What the Core-loop and meta-loop analysis prompt does

Act as a senior game-systems designer and telemetry-led player-experience analyst.

The prompt will, at minimum:

  • Validate datasets, definitions, time windows, market scope and source-of-truth ownership before assessing core-loop and meta-loop analysis
  • Examine player promise, moment-to-moment actions, feedback, mastery, failure, reward, session arc, progression, collection, social and economy layers, loop frequency, dependency, friction, novelty, motivation and telemetry coverage; preserve original identifiers and show the derivation of every finding
  • Segment only when evidence supports the split. Expose missingness, sample bias, seasonality, releases, campaigns, migrations and other confounders instead of hiding them in averages
  • Recompute material metrics from supplied values; disclose formulas, denominators, exclusions and scenario assumptions. Never invent benchmarks, market sizes or competitor performance
  • Turn evidence into a loop map, evidence-led friction diagnosis, coherence scorecard and prioritised design-validation backlog; assign owner, priority, dependency, expected signal, verification method and human-approval point to each action

Who it is for

Gökhan Güzel's games prompt for ChatGPT users: marketers, founders, agencies and consultants who need an auditable, evidence-based deliverable instead of generic advice.

What you get

  • Executive summary and data-quality report
  • Core-loop and meta-loop analysis methodology and evidence ledger
  • Segmented findings, calculations and scoring
  • Prioritised action backlog with owners and validation criteria
  • Sources, limitations, confidence and QA report

Variables

PlaceholderPurpose
{{constraints}}Approved rule, policy or constraint; state owner, version, scope, jurisdiction and effective date
{{core_actions}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{design_document}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{economy_systems}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{funnel_data}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{game_build}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{game_name}}Verified identifier or text value; state exact spelling, source, status and validity scope
{{known_design_goals}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{player_feedback}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{progression_systems}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{retention_data}}Structured dataset or source file; state fields, data types, period, units, currency, time zone and provenance
{{session_telemetry}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{success_metrics}}Numeric value or table; state formula, numerator, denominator, unit, currency, tax treatment, period and source
{{target_audience}}Target audience, segment, persona, customer/player or industry group

How to use

Copy the prompt with the button above, replace every {{placeholder}} with your verified data, and paste it as the first message in a new ChatGPT conversation. The prompt runs a short question gate first; answer it, then the deliverable is produced.

Run Core-loop and meta-loop analysis in ChatGPT

Open a new ChatGPT chat, paste the filled-in Core-loop and meta-loop analysis prompt and answer the short question gate. ChatGPT then returns the executive decision, the evidence ledger and the task-specific tables in one reply.

Source

Original file in the multilingual prompt library on GitHub: