Beyourself

A pattern scanner and editor for text, code, and audio. It ships with a desktop app and an MCP server, so an AI agent can drive it directly (batch-score documents, pull flagged sections, and so on) instead of only a human clicking through the UI.

Two tiers of detection for both text and code:

• Free/instant heuristic (analyze_text, analyze_code, etc.): lexical and regex pattern matching. Fast and free, but a weaker signal than real AI detectors. It misses well-edited output that avoids the specific patterns it looks for.
• Deep/paid LLM judge (analyze_text_deep, analyze_code_deep): actual semantic judgment from Claude, closer to what detectors like GPTZero or Originality.ai do, with a suggested fix for each flagged item. Costs real money and takes several seconds per call. Use it to spot-check specific files, not for batch scanning.

Both tiers give a probabilistic signal, not a certainty. Treat flags as worth a second look, not proof.

Desktop app

npm install
npm start

MCP server

npm run mcp

This project ships a .mcp.json so Claude Code finds the server automatically when working in this directory. To use it from Claude Desktop instead, add this to claude_desktop_config.json:

{
  "mcpServers": {
    "beyourself": {
      "command": "node",
      "args": ["/path/to/BeYourself/mcp-server.js"]
    }
  }
}

Tools

• analyze_text { text, includeAllSentences? }: score raw text, return flagged sentences and suggestions.
• analyze_file { path, includeAllSentences? }: load a .txt, .md, or .docx file and score it.
• batch_analyze { paths[], threshold? }: score many files at once. Returns a needsReview flag per file and a needsReviewPaths list for anything at or above the threshold (default 50).
• get_flagged_sections { path, threshold? }: return just the sentences at or above a per-sentence score (default 25), with rewrite suggestions.
• analyze_text_deep { text, model? }: real LLM judgment of AI likelihood, using the user's authenticated claude CLI, not just lexical heuristics. Costs real money per call (roughly $0.05 to $0.20 depending on prompt-cache warmth) and takes several seconds. Use it for a handful of documents that matter, not for batch scanning.
• save_document { text, outputPath }: write edited text back out in the format outputPath's extension specifies. .docx generates a real Word document; .txt and .md write plain text. No separate app needed to resave.
• analyze_code { code, fileName? }: score source code for AI-typical patterns (generic names, redundant comments, boilerplate catch blocks, leftover scaffolding).
• analyze_code_file { path }: same, loaded from disk.
• batch_analyze_code { paths[], threshold? }: same batch/needsReview pattern as batch_analyze, for code.
• analyze_code_deep { code, fileName?, model? }: real LLM code review using the claude CLI instead of the free regex heuristic. Catches code smells (over-engineering, non-idiomatic patterns) a regex can't, with a per-line suggested fix. Costs real money per call (roughly $0.05 to $0.20) and takes several seconds.
• save_code_file { code, outputPath }: write edited code back to disk, preserving its extension.
• audio_pipeline_status {}: check whether Demucs, Basic Pitch, ffmpeg, FluidSynth, and the SoundFont are installed and ready.
• run_audio_pipeline { inputPath, workDir }: separate an audio file into stems (Demucs) and transcribe each stem to MIDI (Basic Pitch), ready to load into a DAW for editing.
• render_midi_to_audio { midiPath, outputPath, soundfontPath? }: render a single MIDI file (edited or not) back to .wav using FluidSynth and the bundled General MIDI SoundFont.
• finalize_audio_track { stemPaths[], outputPath, workDir, soundfontPath? }: one-shot repackage. Renders any .mid/.midi stems back to audio, mixes with any untouched .wav stems, and transcodes to whatever file type outputPath specifies, so a track that came in as .mp3 can go back out as .mp3. This is a single deterministic render, not an automated re-scan loop.
• analyze_midi_file { path }: score a MIDI file for AI-generated or unedited composition traits: rigid quantization (notes landing exactly on-grid), flat velocity and dynamics, and exact loop-like pattern repetition. This scores the note and timing structure the pipeline already extracted via Basic Pitch. It is not an audio watermark detector. Score the freshly-transcribed MIDI, edit it, then score the edited version to see whether the edit actually changed anything.
• batch_analyze_midi { paths[] }: score multiple MIDI files at once, for example every stem from run_audio_pipeline.

The audio tools need the project's .venv (Python 3.11, since Demucs' build chain doesn't yet support 3.13). See below.

Example pilot workflows

"Batch-analyze these 12 docs. For anything above 50, pull the flagged sections, rewrite them, and save back over the originals."

An agent would call batch_analyze, then for each path in needsReviewPaths: call get_flagged_sections, rewrite the flagged sentences, then call save_document with the same path.

"Separate this track into stems and MIDI. I'll edit the MIDI in Logic. Once I'm done, repackage it as an mp3."

An agent would call run_audio_pipeline, wait for the user to edit the MIDI externally, then call finalize_audio_track with the edited MIDI paths, any untouched stems, and an outputPath ending in .mp3.

Audio pipeline setup

Demucs' build chain doesn't yet support Python 3.13 (the system default on most current Macs), so the audio tooling lives in an isolated venv:

/opt/homebrew/bin/python3.11 -m venv .venv
./.venv/bin/pip install demucs basic-pitch "setuptools<81"

setuptools<81 is needed because resampy, a Basic Pitch dependency, still imports the now-removed pkg_resources API.

run_audio_pipeline and audio_pipeline_status use .venv/bin/demucs and .venv/bin/basic-pitch if present, falling back to the global PATH otherwise.

MIDI-to-audio rendering also needs FluidSynth and a General MIDI SoundFont:

brew install fluid-synth

A SoundFont (soundfonts/MuseScore_General.sf3, MuseScore's bundled General MIDI voice set) is already included in this project.

Tests

npm test

Runs the heuristic-engine sanity tests (text and code) and a real MCP client/server round-trip test covering every registered tool.

node test/audioPipeline.test.js

A slower integration test that runs real Demucs/Basic Pitch inference on a generated test tone. Not part of npm test, since it does actual model inference rather than a fast heuristic check.

Author

Built by will.be.