context-mode

1st comment: 2 day old account, "is the real story here", summary -> comment -> question, general punchiness of style without saying that much. These llms feel like someone said "be an informal hacker news commenter" so they often end with "Curious how" instead of "I'm curious how" or "Worth building" instead of "It's worth building". Not that humans don't do any of this but all of it together in their comment history, you just get a general vibe. author reply: not as obvious, but for one thing yes literally em dash, their post has 10 em dashes in 748 words, this comment has 2 em dashes in 115 words. Not that em dash = ai, but in the context of a post about AI it seems more likely. And finally, https://github.com/mksglu/claude-context-mode/blob/main/cont... the file the author linked in their own repo does not exist! (https://github.com/m

No magic — standard Unix process inheritance. Each execute() spawns a child process via Node's child_process.spawn() with a curated env built by #buildSafeEnv (https://github.com/mksglu/claude-context-mode/blob/main/cont...). It passes through an explicit allowlist of auth vars (GH_TOKEN, AWS_ACCESS_KEY_ID, GOOGLE_APPLICATION_CREDENTIALS, KUBECONFIG, etc.) plus HOME and XDG paths so CLI tools find their config files on disk. No state persists between calls — each subprocess inherits credentials from the MCP server's environment, runs, and exits. This works because tools like gh and aws resolve auth on every invocation anyway (env vars or ~/.config files). The tradeoff is intentional: allowlist over full process.env so the sandbox doesn't leak unrelated vars.

Author here. I shared the GitHub repo a few days ago (https://news.ycombinator.com/item?id=47148025) and got great feedback. This is the writeup explaining the architecture. The core idea: every MCP tool call dumps raw data into your 200K context window. Context Mode spawns isolated subprocesses — only stdout enters context. No LLM calls, purely algorithmic: SQLite FTS5 with BM25 ranking and Porter stemming. Since the last post we've seen 228 stars and some real-world usage data. The biggest surprise was how much subagent routing matters — auto-upgrading Bash subagents to general-purpose so they can use batch_execute instead of flooding context with raw output. Source: https://github.com/mksglu/claude-context-mode Happy to answer any architecture questions.

Every MCP tool call dumps raw data into Claude Code's 200K context window. A Playwright snapshot costs 56 KB, 20 GitHub issues cost 59 KB. After 30 minutes, 40% of your context is gone. I built an MCP server that sits between Claude Code and these outputs. It processes them in sandboxes and only returns summaries. 315 KB becomes 5.4 KB. It supports 10 language runtimes, SQLite FTS5 with BM25 ranking for search, and batch execution. Session time before slowdown goes from ~30 min to ~3 hours. MIT licensed, single command install: /plugin marketplace add mksglu/claude-context-mode /plugin install context-mode@claude-context-mode Benchmarks and source: https://github.com/mksglu/claude-context-mode Would love feedback from anyone hitting context limits in Claude Code. NOTE: Because of the timezone issue, I’m resurfacing the same content here.

Every MCP tool call dumps raw data into Claude Code's 200K context window. A Playwright snapshot costs 56 KB, 20 GitHub issues cost 59 KB. After 30 minutes, 40% of your context is gone. I built an MCP server that sits between Claude Code and these outputs. It processes them in sandboxes and only returns summaries. 315 KB becomes 5.4 KB. It supports 10 language runtimes, SQLite FTS5 with BM25 ranking for search, and batch execution. Session time before slowdown goes from ~30 min to ~3 hours. MIT licensed, single command install: /plugin marketplace add mksglu/claude-context-mode /plugin install context-mode@claude-context-mode Benchmarks and source: https://github.com/mksglu/claude-context-mode Would love feedback from anyone hitting context limits in Claude Code.