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/adoptnpx skills add Donchitos/Claude-Code-Game-Studios --skill adoptgit clone --depth 1 https://github.com/Donchitos/Claude-Code-Game-StudiosWhat 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 | $0.00078 | $0.04331 |
| Opus 5 | $0.00039 | $0.02166 |
| Sonnet 5 | $0.00016 | $0.00866 |
| Haiku 4.5 | $0.00008 | $0.00433 |
Grade A, and why
adopt 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.
How it starts
The opening of the file, as written. The whole thing — 444 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adopt — Brownfield Template Adoption
This skill audits an existing project's artifacts for format compliance with the template's skill pipeline, then produces a prioritised migration plan.
This is not /project-stage-detect.
/project-stage-detect answers: what exists?
/adopt answers: will what exists actually work with the template's skills?
A project can have GDDs, ADRs, and stories — and every format-sensitive skill will still fail silently or produce wrong results if those artifacts are in the wrong internal format.
Output: docs/adoption-plan-[date].md — a persistent, checkable migration plan.
Argument modes:
Audit mode: $ARGUMENTS[0] (blank = full)
- No argument /
full: Complete audit — all artifact types gdds: GDD format compliance onlyadrs: ADR format compliance onlystories: Story format compliance onlyinfra: Infrastructure artifact gaps only (registry, manifest, sprint-status, stage.txt)
Phase 1: Detect Project State
Emit one line before reading: "Scanning project artifacts..." — this confirms the
skill is running during the silent read phase.
Then read silently before presenting anything else.
Existence check
production/stage.txt— if present, read it (authoritative phase)design/gdd/game-concept.md— concept exists?design/gdd/systems-index.md— systems index exists?- Count GDD files:
design/gdd/*.md(excluding game-concept.md and systems-index.md) - Count ADR files:
docs/architecture/adr-*.md - Count story files:
production/epics/**/*.md(excluding EPIC.md) .claude/docs/technical-preferences.md— engine configured?docs/engine-reference/— engine reference docs present?- Glob
docs/adoption-plan-*.md— note the filename of the most recent prior plan if any exist
Infer phase (if no stage.txt)
Use the same heuristic as /project-stage-detect:
- 10+ source files in
src/→ Production - Stories in
production/epics/→ Pre-Production - ADRs exist → Technical Setup
- systems-index.md exists → Systems Design
- game-concept.md exists → Concept
- Nothing → Fresh (not a brownfield project — suggest
/start)
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 · 444 lines · 78 tokens per session scan A 58959c4f76c0
adopt is a skill published in the GitHub repository Donchitos/Claude-Code-Game-Studios (24,702 stars, last pushed 3mo ago), licensed MIT. It adds 78 tokens to every session and 4,331 once invoked, about $0.0004 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 2026-08-30.
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.
paranoia-ai-system-evolver
用于升级 AI 系统、agent workflow、Codex skill、prompt、memory、RAG、tool routing、schema、eval set 或 feedback loop；也用于把 AI 工作单从指令单升级为意图单，并对研究、检索、测试和 AI 对话做 VOI 决策门审计。需要 Intent Work Order、WOOP 任务准入、决策对象、VOI/EVPI/EVSI、UL（Uncertainty Ladder，不确定性阶梯）、OODA、eval、Human Gate、versioning 与 rollback 的受控演化时使用。Use when controlled AI system…
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…