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 product-marketinggit 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/product-marketing)<a href="https://agentmods.dev/skills/sidekicksstudio/marketing-agency-in-a-box/product-marketing"><img src="https://agentmods.dev/badge/skills/sidekicksstudio/marketing-agency-in-a-box/product-marketing/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/product-marketing"><img src="https://agentmods.dev/badge/skills/sidekicksstudio/marketing-agency-in-a-box/product-marketing.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 | $0.00110 | $0.01775 |
| Opus 5 | $0.00055 | $0.00888 |
| Sonnet 5 | $0.00022 | $0.00355 |
| Haiku 4.5 | $0.00011 | $0.00178 |
Grade A, and why
product-marketing 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
83% identical to product-marketing-context — 19 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 — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Marketing Context
You help users create and maintain a product marketing context document. This captures foundational positioning and messaging information that other marketing skills reference, so users don't repeat themselves.
The document is stored at .agents/product-marketing.md.
Workflow
Step 1: Check for Existing Context
First, check if .agents/product-marketing.md already exists. Also check .claude/product-marketing.md and the legacy filename product-marketing-context.md (in either .agents/ or .claude/) for older setups — if found anywhere other than .agents/product-marketing.md, offer to move it to the canonical location.
If it exists:
- Read it and summarize what's captured
- Ask which sections they want to update
- Only gather info for those sections
If it doesn't exist, offer two options:
-
Auto-draft from codebase (recommended): You'll study the repo—README, landing pages, marketing copy, package.json, etc.—and draft a V1 of the context document. The user then reviews, corrects, and fills gaps. This is faster than starting from scratch.
-
Start from scratch: Walk through each section conversationally, gathering info one section at a time.
Most users prefer option 1. After presenting the draft, ask: "What needs correcting? What's missing?"
Step 2: Gather Information
If auto-drafting:
- Read the codebase: README, landing pages, marketing copy, about pages, meta descriptions, package.json, any existing docs
- Draft all sections based on what you find
- Present the draft and ask what needs correcting or is missing
- Iterate until the user is satisfied
If starting from scratch: Walk through each section below conversationally, one at a time. Don't dump all questions at once.
For each section:
- Briefly explain what you're capturing
- Ask relevant questions
- Confirm accuracy
- Move to the next
Push for verbatim customer language — exact phrases are more valuable than polished descriptions because they reflect how customers actually think and speak, which makes copy more resonant.
What ships with it
1 file 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 · 242 lines · 110 tokens per session scan A cbebd56731ed
product-marketing is a skill published in the GitHub repository SidekicksStudio/marketing-agency-in-a-box (2 stars, last pushed 1mo ago), licensed MIT. It adds 110 tokens to every session and 1,775 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 83% identical to product-marketing-context, differing in 19 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…