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split chapters into section subpages
Refactor docs structure so each former H2 chapter section is a dedicated page under chapter directories. What changed: - Split chapter content into per-section files under: - src/content/docs/getting-started/ - src/content/docs/becoming-productive/ - src/content/docs/expanding-horizons/ - Removed legacy single-file chapter pages: - src/content/docs/getting-started.md - src/content/docs/becoming-productive.md - src/content/docs/expanding-horizons.md - Removed chapter landing index pages and made chapter groups point directly to section pages. - Updated sidebar config in astro.config.ts: - Added Home entry. - Kept chapter navigation as explicit item order. - Adjusted section heading depth after split (demoted one level) so subpages use the correct hierarchy. - Incorporated upstream text change from df7c4d4 by removing obsolete improve-skill guidance and adding memory/post-mortem recommendations. - Refactored src/content/docs/index.md to define chapter URLs once using reference-style links. How: - Moved chapter sections into dedicated Markdown files and preserved existing section content. - Updated relative asset paths for moved files. - Repointed cross-links and sidebar slugs to current subpage routes. Manual testing: - Ran: bun lint - Ran: bun x astro check - Verified all new subpages map to original chapter sections and links resolve. Special considerations: - Navigation order is intentionally controlled in astro.config.ts rather than via per-file sidebar order frontmatter. - URLs now target first section pages for each chapter since chapter landing pages were removed.
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astro.config.ts

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},
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],
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sidebar: [
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{ label: "Getting Started", slug: "getting-started" },
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{ label: "Becoming Productive", slug: "becoming-productive" },
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{ label: "Expanding Horizons", slug: "expanding-horizons" },
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{ label: "Home", slug: "index" },
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{
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label: "Getting Started",
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items: [
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{ slug: "getting-started/glossary" },
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{ slug: "getting-started/what-can-ai-agents-even-do" },
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{ slug: "getting-started/how-to-set-up-a-new-repo" },
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{ slug: "getting-started/first-steps-towards-self-improvement" },
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],
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},
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{
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label: "Becoming Productive",
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items: [
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{ slug: "becoming-productive/prompting-techniques" },
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{ slug: "becoming-productive/closing-the-loop" },
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{ slug: "becoming-productive/going-10x" },
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],
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},
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{
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label: "Expanding Horizons",
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items: [
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{ slug: "expanding-horizons/threads-context-and-caching" },
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{ slug: "expanding-horizons/some-words-about-models" },
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{ slug: "expanding-horizons/a-few-words-about-mcp" },
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{ slug: "expanding-horizons/what-to-read-next" },
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],
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},
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],
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components: {
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Footer: "./src/components/Footer.astro",

src/content/docs/becoming-productive.md

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---
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title: Closing the loop
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---
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If you want to be efficient while working with agents, you need to _close the loop_. The agent must be able to **continuously** and **autonomously** gather feedback about its work without your intervention!
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This section provides some tips on how to make your agent more _agentic_.
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## Tests, tests, tests
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1. Make sure your agent writes tests for any regression it finds before attempting to fix actual code. If it doesn’t do this by itself, consider telling it so in your AGENTS.md.
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2. Pay attention to how models assert the expected state. Many models tend to write leaky assertions, which only catch the exact issue they just reasoned about.
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## Backend logs and database access
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1. Make your app tee logs to a `*.log` file. This will allow agents to observe runtime behavior. Models are also good at adding their own temporary logs while debugging.
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2. Make it easy for an agent to connect to your database via `psql` or `sqlite3`. You can even use this interface in place of database GUIs.
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3. Take a look at [tidewave.ai](https://tidewave.ai/).
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## Leverage CLIs
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1. Models are trained _a lot_ on Bash. They breathe it and are very productive when they can process data through shell one-liners or quick Python scripts.
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2. If you build a quick library for some remote API:
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1. Try to make it easy for agents to play with this API.
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2. In a non-interactive language (Go, Rust, Swift, etc.), consider asking your agent to whip up a quick CLI on top of your code.
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3. In JS, Python, Elixir, or Ruby, agents can efficiently use REPLs or one-off scripts.
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Also, take a look at these skills:
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- [https://skills.sh/mitsuhiko/agent-stuff/tmux](https://skills.sh/mitsuhiko/agent-stuff/tmux) - useful if you make/use interactive CLIs. Agents are pretty good at using GDB/LLDB via tmux.
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## Automating web frontend QA
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Current frontier models are surprisingly capable of browsing websites, clicking around, and observing what happens, provided they are given the right tools.
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Try using one of these tools:
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- [https://cursor.com/docs/agent/browser](https://cursor.com/docs/agent/browser)
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- [https://skills.sh/vercel-labs/agent-browser/agent-browser](https://skills.sh/vercel-labs/agent-browser/agent-browser)
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- [https://claude.com/chrome](https://claude.com/chrome)
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Make it easy for agents to spawn a new instance of the app by themselves. Ideally on a separate port so that multiple agents can work in parallel.
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Also, take a look at these skills:
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- [https://skills.sh/vercel-labs/agent-skills/vercel-react-best-practices](https://skills.sh/vercel-labs/agent-skills/vercel-react-best-practices)
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## Automating mobile app QA
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You can also tell the agent to play with a phone simulator.
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Take a look at:
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- [Radon AI](https://radon.swmansion.com/docs/features/radon-ai)
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- [XcodeBuildMCP](https://www.xcodebuildmcp.com/)
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Also, have a look at these skills:
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- [https://skills.sh/expo/skills](https://skills.sh/expo/skills)
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- [https://skills.sh/callstackincubator/agent-skills/react-native-best-practices](https://skills.sh/callstackincubator/agent-skills/react-native-best-practices)

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