No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by YSLAB-ai · Codex Skill · ★ 296
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is scenario-lab safe to install? View the security audit →
Scenario Lab 🌐 Languages: English 日本語 Experimental preview: Monte Carlo simulation for real-world events you can run locally with Codex or Claude. Scenario Lab is a Monte Carlo simulation engine for real-world events such as regional conflict, markets, politics, and company decision-making. Version: public preview. Contributors: CONTRIBUTORS.md. What Is It Scenario Lab turns a developing situation into a structured simulation run. You give it the actors, the current development, and the evidence you want to approve. It then explores many branching futures with Monte Carlo tree search and ranks the branches it finds. At a high level, the engine works like this: A domain pack defines the actors, phases, and action space for a type of event such as an interstate crisis, a market shock, or a company decision. The approved evidence packet and the case framing are compiled into a belief state with actor behavior profiles and domain-specific fields. The simulation engine runs over that state, proposing actions, sampling transitions, and scoring resulting branches. Reports turn the searched branches into readable outcomes, scenario families, and calibrated confidence labels.
| Stars | 296 |
| Forks | 29 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 62.6995696076001/100 |
| Open Issues | 2 |
| Last Updated | 2026-04-28 |
| Created | 2026-04-20 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~325k |
These tools work well together with scenario-lab for enhanced workflows:
Looking for a scenario-lab alternative? If you're comparing scenario-lab with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
AI gets the context. Not your private data. Local-first privacy proxy for browser chat, AI APIs, and coding ag
Open-source cross-agent memory layer for coding agents via MCP. Compatible with Claude Code, Codex, Cursor, Wi
MaverickMCP - Personal Stock Analysis MCP Server
The memory + security kernel for AI agents. Strata: a signed append-only log, receipts on every write, exact-h
Overture is an open-source, locally running web interface delivered as an MCP (Model Context Protocol) server
Manage your Hevy workouts, routines, folders, and exercise templates. Create and update sessions faster, organ
Explore other popular codex skill tools:
scenario-lab is Experimental local-first scenario analysis for natural-language forecasting with Codex or Claude. It is categorized as a Codex Skill with 296 GitHub stars.
scenario-lab is primarily written in Python. It covers topics such as agent-tools, claude, codex.
You can find installation instructions and usage details in the scenario-lab GitHub repository at github.com/YSLAB-ai/scenario-lab. The project has 296 stars and 29 forks, indicating an active community.
scenario-lab is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to scenario-lab on Agent Skills Hub include pasteguard, memorix, maverick-mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: