freud-detection-ai

name	freud-detection-ai
description	Design and validate AI-driven anomaly/fraud-style detection workflows for game operations. Use when defining signal features, model scoring thresholds, investigation routing, or validating detection pipeline reliability and false-positive controls.

Freud Detection AI

Use this skill to implement anomaly-detection workflows with explainable gating and review paths.

Workflow

1. Define scope and constraints.

• Define detection scope, signals, threshold policy, and review SLA.
• Capture objective metrics, bounds, and release blockers.

2. Design implementation plan.

• Design model-scoring flow, fallback heuristics, and escalation rules.
• Keep ownership and dependency boundaries explicit.

3. Execute and iterate.

• Implement in small, traceable increments.
• Record run/build context for reproducibility.

4. Validate contract integrity.

• Validate threshold outcomes, alert quality, and investigation traceability.
• Treat contract breaches as blockers.

5. Prepare handoff.

• Deliver detector configuration diff, alert routing updates, and runbook.
• Include exact commands and acceptance criteria.

Output Contract

Return:

1. Context: goals, assumptions, constraints.
2. Validation: pass/fail checks and key deltas.
3. Changes: concrete file-level updates.
4. Commands: commands and expected outputs.
5. Risks: unresolved issues and limits.

References

• references/workflow.md: detailed execution flow.
• references/checklist.md: sign-off checklist.

Execution Rules

• Keep decisions measurable and reversible.
• Keep validation criteria explicit before iteration.
• Escalate unbounded false-positive risk and opaque scoring logic as blockers.