merch-report-html

name

merch-report-html

description

Generate the shareable end-of-quarter markdown review as a single self-contained interactive HTML report by running the bundled merch_report.py script. Use as the final step once the analysis and policy-bound markdown recommendations are decided. The script renders top sellers, slow movers, and the markdown recommendations into one BlastBox Omega themed HTML file with inline SVG charts, sortable tables, and a clearance toggle - no internet, no external assets. Hand the file to the manager to download and open in any browser.

Merch Report (interactive HTML)

This skill produces the shareable report that closes out the end-of-quarter markdown review by running the bundled merch_report.py script. You do not write any Python and you do not invent numbers: every value comes from the earlier steps. The script renders one self-contained, interactive HTML file (BlastBox Omega theme) - all CSS, JavaScript, and SVG charts are inlined, so it references nothing external and works fully offline once downloaded.

Use this as the last step, after sales-analysis has produced the top sellers and slow movers and markdown-optimizer has produced the policy-bound recommendations.

Inputs (pass the JSON you already have as flags)

Flag	Source
--top-sellers	top sellers from sales-analysis - [{name, units, velocity, revenue}]
--slow-movers	slow movers from sales-analysis - [{name, sku, weeks_of_cover, price, cost, margin}]
--recommendations	RECOMMENDATIONS_JSON from markdown-optimizer - [{name, sku, price, rec_discount_pct, new_price, new_margin, binding, flag_clearance}]
--policy	the guardrails from the Store Policy Agent - {max_discount_pct, margin_floor_pct, min_useful_promo_discount_pct}
--window	the review window label, e.g. "Apr 12 - May 31, 2026"
--weeks	analysis window length (default 8)
--woc-threshold	slow-mover weeks-of-cover threshold (default 8)
--out	output path (default merch_report.html)

Run the bundled script

python3 merch_report.py \
    --window "Apr 12 - May 31, 2026" --weeks 8 \
    --top-sellers '[{"name":"MEGA Lizards from Outer Space","units":540,"velocity":67.5,"revenue":37794.6}]' \
    --slow-movers '[{"name":"OmegaVision VR Headset","sku":"SKU-VR-GOGGLES","weeks_of_cover":64.0,"price":199.99,"cost":70.0,"margin":65.0}]' \
    --recommendations '[{"name":"OmegaVision VR Headset","sku":"SKU-VR-GOGGLES","price":199.99,"rec_discount_pct":30.0,"new_price":139.99,"new_margin":50.0,"binding":"max_discount","flag_clearance":false}]' \
    --policy '{"max_discount_pct":30,"margin_floor_pct":15,"min_useful_promo_discount_pct":10}'

The script writes merch_report.html to the working directory and prints a one-line confirmation (file size + counts). Hand that file back as a download.

What the report contains (so you can describe it)

• A KPI strip: slow movers, promo markdowns, items flagged for clearance, deepest promo cut, average new margin.
• Top Sellers and Slow Movers sections, each with an inline SVG bar chart (the slow-mover chart marks the weeks-of-cover threshold) and a sortable table.
• A Markdown Recommendations section: a discount-depth chart, the policy chips, a sortable table showing current price, discount, new price, new margin, and the binding constraint, with clearance-flagged rows highlighted and a toggle to hide them.

Report back

Tell the manager the interactive report is ready (merch_report.html), that it opens in any browser, and that tables are sortable and clearance items can be toggled. Summarize the headline: how many promo markdowns, the deepest cut, and which items were routed to clearance.

Notes

• The bundled files are merch_report.py and report_template.html.j2; the script uses only the Python standard library plus Jinja2 (available in the runtime). If the template file is missing it falls back to a minimal embedded template.
• The report inlines everything - no CDN, no web fonts, no network. It is safe to share and open offline.
• Never invent numbers - every figure comes from the analysis, optimizer, and Store Policy Agent results. All data is mock; for demo purposes only.