FD music-creator
AI 音乐创作全流程技能。根据主题和风格创作歌词，通过 MiniMax AI 生成音乐和封面图，Whisper ASR + V2 高精度对齐（纯 Python，无需 espeak-ng）精准校准歌词时序，生成标准 LRC 歌词文件和带歌词同步的播放页面并部署。触发词：创作歌曲、写首歌、生成音乐、AI 音乐、music creator、帮我写首歌、创作音乐、做个音乐。
AI 音乐创作全流程技能。根据主题和风格创作歌词，通过 MiniMax AI 生成音乐和封面图，Whisper ASR + V2 高精度对齐（纯 Python，无需 espeak-ng）精准校准歌词时序，生成标准 LRC 歌词文件和带歌词同步的播放页面并部署。触发词：创作歌曲、写首歌、生成音乐、AI…
As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.
The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.
How to improve
- Remove the critical guard findings (secrets, dangerous commands, hidden instructions): while they stand the skill is blocked and cannot grade above F.
- 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 · 1
-
critical Secrets in code
secret-openai-keyconfig.json:9OpenAI-style API key (quoted — discussed, not commanded)"minimax_api_key": "sk-c…Ey4"
quoted
Files scanned: 10. 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") - note
frontmatter-keyunknown frontmatter key "slug"
Process rating: all ten parameters 48/100
- 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
- 30Running it twice. 2 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (python, node) that frontmatter does not declare
- 100Steps. 18 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1926 tokens
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
- -34 of 6 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 184: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 18 items
- +4Has examples (11 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.