Initialize a SWARM Research OS vault with directory structure and templates.
npx skills add https://github.com/swarm-ai-safety/swarm --skill vault-init
Please help me install this Agent Skill. Skill: vault-init Source: https://github.com/swarm-ai-safety/swarm/tree/main/.skills/vault-init Command: npx skills add https://github.com/swarm-ai-safety/swarm --skill vault-init
This Skill automates the setup of a structured knowledge management system (Research OS vault) for AI safety research, ensuring consistency and discoverability of critical research artifacts.
vocabulary.yaml, categories.yaml), and schema-enforced templates for various research note types (experiments, claims, governance, etc.).run.yaml metadata, ensuring all historical data is integrated into the new vault structure.Initialize a SWARM Research OS vault in the current directory.
High-intent search queries and answers about installing and using this skill.
To set up an AI safety research vault, this Skill initializes a standardized directory structure, installs schema-enforced note templates, and generates a master index for immediate knowledge management and documentation.
Creating standardized research note templates is done by initializing the vault, which installs schema-enforced templates for various research note types including experiments, claims, and governance mechanisms to ensure consistency.
You can backfill existing research run data by using the optional metadata backfilling feature, which processes historical research runs to generate run.yaml metadata and integrates them into the new vault structure.
No external dependencies are required to initialize a SWARM Research OS vault, as the Skill relies solely on its internal scripts and references to generate directories, templates, and validation configurations.
When initializing a research knowledge vault, the setup generates essential configuration files including vocabulary.yaml and categories.yaml, alongside a master index for vocabulary and category mappings.
You can use this initialization for both new and existing research projects; it creates a standardized structure for new work and optionally backfills historical run data with run.yaml metadata for existing projects.
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