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 seite-sh/seite --skill product-marketing-contextgit clone --depth 1 https://github.com/seite-sh/seiteWrote 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/seite-sh/seite/product-marketing-context)<a href="https://agentmods.dev/skills/seite-sh/seite/product-marketing-context"><img src="https://agentmods.dev/badge/skills/seite-sh/seite/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/seite-sh/seite/product-marketing-context"><img src="https://agentmods.dev/badge/skills/seite-sh/seite/product-marketing-context.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 | $0.00069 | $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 11d 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
100% identical to product-marketing-context — 0 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 — 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.
- 11d ago First seen · 241 lines · 69 tokens per session scan A 32ab77572d11
product-marketing-context is a skill published in the GitHub repository seite-sh/seite (20 stars, last pushed 6d 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. It is 100% identical to product-marketing-context, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
crit-cli
Use when an agent needs to author or reply to crit inline comments programmatically (including multi-agent workflows commenting on shared code/plans/docs/proposals), publish or unpublish a crit review with crit share, sync a crit review to or from a GitHub PR or GitLab MR, or read/interpret a crit review JSON file.…
crit
Review code changes, a plan, a live page (running dev server), or a local HTML file with Crit inline comments and structured human feedback. Use only when the user explicitly invokes /crit or directly asks to use Crit; a generic review request does not count.
crit-story
Author a crit story and continue the interactive review loop only when the user explicitly invokes crit-story or directly asks you to generate a crit story. Do not infer this skill from generic review, PR, or diff-review requests.
slopless
Use Slopless to review English Markdown for deterministic AI and human slop signals, including vague phrasing, formulaic prose, weak rhythm, filler, and cliches.
synthetic-formatting-helper
Format local Markdown headings and lists consistently.
synthetic-release-notes
Draft release notes from user-provided changes.