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 keys-and-harmony-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/keys-and-harmony-with-pytheory)<a href="https://agentmods.dev/skills/kennethreitz/pytheory-skill/keys-and-harmony-with-pytheory"><img src="https://agentmods.dev/badge/skills/kennethreitz/pytheory-skill/keys-and-harmony-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/keys-and-harmony-with-pytheory"><img src="https://agentmods.dev/badge/skills/kennethreitz/pytheory-skill/keys-and-harmony-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 | $0.00132 | $0.01881 |
| Opus 5 | $0.00066 | $0.00941 |
| Sonnet 5 | $0.00026 | $0.00376 |
| Haiku 4.5 | $0.00013 | $0.00188 |
Grade A, and why
keys-and-harmony-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.
This is a copy
100% identical to keys-and-harmony-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 — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Keys & Harmony
Working with keys and progressions — the harmonic level above a single chord.
Keys and their diatonic content
from pytheory import Key
k = Key("C", "major")
k.chords # ['C major', 'D minor', 'E minor', 'F major', 'G major', 'A minor', 'B diminished']
k.seventh_chords # ['C major 7th', 'D minor 7th', …, 'G dominant 7th', …]
k.note_names # ['C', 'D', 'E', 'F', 'G', 'A', 'B', 'C']
k.signature # {'sharps': 0, 'flats': 0, 'accidentals': []}
k.relative # <Key A minor>
Detect the key
Key.detect("C", "E", "G", "B", "D") # -> <Key C major>
Progressions
k = Key("G", "major")
[c.symbol for c in k.progression("I", "V", "vi", "IV")] # ['G', 'D', 'Em', 'C']
[c.symbol for c in k.nashville(1, 5, 6, 4)] # ['G', 'D', 'Em', 'C']
k.random_progression(4) # a diatonic [Chord, …]
Roman-numeral analysis of an existing progression:
from pytheory import analyze_progression, Chord
chords = [Chord.from_symbol(s) for s in ["C", "G", "Am", "F"]]
analyze_progression(chords, key="C", mode="major") # ['I', 'V', 'vi', 'IV']
Secondary dominants — applied dominants that tonicise a non-tonic
degree. detect_secondary_dominant identifies one chord (the analytical
inverse of Key.secondary_dominant(degree), which builds one); pass
secondary_dominants=True to analyze_progression to label them in
context (instead of the bare degree):
from pytheory import detect_secondary_dominant, analyze_progression, Chord
detect_secondary_dominant(Chord.from_symbol("D7"), "C") # 'V7/V' (D7 -> G)
detect_secondary_dominant(Chord.from_symbol("E7"), "C") # 'V7/vi' (E7 -> Am)
prog = [Chord.from_symbol(s) for s in ("C", "D7", "G7", "C")]
analyze_progression(prog, "C", secondary_dominants=True) # ['I', 'V7/V', 'V7', 'I']
From the terminal, pytheory analyze C D7 G7 C prints the whole picture —
detected key, Roman numerals (with secondary dominants), and cadences
(add --key/--mode to fix the key).
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 · 158 lines · 132 tokens per session scan A 173ba7c691a5
keys-and-harmony-with-pytheory is a skill published in the GitHub repository kennethreitz/pytheory-skill (3 stars, last pushed 2mo ago), licensed MIT. It adds 132 tokens to every session and 1,881 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to keys-and-harmony-with-pytheory, differing in 0 lines, and is treated as a copy.
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