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Gamification

Community
renatoasse
gamification

Desenha sistema de progressão e engajamento — XP, níveis, quests, recompensas.

Overview

Publisherrenatoasse
Repositoryibeia-skills
Skill namegamification
Stars
50
Forks
6
Bundled files
6
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.

  • 6 bundled files

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

  • Open source

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

Installation

Install the Gamification 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/renatoasse/ibeia-skills/tree/main/skills/gamification
  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/renatoasse/ibeia-skills.git /tmp/ibeia-skills
mkdir -p .claude/skills
cp -r /tmp/ibeia-skills/skills/gamification .claude/skills/gamification
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gamification 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 Gamification 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 Gamification 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.

Gamification

Design a progression/engagement system that reinforces a real target behavior. Three ideas drive every decision:

  1. Behavior-first — every mechanic exists to reinforce ONE mapped target behavior. A mechanic you can't justify against the Behavior Brief is decoration: cut it.
  2. Concrete balancing — curves, formulas, and starting numbers, not vibes. An XP system without a curve is a wish.
  3. Ethics Lens — every mechanic gets an honest White↔Black label with its real trade-off. The skill informs; the user sets the dial. Guide, don't police.

The unit of work is the LOOP (a system of mechanics feeding each other), not an isolated badge.

Announce at start: "Using gamification to design [system] reinforcing [target behavior]."

The Process — 3 phases, 1 gate

UNDERSTAND ──► Behavior Brief ──► DESIGN (+Ethics Lens) ──► 🚦 user approves? ──► BUILD ──► verify the loop
                                          │ no
                                          ▼
                                    revise blueprint

Phase 1 — UNDERSTAND → Behavior Brief

Read reference/understand.md and follow it. Map the product's core loop, the user's real win, the target behavior to reinforce, and the dominant player profile (Hexad). If gamification already exists, audit it (Octalysis-label what's there; separate working mechanics from decoration). Produce a Behavior Brief. Do NOT propose mechanics yet.

Phase 2 — DESIGN → approval gate 🚦

Read reference/psychology.md (the why), reference/mechanics.md (the full menu), reference/balancing.md (the numbers), reference/ethics.md (the lens), then reference/blueprint.md (the output format). Select mechanics from the menu — each justified against the target behavior, each labeled by the Ethics Lens, each with starting parameters. Present the blueprint and STOP. Do not touch code until the user explicitly approves.

Phase 3 — BUILD → verified loop

Implement the approved blueprint following the repo's existing patterns. Wire up the success metrics defined in the blueprint. Verify the LOOP works end to end — earn, progress, level, reward, return — not just that it compiles. Delegate pure visuals (palette, typography, celebration art) to impeccable/frontend-design when it makes sense: gamification owns the system, not the pixels. Commit in atomic units per the repo's conventions.

Calibration — declare both dials at the top of every blueprint

Dial 1 — Surface intensity (how much mechanic fits this product):

SurfaceExamplesMechanics dose
Serious tooladmin panels, tax/finance, internal dashboardsProgress/completion + celebrated real wins only. Deep mechanics feel absurd here.
Consumer producte-commerce, SaaS, content apps+ streaks-lite, badges on real milestones, onboarding-as-level-1
Engagement app / gamehabit trackers, education, fitness, gamesThe full menu is on the table — selected by the Behavior Brief, never all at once

Dial 2 — White↔Black ethics dial (how aggressive): default is White-Hat (sustainable, trust-building). The user can push toward Black-Hat (urgent, compelling, corrosive) — deliver what they choose with eyes open: label it, state the trade-off once, never silently escalate. See reference/ethics.md.

Red flags — stop and reconsider

ThoughtReality
"XP would be cool here"No mapped target behavior = decoration. Justify or cut.
"One more mechanic won't hurt"Every mechanic is cognitive + maintenance cost. The burden of proof is on adding.
"I'll skip the Ethics Lens, it's obviously fine"The user must SEE what they chose. Label every mechanic, even pure White-Hat.
"A no-forgiveness streak retains better"True — and it punishes having a life. Conscious dial choice, never a default.
"Variable rewards on everything"The most addictive and most corrosive mechanic. Careful handling, explicit label.
"Reward the thing users already love doing"Overjustification effect: extrinsic rewards can kill existing intrinsic motivation. Gamify what's hard to start, not what's already fun.
"Novelty will carry it"Every extrinsic mechanic decays. No intrinsic transition + endgame plan = dead system in weeks.
"One mechanic fits all users"Hexad profiles respond differently. Leaderboards delight Achievers and repel Free Spirits.
"This is a gamification problem" (but it's UX friction)Clarity problems ≠ progression problems. Hand off → dont-make-me-think.
"Skipping the gate, the design is obvious"The blueprint gate is mandatory. Present, then wait.

Sibling skill — dont-make-me-think

dont-make-me-think owns UX clarity and friction (make the journey obvious); this skill owns deep engagement mechanics. Two-way handoff: it sends deep-mechanics work here; you send clarity/friction problems there. If it isn't installed on this machine, say so and continue — this skill is fully self-sufficient.

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 Gamification AI skill do?

Desenha sistema de progressão e engajamento — XP, níveis, quests, recompensas.

Why use Gamification on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/renatoasse/ibeia-skills/tree/main/skills/gamification. 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 Gamification?

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 Gamification?

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

Is the Gamification AI skill free?

It is published on GitHub by renatoasse. Check the repository for licensing terms. 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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