Developer tools
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Managing Your Context Window in Claude Code
If you’re using Claude Code, there’s a feature you should know about that gives you visibility into how your context window is being used. The
/contextskill breaks everything down so you can see exactly where your tokens are going.Here’s what it shows you:
- System prompt – the base instructions Claude Code operates with
- System tools – the built-in tool definitions
- Custom agents – any specialized agents you’ve configured
- Memory files – your CLAUDE.md files and auto-memory
- Skills – any skills loaded into the session
- Messages – your entire conversation history
Messages is where you have the most control, and it’s also what grows the fastest. Every prompt you send, every response you get back, every file read, every tool output; it all shows up in your message history.
Then there’s the free space, which is what’s left for actual work before a compaction occurs. This is the breathing room Claude Code has to think, generate responses, and use tools.
You’ll also see a buffer amount that’s reserved for auto-compaction. You can’t use this space directly, it’s set aside so Claude Code has enough room to summarize the conversation and hand things off cleanly.
Why This Matters
Understanding your context usage helps you work more efficiently. A few ways to keep your context lean:
- Start fresh sessions for new tasks instead of reusing a long-running one
- Be intentional about file reads — only read what you need, not entire directories
- Use sub-agents — when you delegate work to a sub-agent, it runs in its own context window instead of yours. All those file reads, tool calls, and intermediate reasoning happen over there, and you just get the result back. It’s one of the best ways to preserve your primary context for the work that actually needs it.
- Trim your CLAUDE.md — everything in your memory files loads every session, so keep it tight
I’ll dig into sub-agents more in a future post. For now, don’t forget about
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Claude Code Prompts for Taming Your GitHub Repository Sprawl
Some useful Claude Code prompts for GitHub repository management.
1. Archive stale repositories
Using the GitHub CLI (gh), find all of my repositories that haven't been pushed to in over 5 years and archive them. List them first and ask for my confirmation before archiving. Use gh repo list <user> --limit 1000 --json name,pushedAt to get the data, then filter by date, and archive with gh repo archive <user>/<repo> --yes.2. Add missing descriptions
Using the GitHub CLI, find all of my repositories that have an empty or missing description. Use gh repo list <user> --limit 1000 --json name,description,url to get the data. For each repo missing a description, look at the repo's README and any other context to suggest an appropriate description. Present your suggestions to me for approval, then apply them using gh repo edit <user>/<repo> --description "<description>".3. Add missing topics/tags
Using the GitHub CLI, find all of my repositories that have no topics. Use gh repo list <user> --limit 1000 --json name,repositoryTopics, description,primaryLanguage to get the data. For each repo with no topics, analyze the repo name, description, and primary language to suggest relevant topics. Present your suggestions for approval, then apply them using gh api -X PUT repos/<user>/<repo>/topics -f '{"names":["tag1","tag2"]}'.To make #1 easier, repjan is a TUI tool that pulls all your repos into an interactive dashboard. It flags archive candidates based on inactivity and engagement, lets you filter and sort through everything, and batch archive in one sweep. If you’ve got hundreds of repos piling up, it’s way faster than doing it one by one.
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First Impressions of OpenAI's Codex App
I’ve been experimenting with OpenAI’s new Codex app for engineering work that launched on February 2nd, and I’m not impressed.
No subagents from what I can tell. It gets stuck on stuff that shouldn’t be blockers. The gpt-5.2-codex model feels slow. I don’t care about the existing skills enough to try to set one up for it.
I did sign up for a free month trial of ChatGPT Plus though, so I’m going to give it a few more attempts before my time runs out. But so far, it doesn’t feel like a force multiplier the way Claude Code or Amp Code does. Even Open Code feels more productive.
Maybe I’ll have better luck with Codex on the CLI? We’ll see.
There’s something about flat-fee billing that feels so much better than watching tokens drain away. Less constrained and more open to trying new things I guess.
I appreciate that they went through the effort of building what looks like a native Swift app instead of yet another VS Code fork.
I think Codex desktop app is aimed at competing with Cursor’s market share, or maybe it’s some attempt at solving whatever direction Claude Desktop is heading.
It doesn’t feel like it’s solving my problems as a developer who already has workflows that I know work.
Back to the CLI pour moi.
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AMP Code: First Impressions of a Claude Code Competitor
I tried AMP Code last weekend and came away genuinely impressed. I didn’t think there was anything at Claude Code’s level currently available.
That said, AMP is in a somewhat unfortunate position. Similar to Cursor, they have to pay the Anthropic tax, and you really want your primary model to be Opus 4.5 for the best results.
So while I was able to get some things done, once you start paying per token… you feel constrained. I’m speaking from a personal budget perspective here, but I blew through ten dollars of credits on their free tier pretty easily.
I could see how with billing enabled and all the sub-agents they make super easy to use, you could burn through a hundred-dollar Claude Code Max plan budget in a week, or even a day, depending on your usage.
What I Really Like
There’s a lot to appreciate about what AMP is doing.
Team collaboration is a standout feature. It’s incredibly easy to share a discussion with other people on your team. Being able to collaborate with your team on something using agents is extremely powerful.
Their TUI is exceptional. I mean, it’s so much better than Claude Code’s terminal interface. They probably have the best TUI on the market right now. It’s definitely better than Open Code.
Sub-agents work out of the box. All the complicated sub-agent stuff I’ve set up manually for my Claude Code projects? It just comes ready to go with AMP. They’ve made really smart decisions about which agents handle which tasks and which models to use. You don’t have to configure any of it, it’s all done for you.
The Bottom Line
I think for enterprise use cases, AMP Code is going to make a lot of sense for a lot of companies.
For individual developers on a personal budget, the cost model is something to think carefully about.
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Claude Code’s built-in tasks are pretty solid—they work well for what they do. But I still find myself reaching for Beads. There’s something about having persistent issue tracking that lives with your code, syncs with git, and doesn’t disappear when you close your terminal. Different tools for different jobs, I suppose.
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Beads: Git-Native Issue Tracking for AI-Assisted Development
If you’re working with AI coding agents like Claude Code, you’ve probably noticed a friction point: context.
Every time you start a new session, you’re rebuilding mental state. What was I working on? What’s blocked? What’s next?
I’ve been using Beads, and it’s changed how I manage work across multiple AI sessions.
What Makes Beads Different?
Beads takes a fundamentally different approach. Issues live in your repo as a
.beads/issues.jsonlfile, syncing like any other code. This means:- No context switching: Your AI agent can read and update issues without leaving the terminal
- Always in sync: Issues travel with your branch and merge with your code
- Works offline: No internet required, just git
- Branch-aware: Issues can follow your branch workflow naturally
The CLI-first design is what makes it click with AI coding agents. When I’m working with Claude Code, I can say “check what’s ready to work on” and it runs
bd readyto find unblocked issues. No copying and pasting from a browser tab.Getting Started
Getting up and running takes about 30 seconds:
# Install Beads curl -sSL https://raw.githubusercontent.com/steveyegge/beads/main/scripts/install.sh | bash # Initialize in your repo bd init # Create your first issue bd create --title="Try out Beads" --type=taskFrom there, the workflow is straightforward:
bd readyshows issues with no blockersbd update <id> --status=in_progressto claim workbd close <id>when you’re donebd syncto commit beads changes
Why This Matters for AI Workflows
The real power shows up when you’re juggling multiple tasks across sessions. Your AI agent can:
- Pick up exactly where you left off by reading the issue state
- Track dependencies between tasks (this issue blocks that one)
- Create new issues for discovered work without breaking flow
- Close completed work and update status in real-time
I’ve found this especially useful for longer projects where I’m bouncing between features, bugs, and cleanup tasks. The AI doesn’t lose track because the state is right there in the repo.
Is It Right for You?
Beads isn’t trying to replace GitHub Issues for team collaboration or complex project management.
It’s designed for a specific workflow: developers using AI coding agents who want persistent, agent-friendly task tracking.
If you’re already working with Claude Code, Aider, or similar tools, give it a try. The setup cost is minimal, and you might find it solves a problem you didn’t realize you had.