awesome-game-security is a curated collection focused on security research for games and game software, including cheating, anti-cheat systems, debugging, and anti-debugging. It is intended for security researchers and developers studying or building game-protection tools. The catalogue entries provide skills for AI coding agents related to the repository’s security workflows.
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 gmh5225/awesome-game-security --skill research-rigorgit clone --depth 1 https://github.com/gmh5225/awesome-game-securityWrote 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/gmh5225/awesome-game-security/research-rigor)<a href="https://agentmods.dev/skills/gmh5225/awesome-game-security/research-rigor"><img src="https://agentmods.dev/badge/skills/gmh5225/awesome-game-security/research-rigor/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/gmh5225/awesome-game-security/research-rigor"><img src="https://agentmods.dev/badge/skills/gmh5225/awesome-game-security/research-rigor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00114 | $0.03037 |
| Opus 5 | $0.00057 | $0.01519 |
| Sonnet 5 | $0.00023 | $0.00607 |
| Haiku 4.5 | $0.00011 | $0.00304 |
Grade A, and why
game-security-research-rigor 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 — 292 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Game Security Research Rigor
Purpose
Use this skill with the relevant domain skill. Its job is to keep conclusions no stronger than the evidence and to make factual, empirical, and operational claims independently checkable.
Use skill evaluation for catalog routing, coexistence, and answer-quality assessment. Use robustness and triage for owned-build diagnostics and regression evidence.
For collection-specific source lineage, archive completeness and conflicting project descriptions, use repository evidence reconciliation. For exact local locations, use repository navigation.
Separate the reasoning layers
Never collapse these layers:
- Observation — the raw artifact or measurement.
- Finding — a rule, baseline, or invariant was violated.
- Attribution — a hypothesis about cause or actor intent.
- Decision — a risk-based response to the supported conclusion.
An anomaly, hash mismatch, or invariant violation establishes a finding only under the stated measurement assumptions. It does not by itself prove cheating, malicious intent, or the responsible actor.
Research workflow
- Scope the question
- Identify object, platform, game/build, mode, timeframe, trust boundaries, available evidence, and the consequence of a wrong conclusion.
- State what is outside scope.
- Acquire evidence
- Prefer primary artifacts for version-specific behavior: source code, specifications, raw telemetry, traces, binaries, and official changelogs.
- Use peer-reviewed or independently reproduced work for generalization.
- Treat vendor posts and community reports as claim-bearing sources, not automatic proof.
- Verify every citation
- Confirm the URL or DOI resolves.
- Match title, authors, venue, and year to authoritative metadata.
- Read enough of the source to confirm it supports the exact claim.
- A venue name, search result, bibliography entry, or source count is not evidence by itself.
- Build a claim ledger
- Record claim, supporting artifact, source/version/date, method, assumptions, counterevidence, uncertainty, and remaining verification work.
- Label statements as observed, reproduced, sourced, inferred, or unknown.
- Test alternatives
- Look for benign explanations, measurement error, stale schemas, version drift, selection bias, and contradictory evidence.
- Reproduce and validate
- Preserve inputs, transforms, tool/model versions, configuration, timestamps, and commands needed to reproduce the result.
- Re-run against negative controls and changed conditions.
- Conclude narrowly
- Use one of: supported, suspicious, no signal observed within scope, or inconclusive.
- Never turn missing data into a clean result.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed · +128 lines · +52 tokens per session b709b06399f4
- 11d ago First seen · 164 lines · 62 tokens per session scan A 8071fee846bb
game-security-research-rigor is a skill published in the GitHub repository gmh5225/awesome-game-security (3,499 stars, last pushed today), licensed MIT. It adds 114 tokens to every session and 3,037 once invoked, about $0.0006 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.
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