AB suno-lyrics-to-song
Turn complete lyrics, a rough lyric draft, loose lines, or an existing hook into a structured, listenable Suno song. This Suno lyrics-to-song workflow works as an AI song generator from lyrics and AI music generator from lyrics: choose Preserve mode to keep every original line or Refine mode to improve rhythm, rhyme, singability, and hook strength. Build the song structure across verses, chorus, bridge, and purposeful repetition, then create a style prompt for music with genre, mood, arrangement, and vocal direction for Suno custom lyrics. Use it to turn lyrics into a song, make a song from lyrics, convert lyrics to music, get help from a lyrics songwriting assistant, or take rough lyrics to song-ready form through a custom lyrics-to-song process.
Turn complete lyrics, a rough lyric draft, loose lines, or an existing hook into a structured, listenable Suno song.
As a process B 65/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 17. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 65/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 3 mutating operations with no state check
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 100Steps. 20 steps
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 2 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1990 tokens
- 100Progress reporting. Reports progress
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -32 of 3 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 757: enough signal without eating the budget
- +4Structure: 8 headings
- +3Step-by-step instructions: 20 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (11 of 11)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.