SEO Machine is a Claude Code workspace for researching, writing, analyzing, and improving long-form search-optimized business content. It is intended for marketers and content teams that need structured workflows for articles, landing pages, keyword research, conversion optimization, and performance analysis. Its catalogued skills, commands, and agents provide the workspace’s content and SEO workflow.
Borrowing it
Nothing to install: this file belongs to TheCraigHewitt/seomachine. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/TheCraigHewitt/seomachine/main/.claude/skills/product-marketing-context/SKILL.mdgit clone --depth 1 https://github.com/TheCraigHewitt/seomachineWrote 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/thecraighewitt/seomachine/product-marketing-context)<a href="https://agentmods.dev/skills/thecraighewitt/seomachine/product-marketing-context"><img src="https://agentmods.dev/badge/skills/thecraighewitt/seomachine/product-marketing-context/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/thecraighewitt/seomachine/product-marketing-context"><img src="https://agentmods.dev/badge/skills/thecraighewitt/seomachine/product-marketing-context.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00069 | $0.01668 |
| Opus 5 | $0.00034 | $0.00834 |
| Sonnet 5 | $0.00014 | $0.00334 |
| Haiku 4.5 | $0.00007 | $0.00167 |
Grade A, and why
product-marketing-context 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- product-marketing-context — 100% identical, 0 lines differ
- product-marketing-context — 100% identical, 77 lines differ
- product-marketing-context — 98% identical, 1 lines differ
- product-marketing-context — 95% identical, 13 lines differ
- product-marketing-context — 91% identical, 9 lines differ
- product-marketing-context — 89% identical, 15 lines differ
- product-marketing-context — 89% identical, 16 lines differ
- product-marketing-context — 89% identical, 15 lines differ
How it starts
The opening of the file, as written. The whole thing — 241 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 .claude/product-marketing-context.md.
Workflow
Step 1: Check for Existing Context
First, check if .claude/product-marketing-context.md already exists.
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
Important: Push for verbatim customer language. Exact phrases are more valuable than polished descriptions.
Sections to Capture
1. Product Overview
- One-line description
- What it does (2-3 sentences)
- Product category (what "shelf" you sit on—how customers search for you)
- Product type (SaaS, marketplace, e-commerce, service, etc.)
- Business model and pricing
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 · 241 lines · 69 tokens per session scan A 32ab77572d11
product-marketing-context is a skill published in the GitHub repository TheCraigHewitt/seomachine (7,434 stars, last pushed 1mo ago), licensed MIT. It adds 69 tokens to every session and 1,668 once invoked, about $0.0003 per session on Opus 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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