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Agent skill

Musical Dna

by jwynia in jwynia/agent-skills

Extract descriptive musical characteristics from any artist or band without using their name, building a vocabulary of sonic qualities for AI music generation, music description, or creative…

MITAuto-check passedMedia & Creative

Install Musical Dna

skills CLI
$ npx skills add jwynia/agent-skills --skill musical-dna -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jwynia/agent-skills musical-dna --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jwynia/agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/creative/music/musical-dna .claude/skills/musical-dna && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
musical-dna
GitHub stars
170
Token cost
~2.9k tokens
SKILL.md length
1,233 words
Files
1
Skills in repo
111
Repo updated
First seen
Licence
MIT

At a glance

Extract descriptive musical characteristics from any artist or band without using their name, building a vocabulary of sonic qualities for AI music generation, music description, or creative…

  • Works in 8 steps: Select Representative Tracks → Systematic Deconstruction → Extract Prompt-Ready Phrases → …
  • Tasks that involve Music and audio generation
  • SKILL.md covers Purpose, Core Principle, Quick Reference: Six Dimensions and Analysis Process, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Musical Dna is an agent skill from jwynia/agent-skills. Extract descriptive musical characteristics from any artist or band without using their name, building a vocabulary of sonic qualities for AI music generation, music description, or creative recombination.

Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Media & Creative, covering Music and audio generation. The licence is MIT.

When your agent uses it

  • Tasks that involve Music and audio generation

Example prompts

  • “/musical-dna”

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Select Representative Tracks
  2. Systematic Deconstruction
  3. Extract Prompt-Ready Phrases
  4. The Name Drop
  5. The Single Dimension
  6. The Genre Substitute
  7. The Representative Track Trap
  8. The Technical Overdose

What it can do on your machine

Read from SKILL.md and the folder at commit e02ec7e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Musical Dna loads about 2.9k tokens when it runs. Until then it costs about 54 tokens; SKILL.md has 1,233 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~54
When it runs · the whole SKILL.md, loaded when a task matches
~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jwynia/agent-skills at commit e02ec7e, republished under its MIT licence (© jwynia). 1,233 words, ~2,886 tokens.

Download SKILL.mdSave it as .claude/skills/musical-dna/SKILL.md (or your agent's skills folder).
name
musical-dna
description
Extract descriptive musical characteristics from any artist or band without using their name, building a vocabulary of sonic qualities for AI music generation, music description, or creative recombination.
license
MIT
metadata.author
jwynia
metadata.version
1.0
metadata.type
utility
metadata.mode
evaluative
metadata.domain
music

Musical DNA Analysis

Purpose

Extract descriptive musical characteristics from any artist or band without using their name, building a vocabulary of sonic qualities for AI music generation, music description, or creative recombination. Replace "sounds like [Artist]" with specific, technique-focused descriptions.

Core Principle

How, not who. Describe techniques, approaches, and sonic qualities rather than referencing artists. This enables:

  • Ethical AI music generation
  • Precise communication about sound
  • Creative recombination of elements
  • Genre-independent vocabulary

Quick Reference: Six Dimensions

DimensionWhat to Analyze
Rhythmic FoundationDrums, tempo, bass lines, time signatures
Harmonic ArchitectureChords, modes, progressions, melodies
Instrumental TechniquesPlaying styles, effects, timbres
Production AestheticsRecording feel, mix, spatial treatment
Genre FusionInfluence integration, innovation points
Energy ArchitectureSong structure, dynamics, emotional trajectory

Analysis Process

Step 1: Select Representative Tracks

Choose 3-5 tracks that capture:

  • Their most recognizable sound
  • Range across their catalog
  • Both typical and boundary-pushing examples
Step 2: Systematic Deconstruction

Work through each dimension, focusing on specific techniques and approaches.

Step 3: Extract Prompt-Ready Phrases

Convert observations into standalone descriptive phrases that work without artist context.

Dimension 1: Rhythmic Foundation

Drum Character
  • Kit composition: Acoustic, electronic, hybrid, sampled
  • Stick technique: Brushes, rods, mallets, standard sticks
  • Snare approach: Rim shots, ghost notes, cross-stick, tight vs. ringy
  • Kick pattern: Four-on-floor, syncopated, polyrhythmic, sparse
  • Hi-hat work: Open/closed patterns, 16th note rides, swung
  • Fill style: Busy, minimal, tom-heavy, snare rolls
Time & Tempo
  • Time signatures: 4/4, 3/4, 6/8, odd meters (5/4, 7/8)
  • Tempo range: Locked BPM or flexible? Fast, mid, slow?
  • Subdivision emphasis: 8ths, 16ths, triplets, swung
  • Polyrhythmic layering: Multiple meters happening simultaneously
Bass Line DNA
  • Technique: Fingered, picked, slapped, synth, upright
  • Role: Rhythmic anchor vs. melodic counterpoint
  • Range: Sub-bass heavy, mid-focused, full range
  • Kick relationship: Locked, complementary, independent

Example Phrases:

  • "Driving 8th-note hi-hat over syncopated kick"
  • "Slapped bass with muted ghost notes"
  • "Swung triplet feel at 95 BPM"

Dimension 2: Harmonic Architecture

Chord Progressions
  • Major/minor balance: Predominantly one or mixed?
  • Modal inflections: Dorian darkness, Mixolydian brightness
  • Chromatic movement: Smooth voice leading, sudden shifts
  • Chord density: Triads, 7ths, extended (9ths, 11ths, 13ths)
  • Harmonic rhythm: Slow changes (1/bar) or rapid (2+/bar)
Tonal Centers
  • Key preferences: Sharp keys, flat keys, open-string friendly
  • Modulation: None, gradual, sudden, frequent
  • Scale choices: Natural minor, harmonic minor, pentatonic, modes
  • Dissonance tolerance: Clean resolution, lingering tension
Melodic Contour
  • Range: Wide intervals or narrow
  • Movement: Stepwise, leaping, arpeggiated
  • Phrase length: Short punchy or long flowing
  • Repetition balance: Hooks vs. development

Example Phrases:

  • "Minor key with Dorian 6th inflection"
  • "Slow harmonic rhythm, one chord per 4 bars"
  • "Wide interval leaps in vocal melody"

Dimension 3: Instrumental Techniques

Guitar Approaches
  • Pickup selection: Bridge (bright), neck (warm), split
  • Tone shaping: Treble-forward, mid-scoop, bass-heavy
  • Technique: Fingerpicking, flatpicking, hybrid, percussive
  • Tuning: Standard, drop D, open tunings, baritone
Effects Chain
  • Distortion type: Overdrive, fuzz, high-gain, clean
  • Time-based: Reverb (room, hall, plate), delay (analog, digital, tape)
  • Modulation: Chorus, phaser, flanger, tremolo, vibrato
  • Pitch: Octave, harmonizer, whammy
  • Dynamics: Compression (heavy, light, none)
Other Instruments
  • Keys/synth: Analog warmth, digital precision, organ, piano
  • Percussion: Auxiliary (tambourine, shaker), world instruments
  • Brass/strings: Section vs. solo, dry vs. lush
  • Electronics: Samples, loops, glitches, synthesis

Example Phrases:

  • "Neck pickup through mild tube overdrive"
  • "Slap-back delay with plate reverb"
  • "Fingerpicked acoustic with percussive body hits"

Dimension 4: Production Aesthetics

Spatial Characteristics
  • Environment feel: Professional studio, live room, bedroom, outdoor
  • Reverb treatment: Dry, intimate, expansive, cavernous
  • Stereo field: Wide, narrow, mono-compatible
  • Depth staging: Everything forward, layered front-to-back
Mix Philosophy
  • Prominence hierarchy: Drums-first, vocal-forward, guitar-heavy
  • Frequency allocation: Each instrument's spectral home
  • Dynamic range: Compressed, dynamic, limiting
  • Clarity vs. saturation: Pristine separation vs. glued warmth
Sonic Texture
  • Signal path: Clean, saturated, distorted, degraded
  • High frequency: Bright, airy, rolled-off, harsh
  • Low end: Tight, boomy, sub-heavy, absent
  • Midrange: Scooped, present, honky, balanced

Example Phrases:

  • "Bedroom recording aesthetic with lo-fi saturation"
  • "Drum-forward mix with tight low end"
  • "Vintage tape warmth with rolled-off highs"

Dimension 5: Genre Fusion Analysis

Influence Mapping
  • Primary foundation: The dominant genre base (60%+)
  • Secondary elements: Strong secondary influence (20-30%)
  • Tertiary accents: Occasional flavor (10% or less)
Integration Methods
  • Temporal placement: Genre X in verses, genre Y in choruses
  • Instrumental assignment: Drums from A, guitars from B
  • Transition approach: Seamless blend vs. jarring contrast
  • Era mixing: Vintage techniques + modern production
Innovation Points
  • Boundary crossing: Where conventions are broken
  • Novel combinations: Unexpected genre marriages
  • Signature fusion: Their unique contribution

Example Phrases:

  • "Math rock precision over post-punk foundation"
  • "Hip-hop production sensibility applied to folk songwriting"
  • "Grunge dynamics with shoegaze texture"

Dimension 6: Energy Architecture

Show full SKILL.md (504 more words)Show less
Song Structure
  • Intro character: Atmospheric, punchy, fade-in, cold start
  • Verse energy: Pulled back, driving, building
  • Chorus intensity: Lift, explosion, subtle shift
  • Bridge/breakdown: Contrast, climax, reflection
  • Outro approach: Fade, stop, resolve, evolve
Dynamic Range
  • Intensity curves: Gradual build, sudden shifts, flat line
  • Peak placement: Early, middle, late, multiple
  • Release patterns: Sudden drop, gradual decay
Emotional Trajectory
  • Mood arc: Single state, journey, oscillation
  • Tension cycles: Build-release frequency
  • Climax character: Cathartic, devastating, transcendent

Example Phrases:

  • "Slow build across 4 minutes to explosive final chorus"
  • "Sudden dynamic drops creating tension"
  • "Verse-chorus contrast via density rather than volume"

Documentation Template

One-Sentence DNA
[Rhythmic approach] + [harmonic character] + [instrumental signature] + [production aesthetic]

Example: "Syncopated post-punk drumming over minor modal progressions, angular clean guitar with chorus effect, dry room recording with bass-forward mix"

Detailed Breakdown
markdown
## Rhythmic Signature
- Time feel:
- Drum character:
- Bass approach:
- Syncopation style:

## Harmonic DNA
- Chord tendencies:
- Scale preferences:
- Progression patterns:

## Instrumental Character
- Guitar tone/technique:
- Effects signature:
- Other key instruments:

## Production Fingerprint
- Recording aesthetic:
- Mix characteristics:
- Sonic texture:

## Genre Fusion Map
- Primary foundation:
- Secondary elements:
- Innovation points:

## Energy Architecture
- Typical structure:
- Dynamic range:
- Build patterns:
Extractable Prompt Elements

List 5-10 standalone phrases usable in AI generation:

  • "..."
  • "..."

Ethical Guidelines

Do
  • Combine elements from multiple analyses
  • Focus on techniques and approaches
  • Build reusable vocabulary
  • Create novel fusions
Don't
  • Copy complete profiles directly
  • Replicate signature riffs/melodies
  • Use as "sounds like [Artist]" substitute
  • Claim to reproduce specific artists

Anti-Patterns

1. The Name Drop

Pattern: Using artist names as shorthand instead of technique descriptions. "Sounds like Radiohead" instead of describing the actual sonic qualities. Why it fails: Defeats the entire purpose. Artist names are black boxes that convey different things to different people and may produce copyright issues in AI generation. Fix: Never use artist names in final output. For every "sounds like X," unpack what that actually means in terms of rhythm, harmony, production, etc.

2. The Single Dimension

Pattern: Analyzing only one dimension (usually rhythm or production) while ignoring others. Producing incomplete profiles. Why it fails: Musical identity emerges from interaction of all dimensions. A rhythmic profile without harmonic context is useless for generation. Fix: Force yourself through all six dimensions. Even if an artist seems "about the guitar sound," their rhythmic choices matter.

3. The Genre Substitute

Pattern: Describing music by genre labels instead of techniques. "Post-punk" instead of describing what makes it post-punk. Why it fails: Genre labels are contested categories, not techniques. AI systems need concrete instructions, not genre negotiations. Fix: Treat genre labels as starting points requiring unpacking. What rhythmic, harmonic, and production choices define this genre for this artist?

4. The Representative Track Trap

Pattern: Analyzing one famous song and extrapolating to entire catalog. Missing range and evolution. Why it fails: Artists vary. Their most famous song may not be representative. Analysis from one track produces narrow profiles. Fix: Analyze 3-5 tracks from different periods and modes. Look for both constants and variations.

5. The Technical Overdose

Pattern: Including so much technical detail that prompts become unusable. Every possible parameter specified. Why it fails: AI generation systems can't process unlimited context. Overly detailed prompts get truncated or confuse the model. Fix: Distill to 5-10 essential phrases. Prioritize what makes this artist distinct rather than comprehensive.

Integration Points

Inbound:

  • From listening to music you want to analyze

Outbound:

  • To AI music generation prompts
  • To lyric-diagnostic for complete song analysis

Complementary:

  • lyric-diagnostic: Lyrical analysis (words)
  • This skill: Musical analysis (sounds)

© jwynia, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/creative/music/musical-dna of jwynia/agent-skills.

Open the folder on GitHubat commit e02ec7e

Compare with similar skills

Musical Dna next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Musical Dna compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Musical Dna this skilljwynia/agent-skills170—~2.9kAutomated safety check: PassMIT
HyperFrames Media Useheygen-com/hyperframes60k—~2.4kAutomated safety check: PassApache-2.0
Musictadaspetra/loop2962 repos~827Automated safety check: PassMIT
Sound Effectstadaspetra/loop2962 repos~1.1kAutomated safety check: PassMIT
Characteristic VoiceNoizAI/skills526—~1.8kAutomated safety check: PassNone
Text To Sfxsonilo-ai/skills1151 repos~1.6kAutomated safety check: NotesMIT

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Categories

Questions about Musical Dna

What does Musical Dna do?

Extract descriptive musical characteristics from any artist or band without using their name, building a vocabulary of sonic qualities for AI music generation, music description, or creative…. Musical Dna is an agent skill from jwynia/agent-skills. Extract descriptive musical characteristics from any artist or band without using their name, building a vocabulary of sonic qualities for AI music generation, music description, or creative recombination.

When should I use Musical Dna?

Musical Dna fits situations like: tasks that involve Music and audio generation.

How do I install Musical Dna in Claude Code?

Run `npx skills add jwynia/agent-skills --skill musical-dna -a claude-code`. Or copy the skill folder (skills/creative/music/musical-dna in jwynia/agent-skills) into .claude/skills/musical-dna in your project. Claude Code loads it when a task matches its description.

How do I install Musical Dna in Codex?

Run `npx skills add jwynia/agent-skills --skill musical-dna -a codex`. Or copy the skill folder (skills/creative/music/musical-dna in jwynia/agent-skills) into .agents/skills/musical-dna in your project. Codex loads it when a task matches its description.

Can I use Musical Dna in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jwynia/agent-skills --skill musical-dna -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/musical-dna, .gemini/skills/musical-dna, .github/skills/musical-dna and .opencode/skills/musical-dna in your project.

What does Musical Dna need to run?

SKILL.md names no scripts, command-line tools or credentials: Musical Dna is instructions for the agent only.

Does Musical Dna access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Musical Dna safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Musical Dna use?

Musical Dna is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Musical Dna use?

About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Musical Dna?

Skills that share tags, products or a category with Musical Dna: HyperFrames Media Use (heygen-com/hyperframes, 60k stars), Music (tadaspetra/loop, 296 stars), Sound Effects (tadaspetra/loop, 296 stars) and Characteristic Voice (NoizAI/skills, 526 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Musical Dna?

jwynia (a GitHub user) maintains it in jwynia/agent-skills, which has 170 GitHub stars. The repository holds 111 skills in this directory. The repository was last updated on February 24, 2026.

Source: jwynia/agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.