Install
$ agentstack add skill-jeffbrines-openfpa-sku-profitability ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
SKU Profitability
> Generated skill (example). This is the kind of bespoke skill fpa-learn-business > proposes when the business profile says "product company with a discrete SKU set." > It lives in skills/generated/ - in a real engagement it would be written into the > client's repo after human approval, citing the profile facts that justify it (here: > a limited-SKU D2C brand where per-product economics drive the mix decision).
Overview
The channel-level forecast tells you the business is healthy; it doesn't tell you which products carry it. This skill computes per-SKU economics and the Pareto curve so you can see the 80/20, find margin-dilutive SKUs, and make cut/reprice/push calls.
Core principle: Revenue flatters; margin and contribution decide. Rank by gross profit, not by sales.
When to use
- "Which products actually make money?" / "what should we cut?"
- Product-mix, pricing, or assortment-rationalization decisions
- Any product business with a discrete SKU set (especially limited-SKU brands)
Workflow
- Load the SKUs (annual units, price, unit cost):
``python import pyfpa skus = pyfpa.load_skus("examples/ridgeline/skus.yaml") # or build [Sku(...)] inline df = pyfpa.sku_profitability(skus) ` df is sorted by gross profit (desc), indexed by SKU, with columns: units, revenue, cogs, grossprofit, grossmargin, revenueshare, cumulativerevenue_pct`.
- Find the 80/20:
``python n = pyfpa.pareto_breakpoint(df, threshold=0.8) # SKUs that make 80% of revenue ``
- Read the signals:
- Top of the list (high gross profit) - protect and push these.
- High revenue, low
gross_margin- reprice or renegotiate cost; they're buying share with your margin. - Low
revenue_shareAND low margin - candidates to cut (carrying cost without contribution). - The Pareto tail - if the bottom SKUs add complexity (SKUs to manage, inventory to hold) without margin, rationalize them.
- Recommend in business terms: which SKUs to push, reprice, or discontinue, and the margin impact of each move.
Judgment checks (see fpa-cfo-judgment)
gross_marginhere is per-unit price minus unit cost - it excludes channel fees, returns, and fulfillment. A D2C SKU and a wholesale SKU at the same listed margin are not equally profitable once channel economics hit.- A "high-margin" SKU with tiny volume may not be worth the operational complexity it adds. Weigh contribution dollars, not just the percentage.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: JeffBrines
- Source: JeffBrines/openfpa
- License: MIT
- Homepage: https://www.guiderail.io
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.