SKILLEMALL.ai
GitHub ↗ RU Sign in

AC ai-music-prompts

AI music prompt templates and best practices for generating music with AI tools like Suno, Udio, Mureka, and others. Use when user needs to create music prompts, song ideas, or wants guidance on writing effective prompts for AI music generation. Includes bilingual prompt templates for various genres, mood descriptors, instrumentation guidance, and lyric writing tips. Also provides techniques for crafting specific musical outcomes and examples of well-structured prompts in Chinese and English.

modbender/skill-library-mcp Agent Skills author: modbender MIT 9 files body ≈ 24 167 tokens Open the sourcegithub.com↗ analyzed 2 d ago

AI music prompt templates and best practices for generating music with AI tools like Suno, Udio, Mureka, and others.

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • 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: 9. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 24167 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 51/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 10Execution cost. Instruction body is 24167 tokens: crowds the task out of the window
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 85Steps. 341 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. No mutating operations
  • low 21 top-level sections: this looks like several domains in one skill

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
  • -221 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 497: enough signal without eating the budget
  • +4Structure: 285 headings
  • +3Step-by-step instructions: 341 items
  • +4Has examples (206 code blocks)
  • +4Reference files are cited in the instructions (3 of 6)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.