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Game Data Analytics And Live Economy

Community
peterbamuhigire
game-data-analytics-and-live-economy

Use when defining game telemetry, player funnels, retention or progression metrics, experiments, remote configuration, virtual economy sources and sinks, live events, anomaly controls, or ethical data-driven decisions.

Overview

Publisherpeterbamuhigire
Repositorychwezi-dev-engine
Skill namegame-data-analytics-and-live-economy
Stars
29
Forks
6
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Game Data Analytics And Live Economy 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/peterbamuhigire/chwezi-dev-engine/tree/main/skills/game-development/game-data-analytics-and-live-economy
  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/peterbamuhigire/chwezi-dev-engine.git /tmp/chwezi-dev-engine
mkdir -p .claude/skills
cp -r /tmp/chwezi-dev-engine/skills/game-development/game-data-analytics-and-live-economy .claude/skills/game-data-analytics-and-live-economy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Game Data Analytics And Live Economy 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 Game Data Analytics And Live Economy 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 Game Data Analytics And Live Economy 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.

Game Data, Analytics, and Live Economy

Collect the minimum trustworthy game data needed for named decisions, and change live systems through reversible controls.

Use When

  • Defining telemetry schemas, KPI/experiment plans, progression/economy simulation, remote config, live events, or anomaly detection.

Do Not Use When

  • Data collection has no stated decision, consent/legal basis, retention, or owner.
  • The request seeks manipulative monetisation or fabricated benchmarks.

Required Inputs

Decision questions, player journey, audience/age and territory, event/version taxonomy, separate Ads/Analytics/vendor identity/privacy/consent models, economy and ad-placement ledgers, cohorts, wellbeing/trust guardrails, experiment rules, data-quality thresholds, remote-config access, rollback/kill switches, and incident owners.

Workflow

  1. Map each question to a metric, event fields, denominator, cohort/window, quality check, owner, and action threshold.
  2. Version schemas and validate client/server events for duplicates, loss, ordering, clock skew, bots, test traffic, and late arrival.
  3. Model currency/item sources, sinks, balances, caps, prices, grants, refunds, and non-paying paths; reconcile consequential events.
  4. Model every ad format/placement with trigger, opt-in/decline, cap/pace, reward, no-fill/error/offline path, audience treatment and removal switch. Never infer consent across SDKs.
  5. Pre-register experiments and guardrails; prevent overlapping contamination and stop on privacy, accessibility, child-safety, wellbeing, trust or data-quality failure even when revenue/retention rises.
  6. Route remote config, economies, offers, ads and events through approval, typed validation, staged exposure, monitoring, expiry, reconciliation and rollback.

Quality Standards

  • Every metric names its player or operational decision, denominator, version, cohort/window, owner and quality threshold.
  • Economy, offer, event and ad changes are reproducible, staged, auditable and reversible.
  • Consent, deletion, age treatment and data minimisation are verified separately for each SDK and territory.
  • Commercial results are paired with fairness, accessibility, wellbeing, trust and failure-path evidence.

Decision Rules

ConditionAction
Metric has no named decision or denominatorDo not collect or publish it
Engagement rises while regret, harmful spend, inability to stop, sleep disruption, complaints, refunds or child incidents worsenStop or roll back; do not call the experiment a win
Rewarded ad is technically opt-in but progression makes decline punitiveClassify it as coercive and redesign the economy
Consent, deletion or SDK failure path is unverifiedKeep collection/ads disabled and return the gap
Economy mutation cannot reconcile or reverseBlock remote release

Degraded Mode

When consent state, age treatment, network, vendor or telemetry is unavailable, use privacy-minimised defaults, preserve play without ads/analytics where feasible, queue only explicitly permitted operations, and expose a tested kill switch. Mark commercial and behaviour conclusions not assessed when data quality fails.

Anti-Patterns

  • Dashboard without a decision. Fix: bind metric, owner, threshold and action.
  • Retention as a solitary north star. Fix: pair it with player value, wellbeing, trust and exit measures.
  • One privacy toggle for multiple vendors. Fix: map each SDK's data, purpose, consent and deletion separately.
  • Live economy edit without version/reconciliation. Fix: stage, audit and rehearse reversal.
  • Revenue-positive intrusive ad. Fix: treat interruption or coercion as a blocking product defect.

Outputs

Measurement plan; event/schema and SDK-purpose registry; data-quality tests; economy/ad-placement model and reconciliation; wellbeing/trust guardrails; experiment record; remote-config/event change log; rollback evidence; qualified decision readout.

References

Evidence Produced

CategoryArtifactFormatExample
CorrectnessGame telemetry and economy validation packMarkdown plus dataset/queryevent schema, consent, source/sink balance, experiment guardrail, anomaly, and rollback

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 Game Data Analytics And Live Economy AI skill do?

Use when defining game telemetry, player funnels, retention or progression metrics, experiments, remote configuration, virtual economy sources and sinks, live events, anomaly controls, or ethical data-driven decisions.

Why use Game Data Analytics And Live Economy on TypingMind?

Because you install it once and use it with any model. Game Data Analytics And Live Economy 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 Game Data Analytics And Live Economy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/peterbamuhigire/chwezi-dev-engine/tree/main/skills/game-development/game-data-analytics-and-live-economy. 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 Game Data Analytics And Live Economy?

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 Game Data Analytics And Live Economy?

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

Is the Game Data Analytics And Live Economy AI skill free?

Yes. It is published on GitHub by peterbamuhigire 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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