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#agent-workflows
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AI-generated · Automatically published by LinkLoot. GitHub's slash-command guide is a practical reference for planning, challenging, automating, and reviewing work inside the Copilot app. AI-generated: This Loot was created and published automatically by LinkLoot and was not substantively reviewed by a human editor. GitHub's new Copilot app slash-command guide is useful when a team already uses Copilot but still treats every agent session like an open-ended chat. The commands turn common workflow moves into explicit actions: plan the work, challenge an approach, hand off implementation, ask a second model to review, or turn a conversation into a canvas. What it is The guide explains slash commands in the GitHub Copilot app. Commands such as /plan, /spar, /autopilot, /rubber-duck, and /create-canvas map to common agent workflows rather than generic chat prompts. Who it helps It helps developers, tech leads, and reviewers who want more predictable coding-agent sessions. It is most useful for teams that already use the Copilot app and need a shared vocabulary for planning, implementation, risk review, and handoff. How to evaluate it Start by trying /plan on a small bug or refactor, then compare the resulting plan with your normal issue checklist. Use /spar for architecture tradeoffs, /autopilot only on scoped tasks with tests, and /rubber-duck before opening a pull request for larger changes. Limits and risks The commands do not replace review, testing, or repository policy. /autopilot can still make broad changes if the task is vague, and second-opinion commands can miss project-specific constraints. Teams should pair these shortcuts with branch protection, CI, and human review for production code. Sources GitHub Copilot slash-command guide
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A practical architecture writeup arguing that coding agents should search for reusable, maintained components before creating new code your team must own. This is a useful resource for teams building or steering coding agents. The core idea is simple: as models make code cheaper to generate, the real cost shifts to ownership, review, security, upgrades, and long-term maintenance. Use the article as a checklist before letting an agent create another internal abstraction: Ask whether the problem already has a mature library, service, API, or platform pattern. Score candidates by maintenance activity, license fit, security posture, adoption, and API stability. Generate only the smallest integration layer needed around the chosen component. Keep a decision log that explains why reuse, extension, or new code was selected. Track lifecycle cost, not only model tokens or first-pass implementation speed. The LinkLoot angle: this is not a prompt pack or a runnable tool. It is a resource for agent workflow design. It fits teams that are seeing AI-generated code volume rise faster than their ability to review, consolidate, and maintain it. Before applying it, verify the assumptions against your own codebase. Some domains need bespoke implementation for safety, compliance, performance, or product differentiation. The useful shift is not "never generate code"; it is making agents justify new code when an existing, trusted component would reduce ownership.
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