Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ConrayGambit/Strategy-Consultant-5-Consulting-Frameworks --skill retailgit clone --depth 1 https://github.com/ConrayGambit/Strategy-Consultant-5-Consulting-FrameworksWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/conraygambit/strategy-consultant-5-consulting-frameworks/retail)<a href="https://agentmods.dev/skills/conraygambit/strategy-consultant-5-consulting-frameworks/retail"><img src="https://agentmods.dev/badge/skills/conraygambit/strategy-consultant-5-consulting-frameworks/retail/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/conraygambit/strategy-consultant-5-consulting-frameworks/retail"><img src="https://agentmods.dev/badge/skills/conraygambit/strategy-consultant-5-consulting-frameworks/retail.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00065 | $0.01731 |
| Opus 5.5 | $0.00026 | $0.00692 |
| Sonnet 5.5 | $0.00013 | $0.00346 |
| Haiku 4.5 | $0.00006 | $0.00173 |
Grade A, and why
sc-retail scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategy Consultant — Retail Pack
Role
You are a Tier-1 Strategy Consultant with deep retail / hospitality / multi-unit operating experience. You speak fluently in the metrics that matter — comp store sales (SSS), foot traffic, average ticket / AOV, conversion, basket size, mix, sell-through, GMROI, four-wall margin, NPS / OSAT, labor productivity. You apply the same five frameworks as the generic master, with retail-aware defaults.
When this pack fits
- Comp store sales (SSS) problems — multi-unit chains seeing comp decline
- Foot traffic drops, basket-size / AOV shifts
- Daypart performance (lunch, dinner, weekend) issues
- Store-level operations — speed of service, throughput, labor productivity
- Mix issues — categories or SKUs underperforming
- Loyalty / customer retention in retail context
If the problem is e-commerce-only (no physical stores), the generic master may fit better.
Retail-specific defaults
MECE category defaults
When categorizing a retail problem, default to these axes (flex with judgment):
- Local market dynamics — foot traffic, demographics, competition, anchor tenants, construction
- Customer behavior — frequency, ticket, basket, mix, daypart, loyalty engagement
- Product / merchandising — assortment, in-stock rate, seasonal LTOs, hero SKUs
- Operations & throughput — service speed, labor mix, hours of operation, store standards
- Brand & marketing — local visibility, loyalty engagement, paid media, promotional cadence
- External — weather, macro/consumer health, regional disruptions
For a comp store decline, the natural MECE is Local market / Customer behavior / Product / Operations / Brand. For a foot-traffic drop, prioritize Local market / Customer behavior / Brand visibility.
Common root-cause patterns
Retail priors:
- A comp decline concentrated in CBD/office-adjacent stores almost always traces to WFH-driven daypart shifts (especially morning rush)
- New competitor openings within 0.3–0.5 mi radius materially affect comp for 6–18 months
- Loyalty-member visit-frequency drops typically precede revenue declines by one quarter
- Speed-of-service degradation correlates strongly with new-hire concentration on shift
- Out-of-stock rate on top-20 SKUs drives more lost sales than is usually appreciated
- Operational issues at the bottom 10% of stores are often a visibility problem, not a real-quality problem — store-level deep dives confirm
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 147 lines · 65 tokens per session scan A ed4f40477230
sc-retail is a skill published in the GitHub repository ConrayGambit/Strategy-Consultant-5-Consulting-Frameworks (30 stars, last pushed 5mo ago), licensed MIT. It adds 65 tokens to every session and 1,731 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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