Install
openclaw skills install suno-poetry-music-creatorEnhanced Suno song creator with reference song analysis and intelligent lyric optimization. Analyzes user's reference songs to extract style, mood, and structure patterns. Iteratively optimizes lyrics based on Suno generation feedback. Creates AI-generated songs with Suno that match user's musical preferences. Use when user wants to create AI-generated songs, optimize lyrics, analyze reference tracks, or create Chinese classical style songs with proper rhyme and tonal patterns.
openclaw skills install suno-poetry-music-creatorTransform creative themes into AI-generated songs with Suno, now with reference song analysis and intelligent lyric optimization.
User: "创作一首关于春天的歌,风格像周杰伦的《青花瓷》"
→ 分析参考歌曲 → 生成歌词 → 优化 → 输出 Suno 风格标签
IF reference provided: → Analyze reference song characteristics (see [references/lyric-examples.md] for analysis framework) → Extract style DNA → Map to Suno tags
ELSE: → Use default style selection → Research poetry for theme
IF Chinese classical style: → Consult [references/chinese-rhyme.md] for rhyme scheme → Apply tonal patterns and couplet techniques
Transform first draft into poetic version:
See [references/lyric-examples.md] for detailed examples.
IF user wants changes: → Identify specific issues → Apply optimization rules → Generate improved version → Repeat up to 3 times
When user provides a reference song/artist:
Step 1: Extract Reference Information
Artist: [Artist Name]
Song: [Song Title]
Genre: [Inferred Genre]
Era: [Time Period]
Mood: [Emotional Tone]
Step 2: Analyze Musical Characteristics Use web search to find:
Step 3: Extract Style DNA
Reference Analysis:
genre: [Primary Genre]
sub_genre: [Sub-style]
tempo: [BPM range or descriptor]
mood: [Emotional profile]
instrumentation: [Key instruments]
vocal_style: [Vocal characteristics]
lyrical_density: [Wordy/Minimal/Average]
rhyme_scheme: [Rhyme pattern]
chorus_hook: [Hook style]
Step 4: Map to Suno Style Tags
[Genre], [Sub-genre], [Mood], [Instrumentation], [Vocal Style]
When Suno generation doesn't meet expectations:
Step 1: Analyze Feedback Identify issues:
Step 2: Apply Optimization Rules
Rule 1: Poetic Elevation ⬆️ Transform plain descriptions into poetic imagery:
Before: 春风拂柳绿枝芽
After: 春风借柳 织几抹新芽
Techniques: Personification, sensory details, dynamic action See [references/lyric-examples.md] for complete examples.
Rule 2: Length Adjustment
Rule 3: Flow Improvement
Rule 4: Emotional Alignment
Rule 5: Repetition Strategy
Rule 6: Emotional Depth Enhancement 💫
Before: 莫道离别苦 / 且将思念藏
After: 莫叹离别苦 / 岁月暗流藏
Techniques: Personal→Universal, metaphorical depth, philosophical undertones
For Chinese classical style songs:
Example couplet:
春风拂柳 / 细雨润物 (春风对细雨,拂柳对润物)
流水无情 / 青山有意 (流水对青山,无情对有意)
Pop: Pop, Catchy, Radio-friendly, Modern
Rock: Rock, Electric Guitar, Energetic, Driving
Jazz: Jazz, Smooth, Saxophone, Sophisticated
Classical: Classical, Orchestral, Cinematic, Epic
Electronic: Electronic, Synth, EDM, Upbeat
Folk: Folk, Acoustic, Storytelling, Intimate
R&B: R&B, Soulful, Groove, Smooth
Hip-Hop: Hip-Hop, Rap, Beats, Urban
Chinese Classical: Chinese Classical, Folk, Erhu, Guzheng, Female Vocal, Melancholic, Cinematic
Happy: Upbeat, Cheerful, Bright, Positive
Sad: Melancholic, Somber, Emotional, Tearful
Energetic: High Energy, Intense, Powerful, Driving
Calm: Peaceful, Relaxing, Gentle, Serene
Romantic: Love, Passionate, Tender, Intimate
Dark: Moody, Brooding, Mysterious, Intense
Always ask for reference - Even vague references help ("like 80s synth-pop" or "similar to Adele's ballads")
Iterate based on feedback - Don't expect perfection on first try
Match style to content - Sad lyrics need appropriate musical backing
Keep Suno's limitations in mind:
Cultural sensitivity - When adapting classical poetry, respect the original meaning while making it singable
Poetic Elevation is key - First drafts are often plain; always look for opportunities to elevate
Learn from feedback - When users share improved versions, analyze what changed and why