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User Onboarding

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manojbajaj95
user-onboarding

Design and improve product user onboarding (first-time user experience) to drive activation and early retention. Produces an Onboarding & Activation Pack (aha moment spec, first 30 seconds + first mile plan, onboarding journey map, experiment backlog, measurement plan). Use for Growth teams.

Overview

Publishermanojbajaj95
Repositoryclaude-gtm-plugin
Skill nameuser-onboarding
Stars
109
Forks
34
Bundled files
8
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by manojbajaj95 on GitHub. Read the source before you install it.

Installation

Install the User Onboarding AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/manojbajaj95/claude-gtm-plugin/tree/main/skills/user-onboarding
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/manojbajaj95/claude-gtm-plugin.git /tmp/claude-gtm-plugin
mkdir -p .claude/skills
cp -r /tmp/claude-gtm-plugin/skills/user-onboarding .claude/skills/user-onboarding
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable User Onboarding in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads User Onboarding on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so User Onboarding is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

User Onboarding

Workspace Context

Read bootstrap context before asking questions: strategy/brand.md for brand, audience, offer, channels, tools, constraints, and metrics; about/me.md for personal voice; content/ideas.md and content/calendar.md for content planning. Use legacy product-marketing context files only as fallback. Save generated drafts to content/<platform>/drafts/YYYY-MM-DD_short-topic-slug.md, and route durable learnings back to strategy/brand.md, about/me.md, or content/ideas.md.

Operating Contract

This skill is self-contained for its frontmatter scope: use its local instructions, references, scripts, and assets as the playbook; ask only for missing task-specific inputs; hand off to adjacent skills instead of expanding scope; and return an actionable artifact, decision, plan, draft, or diagnostic.

Scope

Covers

  • Designing the first-time user experience (FTUE) from entry/signup → first value
  • Defining activation / the “aha moment” and reducing time-to-value
  • Creating a “first 30 seconds” experience and a “first mile” plan (milestones to activation)
  • Onboarding mechanics: progressive disclosure, guided setup, empty states, templates, checklists, feedback loops
  • Instrumentation + experiments to improve activation and early retention

When to use

  • “Activation is low” / “users drop during signup/onboarding”
  • “Time-to-value is too long”
  • “The first session doesn’t feel magical”
  • “We need an onboarding redesign / guided setup / empty state improvements”
  • “Define our aha moment and design onboarding to get users there”
  • “Create an onboarding experiment backlog + measurement plan”

When NOT to use

  • You’re onboarding employees, customers to a service process, or running training (not product onboarding).
  • You don’t have a stable value proposition / ICP (use brand-messaging-and-positioning or product-market-fit-analysis first).
  • You need a full retention strategy beyond onboarding (use customer-success-and-retention).
  • You need a conversion review of a marketing page or form (use conversion-rate-optimization).

Inputs

Minimum required

  • Product + ICP/segment(s) (1–2) and the primary job-to-be-done
  • Platform + entry point (web/mobile; where FTUE starts)
  • Best-available baseline metrics (even rough):
    • visit → signup → first key action → activation
    • time-to-value (time or sessions to first value)
    • D1 retention (if available)
  • Current onboarding flow summary (steps/screens) and the biggest drop-off point(s)
  • Constraints: timebox, eng/design capacity, allowed channels (in-app/email/push), privacy/legal/brand limits

Missing-info strategy

  • Ask up to 5 questions from references/INTAKE.md, then proceed.
  • If metrics are missing, proceed with explicit assumptions and label confidence.
  • Do not request secrets or PII; prefer aggregated funnels and redacted screenshots.

Outputs (deliverables)

Produce an Onboarding & Activation Pack (Markdown in-chat; or as files if requested) containing:

  1. Context snapshot (goal, segment, constraints, baseline)
  2. Current FTUE map (step-by-step) + friction log
  3. Activation / aha moment spec (behavioral definition + threshold + validation plan)
  4. “First 30 seconds” experience spec (magic moment + success criteria)
  5. “First mile” onboarding plan (milestones to activation; mechanics per milestone)
  6. Experiment backlog (prioritized) + 3–5 experiment cards
  7. Measurement + instrumentation plan (events, properties, dashboards, guardrails)
  8. Rollout/rollback + risk plan
  9. Risks / Open questions / Next steps (always included)

Templates and checklists:

Workflow (7 steps)

1) Intake + goal framing (activation-first)

  • Inputs: User prompt; references/INTAKE.md.
  • Actions: Define the primary segment and the activation outcome. Make the goal measurable (baseline → target, date). Capture constraints and guardrails (e.g., don’t increase drop-off, don’t add dark patterns).
  • Outputs: Context snapshot + goal statement.
  • Checks: Goal is one sentence with a metric, baseline, target, and date.

2) Map the current first-time journey (FTUE)

  • Inputs: Current flow description/screens; funnel + drop-offs; support/sales notes if available.
  • Actions: Create a step-by-step journey map from entry → first value. Log frictions (confusion, time, permissions, empty states, choice overload). Identify the single biggest leak.
  • Outputs: FTUE map table + friction log + primary leak.
  • Checks: Every step has a user goal, product action, and a measurable checkpoint (event or proxy).

3) Define the activation / “aha moment” (behavioral)

  • Inputs: Candidate value behaviors; available events; retention definition (if any).
  • Actions: Propose 3–5 candidate “aha” behaviors and pick one activation definition with a threshold (e.g., “creates X and shares Y within 7 days”). Specify how to validate (correlation with D7/D30; holdout if possible).
  • Outputs: Activation/aha moment spec + validation plan + instrumentation needs.
  • Checks: Activation is observable, repeatable, and measurable (not “understands value”).

4) Design the “first 30 seconds” (make it feel magical)

  • Inputs: Primary leak + activation spec; constraints.
  • Actions: Specify what the user should see/do/feel in the first 30 seconds:
    • Remove or defer non-essential setup (progressive disclosure).
    • Deliver a fast, native, interactive win (avoid passive carousel “tours”).
    • Provide immediate feedback and a clear next action.
  • Outputs: First 30 seconds spec (screen/script + success criteria).
  • Checks: A new user can reach a meaningful “win” within ~30 seconds (or you document why not and the best proxy).

5) Build the “first mile” plan (milestones → activation)

  • Inputs: Activation spec; journey map; UX constraints.
  • Actions: Break activation into 3–6 milestones (the “first mile”). For each milestone, define mechanics (guided setup, defaults, empty states, templates, checklists, feedback, progress). Ensure onboarding is “inside the product,” not detached from real actions.
  • Outputs: First mile onboarding plan + updated journey map.
  • Checks: Each milestone reduces time-to-value and has a leading-indicator metric.

6) Instrumentation + measurement plan

  • Inputs: Current analytics/events; proposed milestones; guardrails.
  • Actions: Define the minimum event schema and dashboards needed (funnel, time-to-value, activation rate, early retention). Add guardrails (support tickets, cancellations, performance).
  • Outputs: Measurement plan + event list/properties + dashboard outline.
  • Checks: Every experiment and milestone has a primary metric and at least one guardrail metric.

7) Prioritize experiments + quality gate

  • Inputs: Candidate interventions; measurement plan; references/CHECKLISTS.md and references/RUBRIC.md.
  • Actions: Convert top opportunities into experiment cards; prioritize (Impact × Confidence ÷ Effort). Add rollout/rollback guidance. Run checklist + score rubric. Always include Risks / Open questions / Next steps.
  • Outputs: Final Onboarding & Activation Pack.
  • Checks: Top 3 experiments are runnable within constraints and have “win/lose/learn” criteria.

Quality gate (required)

Examples

Example 1 (B2B SaaS, guided setup):
“Use user-onboarding. Product: workflow automation tool for SMB ops. Segment: new self-serve admins. Baseline: 42% signup completion, 9% activation within 7 days. Problem: users stall at connecting integrations. Constraint: 3-week sprint, 1 engineer, 1 designer. Output: an Onboarding & Activation Pack with an activation spec, a first-30-seconds + first-mile plan, and a prioritized experiment backlog.”

Example 2 (B2C app, time-to-value):
“Our first session feels flat. Redesign onboarding so users experience value within 30 seconds, define the activation event, and propose 5 experiments with metrics + guardrails.”

Boundary example (not product onboarding):
“Create an employee onboarding program for new hires.”
Response: this skill is for product onboarding; use an HR onboarding/training process instead.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the User Onboarding AI skill do?

Design and improve product user onboarding (first-time user experience) to drive activation and early retention. Produces an Onboarding & Activation Pack (aha moment spec, first 30 seconds + first mile plan, onboarding journey map, experiment backlog, measurement plan). Use for Growth teams.

Why use User Onboarding on TypingMind?

Because you install it once and use it with any model. User Onboarding is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install User Onboarding in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/manojbajaj95/claude-gtm-plugin/tree/main/skills/user-onboarding. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use User Onboarding?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with User Onboarding?

As many as you like. As long as a model supports skills, you can use User Onboarding with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the User Onboarding AI skill free?

Yes. It is published on GitHub by manojbajaj95 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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