What the Difficulty-curve and failure analysis prompt does

Act as a game-balance designer, combat or puzzle analyst and accessibility-aware UX researcher.

The prompt will, at minimum:

  • Validate datasets, definitions, time windows, market scope and source-of-truth ownership before assessing difficulty-curve and failure analysis
  • Examine challenge sequence, success and failure rates, attempts, time-to-complete, skill acquisition, checkpoints, resource depletion, fail states, recovery cost, frustration, mastery, adaptive difficulty, accessibility settings, cohort variance and quit behaviour; 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 difficulty curve, failure taxonomy, player-segment diagnosis and validated tuning 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
  • Difficulty-curve and failure 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
{{accessibility_settings}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{attempt_logs}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{checkpoint_rules}}Approved rule, policy or constraint; state owner, version, scope, jurisdiction and effective date
{{completion_times}}Date, time or period value; state ISO format, time zone, start/end boundary and comparison period
{{difficulty_parameters}}Required input value; state source, data type, format, unit, period, market and locale where applicable
{{failure_events}}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
{{level_sequence}}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
{{player_segments}}Target audience, segment, persona, customer/player or industry group
{{resource_rules}}Approved rule, policy or constraint; state owner, version, scope, jurisdiction and effective date
{{success_events}}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_market}}Target market

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 Difficulty-curve and failure analysis in ChatGPT

Open a new ChatGPT chat, paste the filled-in Difficulty-curve and failure 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: