Expert blueprint for educational games including gamification loops (learn/apply/feedback/adapt), progress tracking (student profiles, mastery %), adaptive difficulty (target 70% success rate), spaced repetition, curriculum trees (prerequisite system), and visual feedback (confetti, XP bars). Use for learning apps, training simulations, or edutainment. Trigger keywords: educational_game, gamification, adaptive_difficulty, spaced_repetition, student_profile, curriculum_tree, mastery_tracking.
npx skills add https://github.com/thedivergentai/GD-Agentic-Skills --skill godot-genre-educational
Expert blueprint for educational games that make learning engaging through game mechanics.
Resource scripts (StudentProfile) to decouple student data from the presentation layer for persistence and scalability.Performance.add_custom_monitor() to inject live student metrics into the Godot Editor Debugger.NOTIFICATION_OS_IME_UPDATE for complex character input support (e.g., East Asian).mouse_filter on overlays; strictly set to PASS to prevent invisible containers from silently consuming clicks._process(); strictly update labels ONLY on state change events to save mobile/tablet battery.> MANDATORY: Read the script matching the scenario before implementing. Do not paste quiz/profile tutorials inline.
| Phase | Skills | Purpose |
|-------|--------|---------|
| 1. UI | godot-ui-rich-text, godot-ui-theming | Readable text, drag-and-drop answers |
| 2. Data | godot-save-load-systems | Student profiles, progress tracking |
| 3. Logic | godot-state-machine-advanced | Quiz flow (Question → Answer → Result) |
| 4. Juice | godot-particles, godot-tweening | Making learning feel rewarding |
| 5. Meta | godot-scene-management | Navigating between lessons and map |
| 6. Balance | godot-monte-carlo-balancer | Override bands to ~70% flow / mastery |
Pick the owner script; keep SKILL free of duplicate StudentProfile / quiz_manager paste-ups.
| Need | Decision | MANDATORY script |
|------|----------|------------------|
| Track mastery / XP / badges | One StudentProfile Resource + ConfigFile I/O | student_progress_config.gd |
| Keep learners in flow (~70%) | Windowed success ratio + hint branch | adaptive_difficulty_adjuster.gd |
| Long-term retention | Interval queue (success → longer delay; fail → sooner) | spaced_repetition_scheduler.gd |
| Prerequisite lesson map | Curriculum Resource graph (id + required_topics) — data only, no UI | Peer godot-resource-data-patterns |
| Grade without hitching | Offload scoring | threaded_scoring_engine.gd |
| Classroom a11y | TTS + locale + focus + anchors | tts_manager / dynamic_localization / focus_navigation_manager / adaptive_ui_anchors |
| Live debugger metrics | Performance.add_custom_monitor("edu/...") — no custom dashboards | (inline one-liner OK) |
StudentProfile (single shape): @export mastery Dictionary + XP; emit change signals; persist via student_progress_config.gd. Do not redefine the class twice in this skill.
Quiz curtain: state machine owns Question → Answer → Result → Adapt; spaced-repetition + adaptive-difficulty scripts decide *what* is next — do not inline a full quiz_manager.gd tutorial here.
Learning is hard — reward effort: satisfying SFX, particles on correct, Tweened XP bars. Pedagogue first; juice never substitutes for spaced repetition / ZPD scaffolding.
text_reveal_effect / rich-text meta.> LLM-ignorance rule: If a general agent would not know it before reading, load the reference — never delete expert deltas.
> Progressive disclosure: open Official Documentation links only when researching a specific API; load Related Skills when routing to a peer domain — do not preload the whole lattice.
tr() / locale workflow for classroom multi-language UI without hardcoding strings.process_mode so assessments freeze world logic while UI stays interactive.Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
| Automate Twitter/X with posting, engagement, and user management via inference.sh CLI. social media automation, x automation, tweet scheduler, twitter integration, post tweet, twitter post, x post, send tweet
| Query the Sequence Read Archive (SRA), retrieve scientific publications, and analyze genomics metadata using the SRAgent toolkit. Supports accession conversion (GSE→SRX→SRR), BigQuery metadata queries, manuscript downloads from multiple sources, and scRNA-seq technology identification. Use when working with SRA/GEO datasets, finding publications, or analyzing single-cell sequencing experiments.
Framework for building competitive landscape decks — market positioning, competitor deep-dives, comparative analysis, strategic synthesis. Use when the user asks for a competitive landscape, competitor analysis, peer comparison, market positioning assessment, strategic review, or investment memo deck. Also triggers on "who are the competitors to X", "benchmark X against peers", "build a market map", or any request to systematically evaluate competitive dynamics across an industry.
> Analyzes unit economics by product or service using PayPal merchant insights and QuickBooks cost data, benchmarks against inflation and cost changes, and shows pricing-scenario data (e.g. "a 5% increase historically correlates with ~3% volume drop"). Surfaces analysis only — does not recommend a price. Use when the user asks about raising prices, pricing, margin analysis, what to charge, whether costs are eating into profit, or how a price change might affect their business. Trigger even if the user doesn't say "margin" explicitly — phrases like "am I making enough?", "should I charge more?", or "my costs are going up" all call for this skill.
Define a dataset's metadata profile — infer a Frictionless Table Schema from its data, add Data Package metadata (license, sources, keywords), and write it into datasets.json so the showcase renders a typed field table. Extend or customize via the L0-L3 profile ladder. Use when a registered dataset needs field types, constraints, or catalog metadata before publishing.
Take thedivergentai/godot-genre-educational from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.