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 skills add Donchitos/Claude-Code-Game-Studios --skill test-flakinessgit 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/test-flakiness)<a href="https://agentmods.dev/skills/donchitos/claude-code-game-studios/test-flakiness"><img src="https://agentmods.dev/badge/skills/donchitos/claude-code-game-studios/test-flakiness/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/donchitos/claude-code-game-studios/test-flakiness"><img src="https://agentmods.dev/badge/skills/donchitos/claude-code-game-studios/test-flakiness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 7 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 5.1 | $0.00027 | $0.02596 |
| Opus 5.5 | $0.00011 | $0.01038 |
| Sonnet 5.5 | $0.00005 | $0.00519 |
| Haiku 4.5 | $0.00003 | $0.00260 |
Grade B, and why
test-flakiness scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Enumerates other installed skillsmediumAgent snooping
Other skills' SKILL.md files reveal prompts, capabilities and secrets that should be invisible to peers.
allowed-tools: Read, Glob, Grep, Write, Edit, Bash(ls -t *), Bash(bash "*/.claude/skills/test-flakiness/../../hooks/yaml-helper.sh" resolve_config *) Copies of this mod
2 near-identical copies found in the catalogue:
- test-flakiness — 98% identical, 70 lines differ
- test-flakiness — 98% identical, 70 lines differ
How it starts
The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
!bash "${CLAUDE_SKILL_DIR}/../../hooks/yaml-helper.sh" resolve_config --keys automation
Automation mode: Resolve modes.automation (project.local.yaml →
project.yaml → default collaborative). Every AskUserQuestion call and
every file write follows .claude/docs/automation-modes.md
(collaborative asks always · guided major-only · autonomous logs and proceeds;
automation_always_ask categories always prompt).
Test Flakiness Detection
A flaky test is one that sometimes passes and sometimes fails without any code change. Flaky tests are worse than no tests in some ways — they train the team to ignore red CI runs, masking genuine failures. This skill identifies them, explains likely causes, and recommends whether to quarantine or fix each one.
Output: Updated tests/regression-suite.md quarantine section + optional
production/qa/flakiness-report-[date].md
When to run:
- Polish phase (tests have had many runs; statistical signal is reliable)
- When developers start dismissing CI failures as "probably flaky"
- After
/regression-suiteidentifies quarantined tests that need diagnosis
1. Parse Arguments
Modes:
/test-flakiness [ci-log-path]— analyse a specific CI run log file/test-flakiness scan— scan all available CI logs in.github/or standard log output directories/test-flakiness registry— read existing regression-suite.md quarantine section and provide remediation guidance for already-known flaky tests- No argument — auto-detect: run
scanif CI logs are accessible, elseregistry
2. Locate CI Log Data
Option A — GitHub Actions (preferred)
Check for test result artifacts:
ls -t .github/ 2>/dev/null
ls -t test-results/ 2>/dev/null
For Godot projects: GdUnit4 outputs XML results compatible with JUnit format,
under reports/ (its default report folder, res://reports/). Check reports/,
and any test-results/ you saved runs into, for .xml files.
For Unity projects: game-ci test runner outputs NUnit XML to test-results/
by default.
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.
- 2d ago Changed scan A → B 382ee117ffbc
- 22d ago Changed · +29 lines 01c9988360bb
- 27d ago Changed 9f8ff1192c53
- 28d ago Changed · +8 lines · -32 tokens per session 527069fc014d
- 1mo ago First seen · 212 lines · 59 tokens per session scan A 3849299d9c0b
test-flakiness is a skill published in the GitHub repository Donchitos/Claude-Code-Game-Studios (27,059 stars, last pushed 3d ago), licensed MIT. It adds 27 tokens to every session and 2,596 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it B with 1 finding (enumerates other installed skills). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
systematic-debugging
4-phase root cause debugging: understand bugs before fixing.
module-regression
Govern cross-module regression in large repositories with a REGRESSION.md ledger that records each module's downstream consumers and executable acceptance command. After a change, run the changed module and every affected downstream command, and use observed exit codes as regression evidence under the project's…
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
maintain-formal-verification-skill
Bring a project's formal verification back in line with the code after the code changes: find the commits that touched verified code, update the model, harness, or properties to describe the code as it is now, rerun, and say whether each failure is drift in the verification or a bug in the code. Use for…
triage-issue
Bug triage: explores codebase for root cause, files GitHub issue with TDD fix plan. Triggers: triage, investigate bug, fix plan, root cause, file issue, bug report.
prevent-recurrence
Use when correcting an issue or failure reveals a reusable prevention lesson, including code, setup, measurement, validation, workflow, agent mistakes, or accepted code-review findings; when someone says "this keeps happening" or asks how to stop it; or in post-incident follow-up. Fixes the instance (failing-first…