Agent Skills
A collection of resources, APIs, and standards related to AI agent skills and capabilities. Agent skills represent the tools, functions, and capabilities that AI agents can invoke to accomplish tasks — spanning web search, code execution, file management, memory, and external API integrations. This topic covers the major platforms and frameworks that define how agent skills are declared, discovered, and invoked.
Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.
API Evangelist profiles Agent Skills the way a machine reads it — 40 machine-readable artifacts across 3 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Agent Skills scores 38.3/100 (thin), with a separate agent-readiness read of 7/100 (human only). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Agent Skills
Each block below is one kind of artifact we hold for Agent Skills. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 3
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Anthropic Tool Use API
The Anthropic Tool Use API allows AI agents built on Claude to call client-defined functions or Anthropic-provided server tools such as web search, code execution, and web fetch...
Google Agent Development Kit (ADK)
Google's Agent Development Kit (ADK) is a flexible framework for building AI agents and multi-agent systems. It supports LLM agents, workflow agents, and custom agents with capa...
Model Context Protocol (MCP)
The Model Context Protocol (MCP) is an open-source standard for connecting AI applications to external systems. MCP defines a standardized way for AI agents to access data sourc...
Pricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Agent Skills Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Agent Skills Finops
FINOPSFeatures 6
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
Function Calling
AI agents can invoke user-defined or platform-provided functions based on natural language instructions, with structured input/output schemas.
Server-Side Tool Execution
Platforms like Anthropic and OpenAI run certain agent skills (web search, code execution) on their own infrastructure, removing the need for client-side execution.
MCP Integration
The Model Context Protocol provides a universal adapter layer enabling agents to discover and call any MCP-compatible server as a skill.
Multi-Agent Orchestration
Frameworks like Google ADK support coordinating multiple specialized agents, with skills delegated across agent boundaries via protocols like A2A.
Strict Schema Enforcement
Agent skill definitions can enforce strict JSON Schema compliance to ensure agents produce well-formed tool calls matching the declared parameter schema.
Tool Discovery
Anthropic's tool_search server tool enables agents to discover available tools at runtime without statically declaring all tool schemas upfront.
Semantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Agent Skills Context
JSON-LDSpectral Rules 1
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Agent Skills API Rules
SPECTRALJSON Schema 4
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
JSON Structure 4
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Agent Skills Mcp Server Structure
JSON STRUCTUREAgent Skills Tool Call Structure
JSON STRUCTUREAgent Skills Tool Result Structure
JSON STRUCTUREAgent Skills Tool Structure
JSON STRUCTUREExamples 4
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Agent Skills Tool Example
EXAMPLESecurity Posture 1
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Use Cases 6
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Automated Research
Agents use web search and fetch skills to retrieve, synthesize, and summarize information from the internet in response to user queries.
Code Generation and Execution
Agents invoke code execution skills to write, run, and debug code within sandboxed environments, returning results to the user.
Data Integration
Agents use OpenAPI-backed skills to read and write data across enterprise systems — CRMs, ERPs, databases — through standardized API calls.
File and Document Management
Agents invoke file system skills to read, write, and organize documents, images, and structured data on behalf of users.
Multi-Step Workflow Automation
Agents chain multiple skills in sequence — searching, retrieving, transforming, and storing data — to complete complex multi-step tasks autonomously.
AI-Assisted Customer Support
Customer service agents use CRM lookup, ticketing, and knowledge base skills to resolve customer issues without human escalation.
Integrations 7
Pre-built integrations with other platforms tell you where this provider already fits in a stack.
Pre-built integrations with other platforms and tools.
Claude (Anthropic)
Native support for tool use and MCP via the Anthropic Messages API.
ChatGPT (OpenAI)
Function calling and MCP tool integration via the OpenAI Responses API.
Gemini (Google)
Tool use and ADK integration for Gemini-based agents.
VS Code Copilot
GitHub Copilot supports MCP servers as agent skill providers within the VS Code development environment.
Cursor
Cursor IDE supports MCP tool integration for AI-assisted coding agents.
LangChain
Open-source framework for composing agent skills into chains and graphs across multiple LLM providers.
LlamaIndex
Data framework enabling agents to index and retrieve from external data sources as structured skills.
Scroll within the panel for all 7 ·
Resources
Every other property we hold for Agent Skills — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Documentation 4
Reference material describing how the API behaves
Design & Contract 2
Pagination, idempotency, versioning, errors, and events
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 1
Authentication, authorization, and security posture
← All providers · Data indexed from github.com/api-evangelist/agent-skills · machine-readable index on apis.io