Claude Code Game Studios is a setup that organizes Claude Code into a coordinated game-development team of specialized AI agents. It supports game projects across design, programming, art, audio, narrative, quality assurance, and production, with skills and workflows for coordinating that work.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/donchitos/claude-code-game-studios/playtest-reportnpx skills add Donchitos/Claude-Code-Game-Studios --skill playtest-reportgit clone --depth 1 https://github.com/Donchitos/Claude-Code-Game-StudiosWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/donchitos/claude-code-game-studios/playtest-report)<a href="https://agentmods.dev/skills/donchitos/claude-code-game-studios/playtest-report"><img src="https://agentmods.dev/badge/skills/donchitos/claude-code-game-studios/playtest-report.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00035 | $0.01270 |
| Opus 5 | $0.00017 | $0.00635 |
| Sonnet 5 | $0.00007 | $0.00254 |
| Haiku 4.5 | $0.00003 | $0.00127 |
Grade A, and why
playtest-report scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
3 near-identical copies found in the catalogue:
- playtest-report — 98% identical, 1 lines differ
- playtest-report — 94% identical, 5 lines differ
- playtest-report — 91% identical, 7 lines differ
How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Phase 1: Parse Arguments
Resolve the review mode (once, store for all gate spawns this run):
- If
--review [full|lean|solo]was passed → use that - Else read
production/review-mode.txt→ use that value - Else → default to
lean
See .claude/docs/director-gates.md for the full check pattern.
Determine the mode:
new→ generate a blank playtest report templateanalyze [path]→ read raw notes and fill in the template with structured findings
Phase 2A: New Template Mode
Generate this template and output it to the user:
# Playtest Report
## Session Info
- **Date**: [Date]
- **Build**: [Version/Commit]
- **Duration**: [Time played]
- **Tester**: [Name/ID]
- **Platform**: [PC/Console/Mobile]
- **Input Method**: [KB+M / Gamepad / Touch]
- **Session Type**: [First time / Returning / Targeted test]
## Test Focus
[What specific features or flows were being tested]
## First Impressions (First 5 minutes)
- **Understood the goal?** [Yes/No/Partially]
- **Understood the controls?** [Yes/No/Partially]
- **Emotional response**: [Engaged/Confused/Bored/Frustrated/Excited]
- **Notes**: [Observations]
## Gameplay Flow
### What worked well
- [Observation 1]
### Pain points
- [Issue 1 -- Severity: High/Medium/Low]
### Confusion points
- [Where the player was confused and why]
### Moments of delight
- [What surprised or pleased the player]
## Bugs Encountered
| # | Description | Severity | Reproducible |
|---|-------------|----------|-------------|
## Feature-Specific Feedback
### [Feature 1]
- **Understood purpose?** [Yes/No]
- **Found engaging?** [Yes/No]
- **Suggestions**: [Tester suggestions]
## Quantitative Data (if available)
- **Deaths**: [Count and locations]
- **Time per area**: [Breakdown]
- **Items used**: [What and when]
- **Features discovered vs missed**: [List]
## Overall Assessment
- **Would play again?** [Yes/No/Maybe]
- **Difficulty**: [Too Easy / Just Right / Too Hard]
- **Pacing**: [Too Slow / Good / Too Fast]
- **Session length preference**: [Shorter / Good / Longer]
## Top 3 Priorities from this session
1. [Most important finding]
2. [Second priority]
3. [Third priority]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 148 lines · 35 tokens per session scan A 708a99ffe099
playtest-report is a skill published in the GitHub repository Donchitos/Claude-Code-Game-Studios (24,800 stars, last pushed 3mo ago), licensed MIT. It adds 35 tokens to every session and 1,270 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
game-design-proposal-writer
将调研、概念架构、体验诊断、验证计划、脑图/xmind/系统设计图、已有策划案和生产约束收束为商业游戏策划案、独立游戏设计案、立项评审稿、发行/投资 pitch 或 vertical slice 设计文档。适用于一句话创意先经 game-concept-architect 生成概念契约后再成案、玩法/系统脑图转可落地策划案、审核并改进已有策划案。强调证据边界、受众与商业适配、scope gate、里程碑、风险、决策请求和下一步投入条件；不替代上游创意生成或体验诊断。.
game-concept-architect
将一句话游戏创意扩展为可验证的游戏设计蓝图：从 concept seed、玩家动词、design nucleus options、动作-目标对齐、assumption ledger、玩家承诺、核心循环、scope gate 到 validation plan。适用于真实项目、私有项目、客户项目、公开案例或 synthetic cases；仓库示例发布规则由 CONTRIBUTING.md 管理。.
game-experience-density-optimizer
当用户需要把游戏体验浓度、留存、首局节奏、Demo 完成率、单机总旅程、D1/D7、反馈、具身感、氛围、认知负荷、最佳刺激窗口、FEP/free-energy、预测误差、Markov blanket、习惯化或 liveops 参与问题，编译成可上线、可埋点、可复盘、可回滚的一周 ED 实验包时使用。Use when converting game experience-density and engagement problems into rollback-ready ED experiments.
game-art-direction
Direct game art and creative vision. Turn GAMEDESIGN into a production-level ARTDIRECTION defining a recognizable visual style, camera and composition, world and character grammar, functional colour/light/material, HUD feedback, motion and transition specs, audio direction, and a signature moment for every screen and…
game-build
Build a risk-matched whitebox or the approved production game for its target runtime. Turn GAMEDESIGN, and ARTDIRECTION when production begins, into a minimal BUILDBRIEF and a runnable candidate that can be iterated with replayable evidence. Use for prototype the riskiest design question, implement the approved game…
novel-game-analyze
Deconstruct a novel for game adaptation. Compress a raw novel, deconstruction library, or writing project into a SOURCEBIBLE with cited textual evidence, extracting world rules, player verbs, spaces, character will, systems, and visual anchors — without inventing a genre yet. Use for gameable book analysis, analyze a…