PyTheory is a Python toolkit for exploring music theory and composing music, with immediate audio playback and visual representations such as guitar tabs and chord diagrams. Musicians and programmers use it to create arrangements across multiple musical systems, instruments, rhythms, and effects. The catalogue add-ons provide agent guidance for working with the project.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add kennethreitz/pytheory --skill transcription-and-notation-with-pytheorygit clone --depth 1 https://github.com/kennethreitz/pytheoryWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/kennethreitz/pytheory/transcription-and-notation-with-pytheory)<a href="https://agentmods.dev/skills/kennethreitz/pytheory/transcription-and-notation-with-pytheory"><img src="https://agentmods.dev/badge/skills/kennethreitz/pytheory/transcription-and-notation-with-pytheory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kennethreitz/pytheory/transcription-and-notation-with-pytheory"><img src="https://agentmods.dev/badge/skills/kennethreitz/pytheory/transcription-and-notation-with-pytheory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00085 | $0.01184 |
| Opus 5 | $0.00043 | $0.00592 |
| Sonnet 5 | $0.00017 | $0.00237 |
| Haiku 4.5 | $0.00009 | $0.00118 |
Grade A, and why
transcription-and-notation-with-pytheory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- transcription-and-notation-with-pytheory — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Transcription & Notation
Getting music into PyTheory from audio/MIDI, and out to MIDI, sheet music, and tab.
Transcribe a recording → notes / MIDI
from pytheory import Score
score = Score.from_wav("hum.wav", bpm=80) # estimates tempo if bpm omitted
for name, part in score.parts.items():
print(name, len(part.notes), "notes")
score.save_midi("hum.mid")
Score.from_wav(path, *, bpm=None, quantize=None, split=False, fmin=50, fmax=1500).quantize=0.25snaps to sixteenths;split=Trueseparates a full mix into bass + melody (and drums) instead of one monophonicmelodypart..m4a/.mp3work ifafconvert/ffmpegis available; WAV always works.- CLI equivalent:
pytheory transcribe hum.m4a out.mid(add--split,--quantize 0.25,--bpm 90).
Identify the chord in an audio buffer
from pytheory.audio import identify_chord
import scipy.io.wavfile
sr, data = scipy.io.wavfile.read("clip.wav")
identify_chord(data, sr)
# {'symbol': 'D7', 'confidence': 0.76, 'notes': ['D', 'F#', 'A', 'C']} (or None)
Returns a best-guess symbol with a confidence (0..1) and the detected
notes, or None if it can't tell. Works best on clean, sustained chords; it's
a real-time recognizer, not a perfect oracle. (The live version is
pytheory tune --chords, in the guitar skill.)
Import MIDI
from pytheory import Score
score = Score.from_midi("song.mid")
Export to every format
score.save_midi("song.mid") # MIDI (drums ch 10)
open("song.abc", "w").write(score.to_abc(title="Song", key="C"))
open("song.xml", "w").write(score.to_musicxml(title="Song")) # MusicXML for notation apps
open("song.ly", "w").write(score.to_lilypond(title="Song", key="C"))
print(score.to_tab("part_name")) # ASCII guitar tab for a part
to_tab(part_name, tuning="guitar", frets=24)turns a single part into tab.to_musicxmlopens in MuseScore/Finale/Sibelius;to_lilypondengraves to PDF via LilyPond;to_abcis compact plain-text notation.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 110 lines · 85 tokens per session scan A 0e568e432a79
transcription-and-notation-with-pytheory is a skill published in the GitHub repository kennethreitz/pytheory (1,639 stars, last pushed 1mo ago), licensed MIT. It adds 85 tokens to every session and 1,184 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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