Developer utility
Prompt Context Packager
Flatten multiple files into one LLM-ready prompt block with character and token estimates — 100% local.
Add files or snippets to build a packed prompt.
Output appears here after you add files.
About this tool
What is Prompt Context Packager?
Drop source files, add instructions, and export a single structured markdown block with fenced code, optional file tree, and approximate token counts suited for Cursor, Claude, Gemini, and similar tools.
Large language models reason better when related files arrive together — types next to callers, tests next to implementations — but pasting folders by hand is error-prone. Packager builds a reviewable context packet so you control what enters the window instead of dumping an entire monorepo.
Deep dive
Managing large context windows and token optimization for LLMs
Context is a budget, not a dumpster
Every token you send costs latency and money, and models degrade when irrelevant files crowd out the signal. Effective packaging means selecting the smallest set that still explains the change: public APIs, the files you will edit, and one or two exemplars of local conventions. Token estimates on this page help you stay under model limits before you hit “send” in Cursor or Claude.
Structure the packet the model can navigate
A short instruction block, an optional tree, and labeled fenced files outperform an undifferentiated paste. Put constraints first (“do not refactor unrelated modules”), then code. Prefer paths that match the repo so the model can cite locations accurately. After packaging, run a second pass with Prompt Architect if you need whitespace flattening or single-line JSON-safe prompts.
Keep secrets out of packs. Strip .env files, private keys, and customer data before packaging — the same privacy discipline as the rest of NexusDigitalLabs tools.
How to use it
Typical workflow
- Add only the files needed for the task; drop lockfiles and generated bundles unless they are the subject.
- Write clear instructions describing the goal, constraints, and desired output format.
- Check the token estimate; prune until you are comfortably under your model’s context budget.
- Copy the packaged markdown into your IDE chat or API request.
FAQ
Frequently asked questions
How accurate is the token estimate?
It uses the same BPE-style heuristic as Prompt Architect (~95–98% vs tiktoken for GPT-family text). Treat it as a planning aid, not a billing meter.
Is there a file size limit?
Only your device memory and browser UI. Very large packs may feel slow to edit or copy — prune before shipping to an LLM.
Should I include node_modules?
Almost never. Prefer your source and types. Dependency internals waste context and rarely improve answers about your app code.