manager flag).

Report

• Present a table: product, current price, recommended discount %, new price, new margin, and whether the margin floor or max-discount cap was binding.
• Call out any item flagged for clearance, noting clearance is a separate manager decision (get_markdown_policy("clearance")).
• Hand the finalized promo set (RECOMMENDATIONS_JSON) to merch-report-pdf for the shareable report.

Notes

• The bundled script is markdown_optimizer.py; it depends only on matplotlib (available in the runtime) and prints recommendations even if the chart is skipped.
• The guardrail numbers MUST come from the Store Policy Agent. If they are missing, fetch them before running - do not guess.
• All data is mock; recommendations are for demo purposes only.