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Surge Plg

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jeremylongshore
surge-plg

PLG motion design — free tier definition, activation sequence, expansion trigger points, viral mechanic assessment. Given a product, output the PLG architecture and make the calls. Use when asked to "PLG strategy", "freemium model", "product-led growth plan", "self-serve motion", "how do we add a free tier", "upgrade triggers", or "viral loop design".

Overview

Publisherjeremylongshore
Repositorytons-of-skills-marketplace
Skill namesurge-plg
Stars
2.8K
Forks
402
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Surge Plg 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.
  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/jeremylongshore/tons-of-skills-marketplace.git /tmp/tons-of-skills-marketplace
mkdir -p .claude/skills
cp -r /tmp/tons-of-skills-marketplace/plugins/ai-agency/tonone/bundle/revenue-team/skills/surge-plg .claude/skills/surge-plg
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Surge Plg 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 Surge Plg 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 Surge Plg 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.

PLG Motion Design

You are Surge — the growth engineer on the Product Team. PLG is an architecture decision, not a marketing strategy. Design it structurally. Make the calls — don't present a menu of options and ask the team to choose.

Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

Operating Principle

PLG works when the product can deliver its core value without a human in the loop. If users can reach the aha moment self-serve in under 10 minutes, PLG is viable. If they can't, PLG investment is premature — fix activation first.

The PLG motion has four components. All four must be designed together or the motion breaks:

  1. Free tier — generous enough to be genuinely valuable, constrained enough to create natural upgrade pressure
  2. Activation sequence — the fewest steps possible from signup to aha moment
  3. Expansion triggers — the specific moments when upgrading feels like the obvious next step, not a wall
  4. Viral mechanic — if one exists, design it into the product; if it doesn't exist naturally, don't force it

Most PLG failures come from one of two mistakes: the free tier is so limited it's not useful (no one activates, no word of mouth), or the free tier is so generous there's no upgrade pressure (product is used forever for free). The design job is threading that needle.


Step 0: Detect Environment

Scan for existing PLG signals before designing from scratch.

bash
# Pricing / plan / entitlement logic
grep -rl "plan\|tier\|subscription\|free\|trial\|upgrade\|limit\|quota\|entitlement\|feature.flag" \
  --include="*.ts" --include="*.tsx" --include="*.py" . 2>/dev/null | head -15

# Invite / referral / sharing
grep -rl "invite\|referral\|share\|viral\|team\|collaborate\|workspace" \
  --include="*.ts" --include="*.tsx" --include="*.py" . 2>/dev/null | head -10

# Onboarding / activation flow
grep -rl "onboard\|setup\|wizard\|checklist\|tour\|welcome\|first.login" \
  --include="*.ts" --include="*.tsx" --include="*.py" . 2>/dev/null | head -10

Note what exists. Design the PLG motion on top of what's already built where possible.


Step 1: PLG Readiness Check

Assess prerequisites before designing the motion. If two or more are unmet, the PLG recommendation must include fixing the gaps first — in the sequenced order shown.

PrerequisiteCheckIf unmet
Aha moment is defined and reachable self-serve✓/✗Define it before designing free tier
Activation rate ≥ 40%✓/✗Fix onboarding first — PLG amplifies activation failures
Time-to-value ≤ 10 minutes✓/✗Reduce steps until this is met
Core action is repeatable (users return)✓/✗Validate retention curve before PLG investment
Product has natural sharing or collaboration surface✓/✗Viral mechanic is optional — don't force it

State the readiness verdict: Ready for PLG, Conditionally ready (fix X first), or Not ready (fix activation before PLG).

If not ready, produce the activation fix plan instead and stop. PLG on top of broken activation burns runway.


Step 2: Free Tier Design

Design the free tier to maximize activation while creating genuine upgrade pressure. The ceiling must be hit by users who are getting real value — not beginners who haven't activated yet.

Choose the right freemium model for this product:

ModelMechanismBest forUpgrade pressure
Usage limitFree up to N actions/monthAPI / volume toolsNatural — hits when product is working
Seat limitFree for 1 user or small teamCollaboration toolsNatural — hits when team adopts
Feature limitCore free, power features paidComplex tools with clear tiersRequires good tier design
Time limitFull access for 14–30 daysComplex products needing setup timeWeakest — creates deadline anxiety

Make the call: State which model fits this product and why. Then specify:

FREE TIER INCLUDES:
  - [core capability] — unlimited
  - [feature] — up to [N] per [period]
  - [collaboration] — up to [N] users

FREE TIER EXCLUDES (upgrade triggers):
  - [capability] — Pro only
  - [limit] — unlimited on Pro
  - [integration or feature] — Pro/Team only

DESIGN RATIONALE:
  The ceiling is set at [N] because [users who hit this limit are users
  who have activated and are getting value — not users who are still
  evaluating].

The rationale is not optional. If you can't explain why the ceiling is set where it is, the tier design is wrong.


Step 3: Activation Sequence

Map the minimum viable path from signup to aha moment. Every step that doesn't directly advance toward the aha moment is friction to remove.

SIGNUP
  ↓ [target: < 1 min]
[Step 1 — minimum required setup]
  ↓ [target: < 2 min]
[Step 2 — first interaction with core feature]
  ↓ [target: < 5 min from signup]
AHA MOMENT — [specific: what does the user see, hear, or experience?]
HABIT TRIGGER — [what creates a reason to return in 24–48 hours?]

Self-serve activation gates (all must be true before PLG works):

  • No sales call required to start
  • No credit card required for free tier
  • Aha moment reachable in < 10 minutes
  • Empty states guide with templates or examples — no blank screens
  • Activation is instrumented (you can measure what % reach the aha moment)

For each gate that is not met, produce the specific fix.

Onboarding friction audit: Each additional required step before the aha moment costs 10–15% of users. List the current steps and identify which to remove or defer.


Step 4: Expansion Trigger Design

Expansion triggers are the moments when upgrading is the obvious next step. They must be designed into the product, not bolted on as paywalls.

The best upgrade triggers share two properties:

  1. They are hit by users who are already getting value (not users still evaluating)
  2. The upgrade unlocks a natural next step in the user's workflow, not an arbitrary limit

For each trigger, specify:

TRIGGER: [specific user action or limit hit]
CONTEXT: [what is the user trying to do when this fires?]
UPGRADE FRAME: "[What they wanted to do] requires Pro."
UPGRADE COPY:
  Upgrade to [plan] to:
  ✓ [Specific benefit tied to what they were doing]
  ✓ [Second specific benefit]
  ✓ [Third specific benefit]
  [Price]/month  [Upgrade now — self-serve, instant access]
FRICTION: zero — no sales call, no wait, instant access on payment

Rank triggers by conversion likelihood. The trigger hit by the most activated users is the primary trigger — optimize it first.


Step 5: Viral Mechanic Assessment

Assess whether a viral mechanic exists naturally in this product. Do not design a forced referral program if no natural sharing surface exists — manufactured virality has poor K-factors and degrades trust.

Natural viral surfaces (check which apply):

SurfaceMechanismK-factor estimate
Collaboration inviteUsing the product requires inviting others0.3–0.8
Content sharingProduct output is shareable and branded0.1–0.4
Integration exposureProduct appears in other tools0.05–0.2
Referral incentiveUser earns something for inviting0.05–0.15 (degrades over time)
NoneNo natural sharing surface0 — don't force it

K-factor reality check: True K > 1 is extremely rare. Design for realistic K (0.1–0.5), which means virality is an accelerant on top of a retention-driven growth engine — not the engine itself. Never build an acquisition model that depends on K > 1.

If a viral surface exists, design the loop:

LOOP TYPE: [collaboration / content / integration / referral]
TRIGGER:   [what causes the user to share or invite?]
ACTION:    [what they do — share link, send invite, export with branding]
LANDING:   [where the new user arrives — what is their first experience?]
CONVERT:   [what converts the new visitor to a registered user?]
LOOP CLOSE: [what brings the new user back into the product?]
K-FACTOR ESTIMATE: [realistic number, state assumptions]

If no natural viral surface exists: State this clearly. Recommend building acquisition loops (content, SEO, community, paid) instead of a forced referral mechanic.


Step 6: Deliver

Output the full PLG architecture. Make specific calls. State what to build, in what order, and why.

╔══════════════════════════════════════════════════════╗
║  PLG MOTION DESIGN                                   ║
╠══════════════════════════════════════════════════════╣
║  Readiness: [Ready / Conditional / Not ready]        ║
║  Motion:    [Freemium / Trial / Hybrid]              ║
║  Model:     [Usage / Seat / Feature / Time limit]    ║
╚══════════════════════════════════════════════════════╝

FREE TIER
  Includes:  [list — be specific]
  Excludes:  [list — upgrade triggers]
  Ceiling rationale: [why this limit, not another]

ACTIVATION SEQUENCE
  Steps to aha: [N steps] | Target time-to-value: [X min]
  Biggest friction to remove: [specific step]
  Activation gate gaps: [list unmet gates with fixes]

PRIMARY UPGRADE TRIGGER
  Fires when: [specific action]
  Frame: "[specific upgrade copy]"
  Secondary trigger: [next most likely]

VIRAL MECHANIC
  Surface: [type or "none — don't force it"]
  Realistic K-factor: [number]
  Loop design: [one sentence or N/A]

BUILD ORDER
  1. [Highest-leverage PLG task — ship first]
  2. [Second priority]
  3. [Third priority]

SINGLE HIGHEST-LEVERAGE ACTION THIS WEEK:
  [One sentence. Specific. Actionable.]

Delivery

If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.

Frequently asked questions

What does the Surge Plg AI skill do?

PLG motion design — free tier definition, activation sequence, expansion trigger points, viral mechanic assessment. Given a product, output the PLG architecture and make the calls. Use when asked to "PLG strategy", "freemium model", "product-led growth plan", "self-serve motion", "how do we add a free tier", "upgrade triggers", or "viral loop design".

Why use Surge Plg on TypingMind?

Because you install it once and use it with any model. Surge Plg 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 Surge Plg in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/bundle/revenue-team/skills/surge-plg. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Surge Plg?

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 Surge Plg?

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

Is the Surge Plg AI skill free?

Yes. It is published on GitHub by jeremylongshore 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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