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 --skill scales-modes-and-tunings-with-pytheorygit clone --depth 1 https://github.com/kennethreitz/pytheory-skillWrote 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-skill/scales-modes-and-tunings-with-pytheory)<a href="https://agentmods.dev/skills/kennethreitz/pytheory-skill/scales-modes-and-tunings-with-pytheory"><img src="https://agentmods.dev/badge/skills/kennethreitz/pytheory-skill/scales-modes-and-tunings-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-skill/scales-modes-and-tunings-with-pytheory"><img src="https://agentmods.dev/badge/skills/kennethreitz/pytheory-skill/scales-modes-and-tunings-with-pytheory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00153 | $0.01720 |
| Opus 5 | $0.00077 | $0.00860 |
| Sonnet 5 | $0.00031 | $0.00344 |
| Haiku 4.5 | $0.00015 | $0.00172 |
Grade A, and why
scales-modes-and-tunings-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 12d 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.
This is a copy
100% identical to scales-modes-and-tunings-with-pytheory — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scales, Modes & Tunings
Scales and modes, the notes/intervals beneath them, and PyTheory's 16 tuning systems.
Scales & modes
from pytheory import TonedScale
ts = TonedScale(tonic="C4", system="western")
ts.scales # ('chromatic','major','minor','dorian','lydian', …)
s = ts["dorian"]
s.note_names # ['C', 'D', 'Eb', 'F', 'G', 'A', 'Bb', 'C']
[str(t) for t in s.tones] # ['C4', 'D4', 'Eb4', 'F4', …]
s.harmonize() # diatonic chords built on each degree
Pentatonic & blues scales live in the
bluessystem, notwestern:TonedScale(tonic="E2", system="blues")["minor pentatonic"](also"major pentatonic","blues","major blues").
Which scale fits these notes?
Scale.recommend ranks scales by how well they contain a set of notes — great
for "what can I solo with here?". Note the import: pytheory.Scale is an alias
for TonedScale, so import the real class from pytheory.scales:
from pytheory.scales import Scale
Scale.recommend("C", "Eb", "G", "Bb", "D", top=3)
# [('C', 'aeolian', 1.0), ('G', 'aeolian', 1.0), ('C', 'dorian', 1.0)]
# -> (tonic, scale_name, fitness 0..1)
Detect the scale of a note set:
TonedScale(tonic="C4", system="western")["major"].detect("C","D","E","F","G","A","B")
# ('C', 'major', 7) -> (tonic, scale, matched-note count)
Tones, intervals, overtones
from pytheory import Tone
Tone.from_string("A4").frequency # 440.0
Tone.from_midi(69) # <Tone A4>
Tone.from_frequency(440.0) # <Tone A4>
Tone.from_string("C4").interval_to(Tone.from_string("G4")) # 'perfect 5th'
Tone.from_string("A2").overtones(4) # [110.0, 220.0, 330.0, 440.0]
[str(t) for t in Tone.from_string("C4").circle_of_fifths()] # ['C4','G4','D5','A5', …]
Twelve-tone rows (serialism)
A ToneRow is an ordering of all 12 pitch classes, each once. From it come
the 48 forms (P/R/I/RI × 12 transpositions) and the row matrix. The
transposition number is the pitch class the form begins on.
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.
- 12d ago First seen · 151 lines · 153 tokens per session scan A a77bc681d9e3
scales-modes-and-tunings-with-pytheory is a skill published in the GitHub repository kennethreitz/pytheory-skill (3 stars, last pushed 2mo ago), licensed MIT. It adds 153 tokens to every session and 1,720 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to scales-modes-and-tunings-with-pytheory, differing in 0 lines, and is treated as a copy.
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