This skill should be used when the user asks to "create a Suno prompt", "write a Suno song", "generate music with Suno", "help me with Suno", "make a song prompt", "create lyrics for Suno", "build a music prompt", or mentions Suno AI music generation. Provides comprehensive guidance for creating professional Suno prompts using advanced prompting strategies, structured formatting within 1000 character limit (NO blank lines between sections), parameter optimization, genre-specific techniques, interactive questioning with efficient project name collection, automated artist/song research via sub-agent (web fetching + pattern extraction), automatic file export to organized project directories, AI-slop avoidance for authentic human-centered lyrics, copyright-safe style descriptions that avoid artist/album/song names, character counting utilities for accurate verification, and optional independent quality review via sub-agent for professional assessment.
Create professional, production-ready music prompts for Suno AI by understanding its probabilistic nature and speaking its native language of structured metadata.
IMPORTANT: This skill includes character counting utilities in utils/ because LLMs cannot accurately count characters.
Tool Requirements:
Available Utilities:
utils/count-prompt.py (Python version)utils/count-prompt.js (Node.js version)Usage in Workflow: During Step 8 (Verify), use the Bash tool to execute the counting utility:
python count-prompt.py 'your-prompt-text-here'
See utils/README.md for detailed usage instructions.
Suno does not read prompts like a human following instructions. Instead, it maps text into a probabilistic style-mesh, blending musical concepts based on co-occurrence patterns learned during training. Every word carries "statistical baggage" - associations that may not be intended.
Critical insight: "Pop" acts as a gravitational black hole. Nearly every genre (rock: 315B links, funk: 116B links, emo: 12.2B links) gets pulled toward pop unless actively counteracted through exclusions, unusual combinations, or strategic contrast.
Use this skill to:
This skill uses interactive tools to gather information and research musical styles:
Use AskUserQuestion to gather essential information through structured choices. This helps users clarify their vision and makes better recommendations.
When to use:
When to ask vs. when to proceed:
Ask questions when:
Proceed directly when:
Example usage:
Question: "What's the primary genre for your song?"
Options:
- "Acoustic/Folk/Singer-Songwriter" (Natural vocals, intimate production)
- "Electronic/EDM/Synthwave" (Synthesized sounds, modern production)
- "Rock/Alternative" (Guitar-driven, raw energy)
- "Pop" (Polished, radio-ready hooks)
IMPORTANT: When user mentions artist reference, automatically launch song-researcher sub-agent for automated pattern analysis.
Trigger conditions:
Automated research workflow:
Extract from user request:
Launch song-researcher sub-agent via Task tool:
Task tool:
subagent_type: "song-researcher"
description: "Research artist patterns"
prompt: "Research [Artist] - [Song if mentioned]. User wants [style/mood description]."
Sub-agent performs automated research:
Receive structured research report:
Use research findings to inform subsequent steps:
Benefits of automated research:
Error handling:
Important: Research is for understanding patterns and inspiration, not copying. Sub-agent extracts structural patterns; main agent creates completely original lyrics informed by these learned structures.
For standalone research: Users can also invoke /research-artist [Artist] independently to explore patterns before creating songs.
Use the Write tool to save complete prompts to organized project directories after creation. This enables:
Benefits:
When files are saved:
User says: "I want a sad song like Phoebe Bridgers"
Step 1 - Ask clarifying questions:
Question: "What aspects of Phoebe Bridgers' style appeal to you?"
Header: "Style Focus"
Options:
- "Intimate, confessional lyrics" (Bedroom pop, vulnerable delivery)
- "Melancholic atmosphere" (Dreamy production, sad mood)
- "Indie folk instrumentation" (Acoustic guitar, minimal production)
- "All of the above" (Comprehensive Phoebe Bridgers approach)
User selects: "All of the above"
Step 2 - Automated research (song-researcher sub-agent):
Main agent launches research:
Task tool:
subagent_type: "song-researcher"
description: "Research Phoebe Bridgers patterns"
prompt: "Research Phoebe Bridgers. User wants sad, intimate, indie folk style."
Sub-agent performs:
Sub-agent returns structured report:
# Research Report: Phoebe Bridgers
## Research Quality
Confidence Score: 85% (Good)
Sources Used: Genius ✓, Chords ✗, Spotify/Context ✓
Songs Analyzed: 3 total (Motion Sickness, Kyoto, Scott Street)
## Artist Context
### Consistent Patterns
- Syllable counts: 8-10 in verses, 6-9 in chorus
- Rhyme: Loose/slant rhymes, conversational (ABCB common)
- Structure: Verse-Chorus-Verse-Chorus-Bridge-Final Chorus
- Metaphor: One central metaphor per song, concrete imagery
- Tone: Vulnerable-to-defiant arc, confessional
- Vocabulary: Specific details, conversational language
## Recommendations for Suno Prompt
Genre: "indie folk, 2020s bedroom pop, Phoebe Bridgers x Julien Baker sensibility"
Vocal: "soft female alto, whisper-to-belt range, confessional delivery, vulnerable yet defiant, slight breathiness"
Lyrical Guidance: 8-10 syllables verses, 6-9 chorus, loose ABCB rhyme, one central metaphor
Production: "lo-fi warmth, close-mic intimacy, fingerpicked acoustic guitar, minimal percussion"
Step 3 - Main agent uses research to build prompt: