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@@ -44,7 +44,7 @@ To hit a tight deadline, you're aiming to speed up each stage of the process, fo
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1. When the PM has worked with {% data variables.product.prodname_copilot_short %} to get an overview of the tasks required, they ask {% data variables.product.prodname_copilot_short %} to **create issues** for each part of the work.
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Copilot drafts the issues in immersive view, where the PM can refine them and publish them to the repository.
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{% data variables.product.prodname_copilot_short %} drafts the issues in immersive view, where the PM can refine them and publish them to the repository.
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The PM marks some of the issues as nice-to-haves or maintenance. These may be good candidates for {% data variables.copilot.copilot_coding_agent %}.
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@@ -54,11 +54,17 @@ To hit a tight deadline, you're aiming to speed up each stage of the process, fo
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Now, the developer can ask questions in the space, and {% data variables.product.prodname_copilot_short %} will already have all the context the PM added.
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## 2. Create with {% data variables.product.prodname_github_models %} and agent mode
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## 2. Prototype with {% data variables.product.prodname_spark %}
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1. The PM asks the developer to start by finding the best AI model to provide the tailored umbrella recommendations, based on the cost and effectiveness of the models.
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1. The developer asks **{% data variables.copilot.copilot_chat_short %}** to recommend several AI models for the job and the pros and cons of each. To provide useful context, they ask Copilot to consider the information in the [AUTOTITLE](/copilot/reference/ai-models/model-comparison) {% data variables.product.prodname_docs %} article.
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1. To settle on a model from the shortlist, the developer uses the **{% data variables.product.prodname_github_models %}** playground to compare results from the same prompt across models. They save time by testing models on a single platform, rather than needing to set up an API key for each model separately.
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1. To prototype the feature, the PM opens {% data variables.product.prodname_spark %} (`https://github.com/spark`) and prompts it to create the widget. They can write a prompt, paste in documentation from the last phase, or upload a screenshot. {% data variables.product.prodname_spark_short %} generates a working app with an LLM already integrated to power recommendations—no IDE or coding required.
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1. To refine the prototype, the PM can prompt {% data variables.product.prodname_spark_short %} again, use the visual editor, or edit the code directly. In the "Prompts" tab, they can adjust the text that powers recommendations without editing code. Each change instantly updates the live preview, so the PM can see how edits affect the app.
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1. To collaborate, the PM publishes the spark and updates the visibility so their organization can access it. They can also create a repository and invite collaborators. Code changes made in the repository sync back to {% data variables.product.prodname_spark_short %}, letting the PM and designers see updates to the widget in real time.
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## 3. Create with {% data variables.product.prodname_github_models %} and agent mode
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1. The PM shares the {% data variables.product.prodname_copilot_short %} space and {% data variables.product.prodname_spark %} prototype and repository with the developer and asks the developer to start by finding the best AI model to provide the tailored umbrella recommendations, based on the cost and effectiveness of the models.
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1. The developer asks **{% data variables.copilot.copilot_chat_short %}** to recommend several AI models for the job and the pros and cons of each. To provide useful context, they ask {% data variables.product.prodname_copilot_short %} to consider the information in the [AUTOTITLE](/copilot/reference/ai-models/model-comparison) {% data variables.product.prodname_docs %} article.
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1. To settle on a model from the shortlist, the developer uses the **{% data variables.product.prodname_github_models %}** playground to compare results from the same prompt across models. They can then also update the {% data variables.product.prodname_spark %} prototype to use the specific model they've selected to see how it impacts the whole widget experience in the prototype (all models supported in {% data variables.product.prodname_github_models %} are supported in {% data variables.product.prodname_spark_short %}). They save time by testing models on a single platform, rather than needing to set up an API key for each model separately.
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@@ -81,7 +87,7 @@ To hit a tight deadline, you're aiming to speed up each stage of the process, fo
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1. The developer reviews the diff of the agent's work and chooses which code to keep.
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## 3. Test with an MCP server
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## 4. Test with an MCP server
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1. When the code is finished, the developer wants to run tests on their local build of the site using Playwright, an automated in-browser testing service.
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> * With the **{% data variables.product.github %} MCP server** enabled, {% data variables.product.prodname_copilot_short %} can run the command to open a pull request directly from {% data variables.product.prodname_vscode_shortname %}, with the title and description already filled in.
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> * Interactions with the {% data variables.product.github %} MCP server are secured by **push protection**, which blocks secrets from being included in AI-generated responses and prevents you from exposing secrets through any actions you perform using the server (public repositories only). See [AUTOTITLE](/code-security/secret-scanning/introduction/about-push-protection).
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## 4. Review with {% data variables.copilot.copilot_code-review_short %}
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## 5. Review with {% data variables.copilot.copilot_code-review_short %}
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1. A repository owner has configured automatic **code reviews** by {% data variables.product.prodname_copilot_short %} on the repository. {% data variables.product.prodname_copilot_short %} provides an initial review on the pull request, identifying bugs and potential performance issues that the developer can fix before a human reviewer gets to the pull request.
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1. The developer's colleague reviews and approves the pull request. The work is ready to merge.
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## 5. Optimize with {% data variables.copilot.copilot_coding_agent %}
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## 6. Optimize with {% data variables.copilot.copilot_coding_agent %}
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1. After the release, the product manager collects customer feedback and identifies an opportunity to improve the widget's suggestions by switching to a more reliable API for weather data. They create an issue to implement this change, and **assign it to {% data variables.product.prodname_copilot_short %}** directly on {% data variables.product.github %}.
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1. {% data variables.copilot.copilot_coding_agent %} works in the background and opens a pull request, which the product manager marks as ready for review.
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1. {% data variables.product.prodname_copilot_short %} works in the background and opens a pull request on {% data variables.product.github %}, ready for another developer to review.
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## 6. Secure with {% data variables.copilot.copilot_autofix_short %}
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## 7. Secure with {% data variables.copilot.copilot_autofix_short %}
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1. An administrator has enabled {% data variables.product.prodname_code_scanning %} on the repository, and a {% data variables.product.prodname_code_scanning %} alert suggests a potential vulnerability in the code.
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1. A security manager requests **{% data variables.copilot.copilot_autofix_short %}** to automatically suggest a fix for the vulnerability, which a developer reviews and approves.
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@@ -140,12 +146,13 @@ To integrate agentic AI features effectively into your workstreams, you'll need
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| ------- | ---------------- |
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| Immersive view of {% data variables.copilot.copilot_chat_short %} | [AUTOTITLE](/copilot/using-github-copilot/copilot-chat/asking-github-copilot-questions-in-github#submitting-a-question-to-copilot-chat) |
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| {% data variables.copilot.copilot_spaces %} ({% data variables.release-phases.public_preview %}) | [AUTOTITLE](/copilot/using-github-copilot/copilot-spaces/about-organizing-and-sharing-context-with-copilot-spaces) |
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| {% data variables.product.prodname_spark %} ({% data variables.release-phases.public_preview %}) | [AUTOTITLE](/copilot/how-tos/administer-copilot/manage-for-enterprise/manage-spark) |
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| {% data variables.copilot.copilot_chat_short %} agent mode | [Use agent mode in VS Code](https://code.visualstudio.com/docs/copilot/chat/chat-agent-mode) |
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| Content exclusions | [AUTOTITLE](/copilot/managing-copilot/configuring-and-auditing-content-exclusion/excluding-content-from-github-copilot) |
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| MCP servers ({% data variables.release-phases.public_preview %}) | [AUTOTITLE](/copilot/customizing-copilot/extending-copilot-chat-with-mcp#configuring-mcp-servers-in-visual-studio-code) |
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| GitHub Models playground ({% data variables.release-phases.public_preview %}) | [AUTOTITLE](/github-models/prototyping-with-ai-models#experimenting-with-ai-models-in-the-playground) |
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| {% data variables.product.prodname_github_models %} playground ({% data variables.release-phases.public_preview %}) | [AUTOTITLE](/github-models/prototyping-with-ai-models#experimenting-with-ai-models-in-the-playground) |
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| Custom instructions | [AUTOTITLE](/copilot/customizing-copilot/adding-repository-custom-instructions-for-github-copilot?tool=vscode) |
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| Copilot code review | [AUTOTITLE](/copilot/how-tos/agents/copilot-code-review/automatic-code-review) |
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| {% data variables.copilot.copilot_code-review_short %} | [AUTOTITLE](/copilot/how-tos/agents/copilot-code-review/automatic-code-review) |
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| {% data variables.copilot.copilot_coding_agent %} ({% data variables.release-phases.public_preview %}) | [AUTOTITLE](/copilot/rolling-out-github-copilot-at-scale/enabling-developers/using-copilot-coding-agent-in-org) |
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| {% data variables.copilot.copilot_autofix_short %} | [AUTOTITLE](/code-security/code-scanning/enabling-code-scanning/configuring-default-setup-for-code-scanning) |
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