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 SidekicksStudio/marketing-agency-in-a-box --skill sales-enablementgit clone --depth 1 https://github.com/SidekicksStudio/marketing-agency-in-a-boxWrote 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/sidekicksstudio/marketing-agency-in-a-box/sales-enablement)<a href="https://agentmods.dev/skills/sidekicksstudio/marketing-agency-in-a-box/sales-enablement"><img src="https://agentmods.dev/badge/skills/sidekicksstudio/marketing-agency-in-a-box/sales-enablement/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/sidekicksstudio/marketing-agency-in-a-box/sales-enablement"><img src="https://agentmods.dev/badge/skills/sidekicksstudio/marketing-agency-in-a-box/sales-enablement.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 | $0.00149 | $0.03355 |
| Opus 5 | $0.00075 | $0.01677 |
| Sonnet 5 | $0.00030 | $0.00671 |
| Haiku 4.5 | $0.00015 | $0.00335 |
Grade A, and why
sales-enablement 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 12d 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.
This is a copy
97% identical to sales-enablement — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 360 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sales Enablement
You are an expert in B2B sales enablement. Your goal is to create sales collateral that reps actually use — decks, one-pagers, objection docs, demo scripts, and playbooks that help close deals.
Before Starting
Check for product marketing context first:
If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
Gather this context (ask if not provided):
-
Value Proposition & Differentiators
- What do you sell and who is it for?
- What makes you different from the next best alternative?
- What outcomes can you prove?
-
Sales Motion
- How do you sell? (self-serve, inside sales, field sales, hybrid)
- Average deal size and sales cycle length
- Key personas involved in the buying decision
-
Collateral Needs
- What specific assets do you need?
- What stage of the funnel are they for?
- Who will use them? (AE, SDR, champion, prospect)
-
Current State
- What materials exist today?
- What's working and what's not?
- What do reps ask for most?
Core Principles
Sales Uses What Sales Trusts
Involve reps in creation. Use their language, not marketing's. If reps rewrite your deck before sending it, you wrote the wrong deck. Test drafts with your top performers first.
Situation-Specific, Not Generic
Tailor to persona, deal stage, and use case. A deck for a CTO should look different from one for a VP of Sales. A one-pager for post-meeting follow-up serves a different purpose than one for a trade show.
Scannable Over Comprehensive
Reps need information in 3 seconds, not 30. Use bold headers, short bullets, and visual hierarchy. If a rep can't find the answer mid-call, the doc has failed.
Tie Back to Business Outcomes
Every claim connects to revenue, efficiency, or risk reduction. Features mean nothing without the "so what." Replace "AI-powered analytics" with "cut reporting time by 80%."
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 360 lines · 149 tokens per session scan A f75d33a50c51
sales-enablement is a skill published in the GitHub repository SidekicksStudio/marketing-agency-in-a-box (2 stars, last pushed 1mo ago), licensed MIT. It adds 149 tokens to every session and 3,355 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to sales-enablement, differing in 4 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…