I built ctxstash: pack a codebase into one LLM-ready file, with a token count

A developer built ctxstash, a zero-dependency command-line tool that packs a codebase into a single Markdown file with a token count estimate, making it easier to share code with LLMs like ChatGPT and Claude. The tool is available on both npm and PyPI, supports file filtering, and automatically excludes junk files like node_modules and binary artifacts.

You're debugging something with ChatGPT, Claude, or Cursor, and you hit the wall every developer knows: the model needs to see your code. So you start the dance — open a file, copy, paste, type "and here's the other file", paste again, label it so the model doesn't get confused… five files later you paste the whole thing and get back "this conversation is too long." Now you're trimming blind, with no idea how many tokens you actually sent. I got tired of doing this by hand, so I built ctxstash : one command that walks a directory and emits a single, tidy Markdown document with every file fenced and language-tagged, a file-tree overview at the top, and an approximate token count so you know up front whether it'll fit. npx ctxstash src context.md ✓ packed 23 files · 142.3 KB · ~38,210 tokens Paste context.md into the model. Done. There's a great Python tool, files-to-prompt , that inspired this — but it's Python-only and doesn't estimate tokens. I wanted something with zero dependencies , a token estimate built in , and an identical build on both npm and PyPI so it doesn't matter which ecosystem you live in. ctxstash . pack the current dir to stdout ctxstash src tests -o ctx.md pack two dirs, write to a file ctxstash . -i " .ts, .tsx" only TypeScript ctxstash . -e " .test.js" drop tests ctxstash . --estimate just tell me the token cost, pack nothing ctxstash src --tree just the file tree --estimate is the one I reach for most — it answers "will this fit in the context window?" without producing a wall of text: files 23 size 142.3 KB ~tokens 38,210 estimate, ~4 chars/token largest by tokens ~6,210 src/bundle.ts ~3,180 src/core.ts ... The packed output looks like this: Repository context Packed by ctxstash — 3 files, 4.1 KB, ~1,040 tokens estimate . File tree src/ core.ts cli.ts README.md Files src/core.ts ...fenced, language-tagged contents... By default it skips the junk automatically — node modules , .git , dist , lockfiles, minified bundles, and binary files images, fonts, compiled artifacts never end up in your context. The summary always goes to stderr , so ctxstash context.md keeps the file clean while you still see the count. npx ctxstash . Node ≥ 18, nothing to install pip install ctxstash Python ≥ 3.8 Both builds are zero-dependency and behavior-identical. run hello, every Markdown file , ctxstash wraps it in a .txt name still gets skipped. npx / pip install and go. npx ctxstash . --estimate It's MIT-licensed and open source: How are you feeding code to LLMs right now — manual copy-paste, a custom script, or something else? And what would make a tool like this actually fit your workflow? I'd genuinely like to know what to build next.