Safe local incident simulations for AI agents, MCP tools, and agent-generated code.
Project description
ai-incident-lab
Languages: English | 中文
Runnable incident simulations for AI agents, MCP tools, and agent-generated code.
Status
v0.2.1 - safe-local scenario CLI, bundled scenario pack, and first-run init flow.
Purpose
Create safe local workshops and regression scenarios that make Safe Agent Operations concrete.
First Production Surface
Local-only incident scenarios mapped to X-One tools, expected findings, cleanup steps, and reviewer lessons.
After PyPI publication:
python3 -m pip install xone-ai-incident-lab
ai-incident-lab init --output ai-incident-scenarios
ai-incident-lab list --scenarios ai-incident-scenarios
ai-incident-lab validate --scenarios ai-incident-scenarios
ai-incident-lab render --scenarios ai-incident-scenarios --format markdown --output ai-incident-runbook.md
ai-incident-lab render --scenarios ai-incident-scenarios --format json --output ai-incident-runbook.json
After Homebrew tap update:
brew install x-one-ai/tap/ai-incident-lab
ai-incident-lab --version
For local development:
python3 -m pip install -e '.[dev]'
python3 -m pytest tests -q
ai-incident-lab validate --scenarios scenarios
Required Evidence
- scenario README
- safe reproduction steps
- expected finding mapping
- cleanup instructions
- teaching notes
Scenario Contract
Scenarios use ai-incident-lab.scenario.v1 and must remain safe-local. They are review exercises, not exploit kits or runtime protection.
Non-Goals
- no real exploit kit
- no hosted sandbox first
- no unsafe secret-bearing fixtures
OPT Operating Model
This project references the shared One Person Team workflow through ops/opt-overlay.md. Project-specific constraints live under ops/constraints, and evolvable local skills live under ops/skills.
Blocked Inputs
Inputs that require user or real-world data are recorded in ../x-one-skipped-inputs.md and should not block foundation work.
Docs
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