Momento/.agents/skills/suno-feedback-elicitor/references

Feedback Elicitor

The Feedback Elicitor guides users through a structured post-generation feedback loop after they have listened to their Suno output, translating subjective musical reactions into concrete parameter adjustments. It handles five feedback types — clear, positive, vague, contradictory, and technical — each with a tailored elicitation strategy. For vague feedback ("it just doesn't feel right"), it uses a three-phase guided elicitation sequence (binary narrowing, comparative anchoring, emotional vocabulary bridge) to draw out specifics. The skill produces structured adjustment recommendations that feed directly back into the Style Prompt Builder and Lyric Transformer.

When to Use Directly vs. Through Mac

Use this skill directly when you have already generated a song on Suno and want to refine it based on what you heard. Use Mac (the orchestrating agent) when feedback refinement is part of a larger iterative workflow where you want seamless handoff between skills.

Feedback Types

Type	Signal	Strategy
Clear	Specific, actionable problem ("the guitar is too loud")	Direct parameter mapping
Positive	Likes the result, wants to evolve or lock in	Identify what to preserve vs. evolve
Vague	Knows something is off but cannot articulate it	Three-phase guided elicitation
Contradictory	Wants conflicting things ("more energetic but also chill")	First Principles reset; check for section contrast
Technical	Artifacts, glitches, pronunciation issues	Regeneration or Suno Studio feature recommendations

Workflow

1. Receive Feedback — Accept natural language reactions; capture everything including creative context
2. Gather Context — Collect original style prompt, lyrics, model, sliders, and intent as relevant
3. Triage — Classify feedback type (mixed feedback is handled per-component)
4. Elicit/Map — Apply type-specific strategy to extract actionable specifics
5. Map to Adjustments — Translate findings into style prompt changes, exclusion updates, slider adjustments, lyric adjustment specs, and model/Studio suggestions
6. Present Recommendations — Before/after narrative preview, structured adjustment package with confidence scores
7. Handoff — Offer to invoke Style Prompt Builder or Lyric Transformer with the adjustments; suggest band profile updates for systematic preferences

Headless Mode (--headless or -H)

• --headless:analyze — Triage and categorize feedback only, return analysis JSON
• --headless:adjustments — Accept feedback + original prompts, return full adjustment recommendations
• --headless — Analyze + generate adjustments with balanced defaults

Scripts

Script	Description
parse-feedback.py	Validates and extracts structured dimensions from feedback input in a single pass
map-adjustments.py	Maps feedback dimensions to Suno parameter adjustments with consistency validation

Example Invocation

# Interactive
"The vocals feel too polished on my last Suno generation"
"It just doesn't feel right — can you help me figure out what to change?"

# Headless
--headless:adjustments --feedback "vocals too polished, needs rawer feel" --style-prompt "warm indie rock..." --model v5-pro
--headless:analyze --feedback "it sounds off somehow"

Output Integration

Adjustment recommendations are structured to feed directly into other skills:

• Style prompt changes go to the Style Prompt Builder via --headless:refine
• Lyric changes go to the Lyric Transformer via --headless:refine as an adjustment spec
• Systematic preferences can be saved back to the band profile
• Iteration logs can be persisted at docs/feedback-history/ for multi-round refinement

Part of the Suno Band Manager Module

This skill is part of the Suno Band Manager module and works with any LLM CLI supporting the Agent Skills standard. For the full guided experience, invoke Mac — the orchestrating agent — instead of using this skill directly.