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 agentmods add commands/brainbytes-dev/everything-claude-marketing/landing-pagegit clone --depth 1 https://github.com/brainbytes-dev/everything-claude-marketingWrote 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/commands/brainbytes-dev/everything-claude-marketing/landing-page)<a href="https://agentmods.dev/commands/brainbytes-dev/everything-claude-marketing/landing-page"><img src="https://agentmods.dev/badge/commands/brainbytes-dev/everything-claude-marketing/landing-page.svg" alt="Measured on agentmods" 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 | $0.00019 | $0.02078 |
| Opus 5 | $0.00010 | $0.01039 |
| Sonnet 5 | $0.00004 | $0.00416 |
| Haiku 4.5 | $0.00002 | $0.00208 |
Grade A, and why
landing-page 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 5d 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 — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/landing-page
Create or optimize a landing page with persuasive structure, compelling copy, strategic CTA placement, and data-driven conversion recommendations.
What This Command Does
This command designs a complete landing page or audits an existing one for conversion optimization. It delivers the full page structure — from hero section through final CTA — with written copy, layout recommendations, social proof placement, and specific suggestions for improving conversion rates. The output includes the information hierarchy, headline and subhead copy, section-by-section content, CTA language and placement strategy, and a prioritized list of optimization tests to run. Whether you are building a new page or trying to push a 2% conversion rate higher, this command gives you a concrete, actionable plan.
When to Use
- Building a landing page for a new product, feature, or campaign
- Optimizing a signup, trial, or demo request page that is underperforming
- Creating a campaign-specific landing page for paid ads or email traffic
- Redesigning a pricing page or comparison page
- Building a webinar, event, or waitlist registration page
- Launching a Product Hunt or beta launch landing page
- A/B testing page elements and need variant ideas
- Auditing an existing page for conversion issues
How It Works
- Understands the goal — Clarifies the page's primary conversion action, traffic source, and audience
- Analyzes current state — For optimization requests, reviews the existing page structure and identifies issues
- Defines the information hierarchy — Determines the optimal order of sections based on visitor intent and awareness level
- Writes the copy — Creates headline, subhead, body sections, and CTAs using proven persuasion frameworks
- Places social proof — Strategically positions testimonials, logos, metrics, and trust signals
- Structures the layout — Recommends section flow, visual weight distribution, and scroll depth management
- Designs CTA strategy — Determines number of CTAs, placement, copy, and visual treatment
- Builds optimization roadmap — Prioritizes A/B tests based on expected impact
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.
- 5d ago First seen · 221 lines · 19 tokens per session scan A a16ef1eac57a
landing-page is a command published in the GitHub repository brainbytes-dev/everything-claude-marketing (5 stars, last pushed 5mo ago), licensed MIT. It adds 19 tokens to every session and 2,078 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.