# Freud Detection AI

> 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.

- Skill: `egorfedorov/freud-detection-ai` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add egorfedorov/freud-detection-ai`
- Raw SKILL.md: https://api.skillmd.com/api/skills/egorfedorov/freud-detection-ai/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: egorfedorov (https://skillmd.com/u/egorfedorov)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/egorfedorov/freud-detection-ai

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# 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.