musical-dna
npx skills add https://github.com/jwynia/agent-skills --skill musical-dna
Agent 安装分布
Skill 文档
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)