Observed Signal · Oct 12, 2026 · Market Signal · Source: GitHub, Inc. · Impact: 2/5
GitHub Copilot app for Beginners: How to teach Copilot about your codebase
Learn how to add custom instructions and skills to your repository, use custom agents, and connect MCP servers like Playwright.
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MCP vs Agent Skills: Decision Framework
The article explains the difference between Model Context Protocol (MCP) and AI Agent Skills (SKILL.md) and provides a decision framework for context engineering. MCP is described as an open standard and client-server JSON-RPC bridge that gives LLM-based agents live access to external systems by exposing Resources, Tools, and Prompts. Agent Skills encode repeatable, static procedures as files (typically SKILL.md) that load via progressive disclosure when a task matches. MCP is appropriate when tasks require live external state; Skills are appropriate for repeatable, static knowledge. Production-grade agents typically need both: MCP for "what's actually true right now" and Skills for "how to act consistently." The article includes examples (e.g., support agents using Stripe and Zendesk via MCP plus a refund-policy Skill) and summarizes practical trade-offs such as infrastructure requirements, context costs, and portability.
Wiring an MCP Server to watsonx Orchestrate Agent
A developer tutorial published on DEV Community (2026-06-21) describes wiring an MCP server to an IBM watsonx Orchestrate agent. The author, Jabulani Ndlovu, built a beginner weather agent using the watsonx Orchestrate ADK where the agent invokes tools through an MCP toolkit rather than calling APIs directly. The MCP server exposes tool endpoints (e.g., get_current_weather, get_weather_forecast) and proxies calls to the Open‑Meteo Weather API, allowing the agent to be defined in a single YAML file without code changes when swapping or adding tools. The post argues MCP decouples agents from tool implementations, simplifying tool reuse across agents and suggesting an architectural pattern for scaling agent toolchains in larger deployments.
Build a Tested Agent Skill with SKILL.md
A developer tutorial demonstrates a pattern for building installable AI agent "skills": place workflow, intent, and safety boundaries in SKILL.md, and move deterministic, repeatable checks into small local scripts (example: a commit-crafter skill). The guide shows a Python validator with a pure validate(message) API, a CLI that uses exit codes (0 = pass, 1 = validation issues, 2 = no input), and unit tests running on the Python standard library. Examples and commands are verified against the repository's main branch and the article references the Agent Skills specification for discovery conventions.
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