signup

signup is a skill for Claude Code from coreyhaines31/marketingskills. It costs 127 tokens per session (2,334 once invoked), scanned A, original, MIT.

A guide for improving the process people use to register for an account, start a free trial, join a waitlist, or activate a product.

In plain words
What is it for?
Use it to review signup forms, reduce registration drop-off, decide which information to request, and improve what happens after signup.
Why use it?
It helps find and remove unnecessary steps or questions that cause people to abandon registration.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the marketing-skills plugin — 50 skills shipped together

Good fit Use it to review signup forms, reduce registration drop-off, decide which information to request, and improve what happens after signup.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/coreyhaines31/marketingskills/signup
About the project

Marketing Skills is a collection of skills for AI coding agents that assist with conversion optimization, copywriting, SEO, analytics, and growth engineering. Technical marketers and founders use it to apply agents to marketing work in tools such as Claude Code, OpenAI Codex, Cursor, and Windsurf. The catalogue entries are the project's own marketing skills and instructions.

coreyhaines31/marketingskills · 49,629 stars · on GitHub · marketing-skills.com

Install

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.

Any agent
npx skills add coreyhaines31/marketingskills --skill signup
Clone the repo
git clone --depth 1 https://github.com/coreyhaines31/marketingskills

Made for: Claude Code.

Or install marketing-skills, the plugin that ships this one along with the rest of its 50 skills.

Wrote 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.

agentmods badge for signup

README.md
[![agentmods](https://agentmods.dev/badge/skills/coreyhaines31/marketingskills/signup/github.svg)](https://agentmods.dev/skills/coreyhaines31/marketingskills/signup)
Your own site
<a href="https://agentmods.dev/skills/coreyhaines31/marketingskills/signup"><img src="https://agentmods.dev/badge/skills/coreyhaines31/marketingskills/signup/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.

agentmods 80×15 button for signup

Your own site · 80×15
<a href="https://agentmods.dev/skills/coreyhaines31/marketingskills/signup"><img src="https://agentmods.dev/badge/skills/coreyhaines31/marketingskills/signup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 127 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,334 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 14 May 2026
  • Snyk pass 14 May 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00127 $0.02334
Opus 5 $0.00063 $0.01167
Sonnet 5 $0.00025 $0.00467
Haiku 4.5 $0.00013 $0.00233

Measured 13d ago against content hash 9395aabae648, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

signup 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 13d 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.

Origin

Copies of this mod

8 near-identical copies found in the catalogue:

skills/signup/SKILL.md · 360 lines

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.

Signup Flow CRO

You are an expert in optimizing signup and registration flows. Your goal is to reduce friction, increase completion rates, and set users up for successful activation.

Initial Assessment

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.

Before providing recommendations, understand:

  1. Flow Type

    • Free trial signup
    • Freemium account creation
    • Paid account creation
    • Waitlist/early access signup
    • B2B vs B2C
  2. Current State

    • How many steps/screens?
    • What fields are required?
    • What's the current completion rate?
    • Where do users drop off?
  3. Business Constraints

    • What data is genuinely needed at signup?
    • Are there compliance requirements?
    • What happens immediately after signup?

Core Principles

1. Minimize Required Fields

Every field reduces conversion. For each field, ask:

  • Do we absolutely need this before they can use the product?
  • Can we collect this later through progressive profiling?
  • Can we infer this from other data?

Typical field priority:

  • Essential: Email (or phone), Password
  • Often needed: Name
  • Usually deferrable: Company, Role, Team size, Phone, Address

2. Show Value Before Asking for Commitment

  • What can you show/give before requiring signup?
  • Can they experience the product before creating an account?
  • Reverse the order: value first, signup second

3. Reduce Perceived Effort

  • Show progress if multi-step
  • Group related fields
  • Use smart defaults
  • Pre-fill when possible

4. Remove Uncertainty

  • Clear expectations ("Takes 30 seconds")
  • Show what happens after signup
  • No surprises (hidden requirements, unexpected steps)

Field-by-Field Optimization

Email Field

  • Single field (no email confirmation field)
  • Inline validation for format
  • Check for common typos (gmial.com → gmail.com)
  • Clear error messages

Read the full file on GitHub · 360 lines

Files

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.

Changes

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.

  1. 13d ago First seen · 360 lines · 127 tokens per session scan A 9395aabae648

Subscribe to this mod's changes

signup is a skill published in the GitHub repository coreyhaines31/marketingskills (49,629 stars, last pushed 7d ago), licensed MIT. It adds 127 tokens to every session and 2,334 once invoked, about $0.0006 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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