agent-skills provides an open, decentralized specification for distributing tools to LLM agents. This approach is designed to be token-efficient, transparent, and immutable, allowing for a scalable model where skills are defined as SKILL.md files and served via CDN, enhancing the capability of LLMs without overloading context windows.
This repository presents agent-skills, an open and decentralized specification for distributing tools to Large Language Model (LLM) agents. This framework serves as a token-efficient, transparent, immutable, and web-native alternative to traditional models such as MCP.
The core innovation behind agent-skills is the shift from tool data injection to a retrieval model. Rather than loading multiple tools into an agent's context at session start (which can overwhelm the context window), tools are described as SKILL.md files that are hosted at stable URLs. These files are typically stored in a Git repository and served via a Content Delivery Network (CDN). This method allows agents to dynamically discover tools through vector search as needed, maintaining a consistent token cost regardless of the catalog size.
agent-skills operates through a structured architecture that includes:
The specification is designed to be implementation-agnostic. Infrastructure for usable skill banks can be built on any suitable platform (for example, using filesystem storage paired with vector indexing). A reference runtime is outlined in the related project, just-bash-data, though several implementations are possible, including those built on SQLite, Postgres, or in-memory stores.
Here’s a brief example demonstrating how to subscribe to a skill pack:
# Subscribe to a skill pack (pinned to a hash for immutability)
db skill_subscriptions insert '{
"_id": "stripe-skills",
"source_type": "git",
"repo": "github.com/stripe/agent-skills",
"ref_requested": "v1.2.0",
"ref_resolved": "a1b2c3d4e5f67890abcdef1234567890abcdef12",
"auto_update": false,
"verify_signature": true,
"trusted_keys": ["B5A4 9C28 D9F1 ..."]
}'
For detailed implementation guidance, the repository includes an IMPLEMENTATION.md file that covers various aspects necessary to set up and run a fully functional agent skill bank.
agent-skills aims to transform how LLM agents interact with tools by offering a systematic, efficient, and secure way to distribute and manage skills. Interested parties are encouraged to explore the specifications, implementations, and community contributions on this GitHub page.
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