
musical-dna
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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/jwynia/agent-skills --skill musical-dna- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads musical-dna automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
| Dimension | What to Analyze |
|---|---|
| Rhythmic Foundation | Drums, tempo, bass lines, time signatures |
| Harmonic Architecture | Chords, modes, progressions, melodies |
| Instrumental Techniques | Playing styles, effects, timbres |
| Production Aesthetics | Recording feel, mix, spatial treatment |
| Genre Fusion | Influence integration, innovation points |
| Energy Architecture | Song 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
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
## 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-diagnosticfor complete song analysis
Complementary:
lyric-diagnostic: Lyrical analysis (words)- This skill: Musical analysis (sounds)
FAQ
What does the musical-dna skill 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 recombination.
How do I install musical-dna?
Run npx skills add https://github.com/jwynia/agent-skills --skill musical-dna in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/musical-dna/ (personal) or .claude/skills/musical-dna/ (project).
Which AI agents can use musical-dna?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is musical-dna safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains musical-dna?
It is published by jwynia in jwynia/agent-skills. It has 542 recorded installs.
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