34315ab7d7The stable Skills page missed the final SEP's Agent Skills compatibility clause, and its examples used abbreviated hashes and inconsistent byte sizes. Archived drafts also retained current-sounding source-of-truth guidance. This corrects the stable page directly to match already-established requirements: restores the compatibility clause, shows the resources capability and server/discover, explains omitted request metadata, and makes the example manifests and content consistent. It also labels archived documents as historical and fixes the SEP domain in AGENTS.md. This is an editorial alignment, not a new extension proposal; #142 remains separate. Validation: all JSON examples parsed; all 13 manifest entries checked against sample bytes for SHA-256 and size; the corrected specification builds with the VitePress publishing site; git diff --check passed. AI assistance: prepared with Codex.
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Problem Statement
Historical document. Frozen during development of SEP-2640; statements of current status and requirements below describe that draft-era context. See the stable specification for current requirements and the archive index for context.
Agent Skills support in AI applications demonstrates demand for rich workflow instructions, but there's no convention for exposing equivalent functionality through MCP primitives.
Current Limitations
- Server instructions load only at initialization — new or updated skills require re-initializing the server
- Complex workflows exceed practical instruction size — some skills require hundreds of lines of markdown with references to bundled files, scripts, and examples
- No discovery mechanism — users installing MCP servers from a registry don't know if there's a corresponding skill they should also install
- Multi-server orchestration — skills may need to coordinate tools from multiple servers, which doesn't fit the single-server instruction model
Broader Context
Skills discoverability is a specific instance of a more general MCP challenge: context-as-resources discoverability and standardization of client host behavior around non-tool primitives. The ecosystem has largely optimized for tools, and patterns for how clients discover and consume other forms of context — including skills — remain underdeveloped. Solutions in this space are likely to have implications beyond skills alone.
MCP's value for skills goes beyond distribution; it provides an interaction model. MCP defines app, human, and assistant roles, giving skills a built-in framework for control model decisions (who sees the content, who decides when it loads). This makes MCP a natural complement to skills as a delivery channel for workflow instructions.
Key Use Cases
See use-cases.md for detailed use cases and community input. In summary:
- Complex Workflow Orchestration — Multi-step workflows requiring 875+ lines of instruction (e.g., mcpGraph)
- Conditional Workflows — Branching instructions dynamically loaded based on context
- Multi-Server Composition — Skills leveraging tools from multiple off-the-shelf servers
- Progressive Disclosure — Skills broken into linked file sets, loaded on demand
Open Questions
See open-questions.md for the full list of unresolved questions with community input.
For the value proposition and a guide on when MCP distribution applies, see why-and-when.md.