GhostWriter by MyHandler vs MashuPack: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of GhostWriter by MyHandler and MashuPack — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
GhostWriter by MyHandler
MyHandler.ai
Windows writing assistant that reads the screen around your cursor on a hotkey double-tap and types a context-aware draft in your voice.
Key features
- Hotkey Double-Tap Drafting: No prompt or dictation is needed — a double-tap with the cursor in any text field triggers capture and drafting directly in place.
- Screen Context Capture: Reads the thread, form or partial sentence surrounding the cursor locally on the machine, using that on-screen context as the entire input.
- Writing Intent Classification: Determines whether the moment calls for a reply, a continuation, a filled-in answer or a fresh compose before writing a single word.
- Sender and Identity Mapping: Maps each message in a thread to its sender and works out which handle is the user's, so the draft answers the other party rather than the user's own words.
- Calendar Cross-Check: When a draft proposes a time, it is validated against the next two weeks of a connected calendar before the text appears.
- Selection Rewriting: Selecting existing text before the hotkey rewrites that selection instead of composing something new.
- Nothing Sent Automatically: The generated text appears at the cursor for the user to read, edit or delete; sending always remains a manual step.
- Local Vault with Zero-Retention Cloud: Capture runs against an encrypted vault on the user's own PC and the assembled context is processed by a zero-data-retention cloud model.
Best for
- Email Backlog: Clearing a queue of owed replies by drafting each one from the thread already on screen instead of retyping the same answer.
- Chat and Slack Replies: Answering a message in a team chat where the draft is grounded in who asked whom for what in the visible thread.
- Web Form Completion: Filling a blank answer box under a question on a web form or application without switching to a separate chat window.
- Sentence Continuation: Picking up a half-written paragraph exactly where it stops, without the assistant restating what was already typed.
- Meeting Scheduling Replies: Responding to a request for a time with a proposal that has already been checked against the user's calendar.
- Tone-Sensitive Rewrites: Selecting a blunt or rough draft and having it rewritten in the user's own voice before sending.
MashuPack
MashuPack
Browser-based tool that converts local code repositories into one clean, structured text file optimized for ChatGPT and Claude.
Key features
- Selective Subsystem Export: Choose exact files, directories, or logical subsystems from a repository and export only the relevant code and metadata to reduce noise fed to models.
- Single Structured Text Output: Compiles selected code into one coherent, structured text file tailored for ChatGPT and Claude to avoid context fragmentation and simplify prompts.
- Client-side Processing (No Backend): Runs entirely in the browser with no repository uploads or backend servers, keeping source code local and minimizing exposure of sensitive data.
- Intelligent File Merging: Merges files while preserving structure and dependency relationships (imports, module boundaries, README/context) so the resulting text maintains meaningful context for LLMs.
- No Account Required: Immediate use without sign-up or authentication—streamlines quick exports and trialing on local projects.
- Model-Targeted Formatting: Produces outputs formatted and structured specifically to improve ingestion by conversational models (e.g., clear file separators, dependency notes, and minimal noise).
- Client-side processing: runs entirely in the browser, keeping code local and avoiding uploads to servers
- Selective export: pick exact files or subsystems from a repository to include in the output
- Single structured output: compiles selected files into one structured text file optimized for LLM input
- Intelligent file merging: merges files while preserving structure and dependencies to reduce context fragmentation
- No account or backend required: use without sign-up or remote storage
- Privacy-first workflow: code remains in the browser; no repository upload
- Model-targeted formatting: output intended for direct use with ChatGPT and Claude
Best for
- LLM-Powered Code Review: Extract a module plus its dependencies into a compact text file to feed to ChatGPT/Claude for targeted code review, bug-finding, or improvement suggestions without exposing the whole repo.
- Refactoring & Design Explanation: Collect a subsystem and supporting files to prompt an LLM to explain architecture, suggest refactors, or generate design docs from the precise context.
- PR/Change Summaries: Produce a condensed, structured snapshot of changed files and context to generate high-quality PR descriptions or release notes via an LLM.
- Onboarding Snippets: Create focused, readable extracts of key files and documentation to accelerate onboarding by asking an LLM to summarize a subsystem for new team members.
- Secure Local Analysis: Prepare local code extracts for offline or privacy-conscious LLM workflows because processing happens in-browser with no uploads.
- Bug Reproduction & Debugging Prompts: Package the minimal set of files and configuration needed to reproduce an issue, then feed that to a model to generate debugging steps or hypotheses.
- Preparing a codebase excerpt for debugging or review with ChatGPT or Claude
- Creating a single, structured context file to feed large repositories into a chat model
- Sharing specific subsystems or file sets with teammates or consultants without uploading entire repo
- Reducing context window fragmentation when using LLMs on large projects
- Quickly compiling repository context for ad-hoc prompts, code summarization, or architecture queries