AC skywork-music-maker
AI song and music generator — create songs with vocals, instrumentals, beats, and lyrics from a text description in any language. Generate lo-fi beats, pop songs, orchestral scores, K-POP, reggaetón, guofeng, and more. Supports vocal cloning, reference track style transfer, lyric writing, and full music production workflows. Just say "make me a chill lo-fi beat" or describe any musical idea and this skill handles the rest.
As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 2
✓ No critical or high findings
Medium and low: 2
-
low Exfiltration
net-credential-useREADME.md:232Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)- **Check Balance**: `curl -H "Authorization: Bearer $MUREKA_API_KEY" https://api.mureka.ai/v1/account/billing`
quoted -
low Exfiltration
net-credential-useSKILL.md:286Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)- **Billing**: Check balance with `curl -H "Authorization: Bearer $MUREKA_API_KEY" https://api.mureka.ai/v1/account/billing`
quoted
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 56/100
- 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
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 100Steps. 69 steps
- 100Failures and branches. 4 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3555 tokens
- 100Running it twice. Mutating operations check current state
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 12 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
- -214 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 426: enough signal without eating the budget
- +4Structure: 28 headings
- +3Step-by-step instructions: 69 items
- +4Has examples (6 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.