pojia-game-security

Files (8)

• SKILL.md
• openai.yaml · agents/
• anti-cheat-design.md · references/
• cheat-architecture.md · references/
• engine-security.md · references/
• incident-analysis.md · references/
• integrity_manifest.py · scripts/
• telemetry_analyze.py · scripts/

SKILL.md (first lines)

# Pojia Game Security

Analyze the abuse path from trust boundary to observable behavior, then implement detection and hardening.

## Start

1. Identify engine, platform, architecture, network model, authoritative state, anti-cheat components, and available artifacts.
2. Classify the technique: external memory, injected/internal, input automation, overlay/ESP, runtime patch, packet/protocol, asset/config, kernel/driver, DMA/hardware, or account/economy abuse.
3. Map required access, modified state, data sources, persistence/lifecycle, and observable artifacts.
4. Reproduce only the minimum behavior needed to validate detection or a defensive hypothesis.

## Select references

- Cheat categories, data flows, and observables: read `references/cheat-architecture.md`.
- Anti-cheat architecture and control placement: read `references/anti-cheat-design.md`.
- Unity/IL2CPP and Unreal analysis: read `references/engine-security.md`.
- Investigation and evidence: read `references/incident-analysis.md`.

## Tools

- Use `scripts/integrity_manifest.py` to create or verify signed-off file hash manifests.
- Use `scripts/telemetry_analyze.py` to summarize player/input telemetry and flag explainable anomaly indicators.
- Combine with `$pojia-reverse`, `$pojia-memory`, and `$pojia-lab` for binaries, runtime state, and evidence handling.

## Deliver

Return the technique classification, trust-boundary failure, required capabilities, observables, reproduction harness, integrity/telemetry checks, false-positive considerations, mitigations, and retest plan. Separate a detection hypothesis from a confirmed cheat artifact.

From z91772524-ai/pojia-next-mac, MIT.

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