Agent skill

Mc Render Compile

by jtydhr88 in jtydhr88/music-composition-skills

The compiler from ARR-SPEC to any generation backend (编译层). An agent skill from jtydhr88/music-composition-skills.

MITAuto-check passed

Install Mc Render Compile

skills CLI
$ npx skills add jtydhr88/music-composition-skills --skill mc-render-compile -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jtydhr88/music-composition-skills mc-render-compile --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jtydhr88/music-composition-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/music-composition/skills/mc-render-compile .claude/skills/mc-render-compile && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
mc-render-compile
GitHub stars
150
Token cost
~2.3k tokens
SKILL.md length
672 words
Files
1
Skills in repo
29
Repo updated
First seen
Licence
MIT

At a glance

The compiler from ARR-SPEC to any generation backend (编译层). An agent skill from jtydhr88/music-composition-skills.

  • Works in 11 steps: 动笔前必填 → 后端能力对照 → ★ 编译顺序 → …
  • Turning a finished ARR-SPEC into Suno / YuE2 / MiniMax / ACE-Step / symbolic output
  • SKILL.md covers 按任务读哪几节, 边界, 1. 动笔前必填 and 2. 后端能力对照, plus 10 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Mc Render Compile is an agent skill from jtydhr88/music-composition-skills. The compiler from ARR-SPEC to any generation backend (编译层). One compiler, many backends - the backend differences live in backends.yaml as data, never as separate skills. Covers the compile order, section-tag mapping, turning structured fields into prose style prompts without losing information, lyric chunking, the honors contract that decides which fields are scorable, degradation strategy when a backend cannot execute a field, seeds and best-of-N, and what to hand over so the user can run it in whatever tool…

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It works with Suno and MiniMax. The repository describes itself as: Professional agent skills for composing and arranging popular music. The licence is MIT.

When your agent uses it

  • Turning a finished ARR-SPEC into Suno / YuE2 / MiniMax / ACE-Step / symbolic output
  • Choosing a backend
  • A generation ignored the spec
  • Adding a new backend

Example prompts

  • “/mc-render-compile”

Requirements

  • Python 3

Workflow steps

11 steps, taken from the step headings in SKILL.md.

  1. 动笔前必填
  2. 后端能力对照
  3. ★ 编译顺序
  4. style_prompt:把结构化字段压成散文而不丢信息
  5. 段落标签与歌词
  6. ★ honors 契约:本 skill 最重要的一条
  7. 降级策略
  8. 交给后端执行
  9. 加一个新后端
  10. 与 ARR-SPEC 的字段对应
  11. 编译完必过

What it can do on your machine

Read from SKILL.md and the folder at commit 7adca0c. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Mc Render Compile loads about 2.3k tokens when it runs. Until then it costs about 197 tokens; SKILL.md has 672 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~197
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jtydhr88/music-composition-skills at commit 7adca0c, republished under its MIT licence (© jtydhr88). 672 words, ~2,295 tokens.

Download SKILL.mdSave it as .claude/skills/mc-render-compile/SKILL.md (or your agent's skills folder).
name
mc-render-compile
description
The compiler from ARR-SPEC to any generation backend (编译层). One compiler, many backends - the backend differences live in backends.yaml as data, never as separate skills. Covers the compile order, section-tag mapping, turning structured fields into prose style prompts without losing information, lyric chunking, the honors contract that decides which fields are scorable, degradation strategy when a backend cannot execute a field, seeds and best-of-N, and what to hand over so the user can run it in whatever tool hosts the backend. Use when turning a finished ARR-SPEC into Suno / YuE2 / MiniMax / ACE-Step / symbolic output, when choosing a backend, when a generation ignored the spec, or when adding a new backend. 编译、后端、Suno、YuE2、MiniMax、ACE-Step、提示词生成、段落标签、honors。

编译层(Render Compile)

本库只有一个编译器。后端是数据,不是 skill。

ARR-SPEC(唯一 IR)
    ↓  mc-render-compile   ← 只有这一个,管共性
    ├─→ backends.yaml: yue2      ★ 可直接吃 ABC 乐谱,控制力最强
    ├─→ backends.yaml: suno         散文 prompt + 段落标签,成品最润
    ├─→ backends.yaml: minimax-music-3
    ├─→ backends.yaml: ace-step-1.5
    ├─→ backends.yaml: stable-audio-3   (无人声)
    └─→ backends.yaml: symbolic         MIDI/MusicXML,完全确定

加一个新模型 = 在 backends.yaml 加一个块,不动任何 skill。 这是编译器前端/后端分离那一套。

按任务读哪几节

任务读
选哪个后端§2 能力对照
编译一份规格§3 编译顺序(按顺序做,不要跳)
写 style_prompt§4
段落标签对不上§5
后端不理我的规格§6 honors 契约 + §7 降级
要出 ABC/MIDImc-symbolic-score
把编译结果交给后端执行§8
加一个新后端§9
生成完了mc-ai-tell-audit

边界

不归这里归哪
ARR-SPEC 里的内容怎么想出来L1/L2 各 skill
ABC / MusicXML / MIDI 怎么生成mc-symbolic-score
生成回来像不像 AImc-ai-tell-audit
照做率怎么算定义在 mc-ai-tell-audit §3.1;本 skill §6 只管 honors 三档怎么影响计分
具体某个后端的权重放哪、节点叫什么backends.yaml(包内副本)(数据)
风格描述里该写什么风格词mc-style-*(L3)

1. 动笔前必填

必填问法
① 目标后端至少一个。多个就要做 §7 的降级表
② 这次要验证什么是出成品,还是跑 AB?AB 就必须锁定其他变量
③ 种子策略固定种子做对照,还是 best-of-N 挑
④ 哪些字段这个后端做不到查 honors。这决定你别在那些字段上浪费提示词预算

2. 后端能力对照

YuE2SunoMiniMaxACE-StepStable Audio符号
输入风格 + 词 + 可选 ABC散文 + 段落标签散文 + 词 + 参考标签 + 散文散文乐谱
调性/和声★ 精确(ABC)只能提示提示提示—精确
曲式/小节数★ 精确说不动提示提示—精确
人声有有有有无无
可复现seed + best-of-8弱—seedseed完全
音色成品度高最高高中高看音源
商用★ 权重 CC-BY-NC,不可商用看订阅档待核看许可看许可可

选择建议:

目的选
验证规格是否被执行YuE2(唯一能"执行"而非"提示"的)
要成品质感Suno
要完全可复现的对照基准符号路径
纯器乐/氛围Stable Audio
出街商用★ 先解决许可——YuE2 权重是 CC-BY-NC-4.0,不可商用

3. ★ 编译顺序

按顺序做。跳步会产生"提示词里互相矛盾"的问题。

#步骤说明
0过一遍 mc-workflow §3.0 的 20 条自查不过的条目改掉或写明理由再编译——不是准入门槛,是最后一次发现"规格没写完"的机会
1读 backends.yaml 的 honors标出 none 的字段——它们不进提示词,也不计分
2mix_intent.mood 放最前它对其余决定有否决权,必须在提示词靠前的位置
3编译 style_prompt§4
4编译段落标签序列§5。★ 数量必须等于 form 段落数(lint #13)
5编译歌词分块§5.3
6符号路径(若后端支持)→ mc-symbolic-score
7编译 exclude / 负面提示§4.4
8定种子与批量§7.3
9写回 render.<backend>ARR-SPEC 自己记录这次编译的结果

★ 第 9 步别漏:render 段是规格的一部分, 没写回去的话,下一轮 AB 就不知道上次到底送了什么进去。


4. style_prompt:把结构化字段压成散文而不丢信息

4.1 信息来源的优先顺序

从下面这些字段抽取,按这个顺序排列(越靠前越容易被模型采纳):

  1. mix_intent.mood(一句话)
  2. meta.style_layer 对应的风格词(→ L3 skill)
  3. meta.tempo、meta.key
  4. arrangement.roster[].instrument + timbre
  5. groove.feel + push_pull 的方向(不写毫秒,写 "bass playing ahead of the beat")
  6. vocal.persona + vocal.delivery
  7. 年代/制作质感词
4.2 ★ 三条硬规则
  1. 禁用无信息形容词:epic / emotional / beautiful / amazing / masterpiece / perfect (lint #13 软警告)。它们不携带任何可执行信息,只占位置
  2. 数值要转成可听的描述: push_pull: bass −12ms → bass playing slightly ahead of the beat(不要写 −12ms)
  3. 每个词都要能指向一个 roster 项或一个 spec 字段。 ★ 自检动作:把 style_prompt 逐词圈一遍,圈不出来源的词删掉
4.3 一个实测样本(City Pop)
1980s Japanese city pop, 112 BPM, D major, Rhodes electric piano,
16th-note muted clean guitar, electric bass playing ahead of the beat,
gated reverb snare, analog saw synth lead riff,
tenor saxophone solo in the bridge and outro,
female vocal with breathy tone mixed back, warm tape saturation

注意它的构造:年代+风格 → 速度调性 → 逐件乐器带演奏法 → 人声 → 制作质感。 没有一个形容词是空的。

4.4 exclude(负面提示)

从"这首歌不是什么"来写,不是从"不要难听"来写。

实测样本:

["EDM drop", "trap hi-hats", "modern sidechain pumping",
 "orchestral epic", "fade out ending"]

★ 最后一项 fade out ending 值得注意—— 淡出是生成模型的默认结尾,不排除它就一定会得到淡出 (→ mc-arrangement-arch §8 三种结尾)。


5. 段落标签与歌词

5.1 映射表

form[].name → 后端标签:

ARR-SPECSuno / 多数后端
Intro[Intro]
Verse[Verse]
Pre-Chorus[Pre-Chorus]
Chorus[Chorus]
Bridge[Bridge]
Instrumental[Instrumental]
Outro[Outro]

★ 两个映射陷阱:

  1. 大サビ 映射成 [Bridge],不是 [Chorus](→ mc-form §7)。 映射错了,后端会在内省的位置给你一个高能量副歌
  2. 落ちサビ(稀薄编制的副歌)仍然映射成 [Chorus], 稀薄的要求写进 style_prompt 或段落注释,不要靠改标签来表达
5.2 数量必须相等

这条规则的完整表述见 §3 步骤 4(数量必须等于 form 段落数,lint #13),§11 必过清单里也有一遍。单独留一节,是因为它是编译时最容易出错的一处:手动改动歌词段落之后,标签序列很容易忘记同步增删,少一个后端就会自己决定那一段是什么,出错时优先查这里。

5.3 歌词分块
  • 歌词按段落切开,与标签一一对应
  • ★ 段落内的换行会影响断句——它不只是排版,后端会当成乐句边界读
  • 与作词库的接口:LYR-SPEC 的段落 id 必须与 form[].id 一致(→ mc-workflow §5.1)

6. ★ honors 契约:本 skill 最重要的一条

每个后端在 backends.yaml 里声明它对 ARR-SPEC 各字段的执行能力:

取值含义验收时怎么算
exact能精确执行计分,不达标就是它没做到
hint只能作为提示影响生成计分,但权重打折
none根本不接受这个字段★ 排除计分,标注"本后端不支持"
Show full SKILL.md (269 more words)Show less
6.1 为什么 none 必须排除而不是给 0

Suno 从来没被告知精确和弦,拿"和弦没跟上"扣它的分等于惩罚它做不到的事。

★ 更要命的后果:如果 none 给 0 分,那么 一个诚实声明"我做不到声像"的后端,会比一个假装做得到的后端得分更低—— 评分体系会奖励谎报能力。这是一个正确性 bug,不是口径问题。

6.2 编译时怎么用
  1. none 的字段不进提示词——写了也没用,还挤占了有用信息的位置
  2. hint 的字段要写,但别指望——配合 best-of-N
  3. exact 的字段必须写准,这是你花钱买控制力的地方

7. 降级策略

7.1 一份规格,多个后端

不要为每个后端各写一份规格。 写一份,按 honors 降级:

字段YuE2 (exact)Suno (none)怎么降级
form.harmony编译成 ABC—降级为风格描述里的调式/色彩词("含大量下属小调色彩")
form.bars编译成 ABC 小节—降级为段落标签序列的长度暗示,并接受它做不到
groove.push_pull—hint降级为 "playing ahead of the beat"
mix_intentnonenone★ 两边都做不到 → 走后期,或走符号路径
7.2 降级要记录

记录格式见 §10(写进 checks.exemptions),交付清单见 §8.1(写回 render.<backend>)。这里要说清楚的是动机:★ 在 render.<backend> 里记下这次降了哪些字段,否则下一轮看到照做率低,会分不清是规格写错了,还是后端本来就做不到。降级造成的低分和规格错误造成的低分,在照做率上表现得一样,只有当时编译的人知道区别,所以必须当场记下来。

7.3 种子与批量
目的做法
AB 对照固定种子,只改一个字段。改两个就什么都证明不了
出成品best-of-N(YuE2 建议 8),按照做率排序(有度量工具就算,没有就逐字段对照),再用耳朵在前 3 里挑
探索放开种子,但要记录哪个种子出了什么

8. 交给后端执行

本 skill 的产出是各后端的输入值:Suno 的风格描述与段落标签、YuE2 的 ABC 与参数、 ACE-Step / MiniMax / Stable Audio 的 prompt 与参数。 ★ 用什么工具把这些值送进模型,由用户的环境决定——命令行、Python API, 还是 ComfyUI 这类节点式载体,本 skill 不预设、不管理。 backends.yaml 的 transport 只记录"这个后端通常跑在哪",是数据,不是前提。

8.1 交付时给用户什么
后端交出去的东西
Sunostyle_prompt、exclude、带段落标签的歌词或占位音节(§4、§5)
YuE2ABC 文本(mc-symbolic-score)+ genre / tempo 参数;并注明:把这份 ABC 直接喂给合成步骤,跳过模型自己的符号规划
ACE-Step / MiniMax / Stable Audioprompt 与时长等参数,按各自的 accepts 列表

每一项都写回 render.<backend>;用户拿着它到自己的工具里填就行。

8.2 YuE2 的关键接法

YuE2 分两步:符号规划(模型自己写 ABC)→ 从 ABC 合成音频。 ★ 把第一步旁路掉,把 mc-symbolic-score 编译出来的 ABC 直接送进第二步。 这是本库控制力的最高点——规格在这里不再是"提示",而是被执行。 凡是支持 YuE2 的载体都能这样接(官方仓库有脚本,ComfyUI 有原生节点);具体怎么接线是用户环境的事。

YuE2 官方仓库自带 skills/yue2-music/(含"检查音乐不变量")。 ★ 不要重写它——我们只写 ARR-SPEC→ABC 的适配器,模型操作的知识引用它们的。


9. 加一个新后端

  1. 在 backends.yaml 加一个块:id / name / transport / accepts / honors / license
  2. ★ honors 要诚实——宁可标 none 也不要标 hint 充数(理由见 §6.1)
  3. 填 transport(hosted / local)——只是记录它通常跑在哪,不是要求
  4. 跑一遍 example-01-citypop.yaml,看编译产物是否合理
  5. 不改任何 skill

10. 与 ARR-SPEC 的字段对应

字段本 skill
render.suno§4、§5
render.yue§4、§5、§8.3
render.midi→ mc-symbolic-score
checks.exemptions★ 降级记录也写这里

11. 编译完必过

  • mc-workflow §3.0 的 20 条过了一遍,不过的写了理由
  • 查过目标后端的 honors,none 的字段没有进提示词
  • mood 在提示词靠前
  • style_prompt 里每个词都指向一个 spec 字段
  • 没有 epic/emotional/beautiful 这类空词
  • 数值参数转成了可听的描述,没有把毫秒写进提示词
  • 段落标签数量 == form 段落数
  • 大サビ 映射成了 [Bridge]
  • exclude 里排除了淡出结尾(除非确实要淡出)
  • 种子策略明确;做 AB 的话只改了一个变量
  • 降级记录写回了 render.<backend>
  • 生成后先逐字段对照规格(照做率),再用耳朵

附:来源

  • 设计依据:本库架构文档 §2.3(编译器 + 后端能力档案)
  • 后端数据:backends.yaml(包内副本)
  • ⚠ MiniMax Music 商业 API 已对新用户关闭,见 backends.yaml 的档案
  • ⚠ 未确认:YuE2 合成步骤接受的 ABC 方言;它的 musical invariants 与 20 条自查的对应关系
  • 分轨:人声 AI 味审计要先把人声轨分出来,任何分轨工具都行(demucs 一类,或用户载体自带的),见 mc-ai-tell-audit §6.1

© jtydhr88, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/music-composition/skills/mc-render-compile of jtydhr88/music-composition-skills.

Open the folder on GitHubat commit 7adca0c

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All 29 skills in this repo
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Works with

Questions about Mc Render Compile

What does Mc Render Compile do?

The compiler from ARR-SPEC to any generation backend (编译层). An agent skill from jtydhr88/music-composition-skills. Mc Render Compile is an agent skill from jtydhr88/music-composition-skills. The compiler from ARR-SPEC to any generation backend (编译层).

When should I use Mc Render Compile?

Mc Render Compile fits situations like: turning a finished ARR-SPEC into Suno / YuE2 / MiniMax / ACE-Step / symbolic output; choosing a backend; A generation ignored the spec; adding a new backend.

How do I install Mc Render Compile in Claude Code?

Run `npx skills add jtydhr88/music-composition-skills --skill mc-render-compile -a claude-code`. Or copy the skill folder (plugins/music-composition/skills/mc-render-compile in jtydhr88/music-composition-skills) into .claude/skills/mc-render-compile in your project. Claude Code loads it when a task matches its description.

How do I install Mc Render Compile in Codex?

Run `npx skills add jtydhr88/music-composition-skills --skill mc-render-compile -a codex`. Or copy the skill folder (plugins/music-composition/skills/mc-render-compile in jtydhr88/music-composition-skills) into .agents/skills/mc-render-compile in your project. Codex loads it when a task matches its description.

Can I use Mc Render Compile in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jtydhr88/music-composition-skills --skill mc-render-compile -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mc-render-compile, .gemini/skills/mc-render-compile, .github/skills/mc-render-compile and .opencode/skills/mc-render-compile in your project.

What does Mc Render Compile need to run?

SKILL.md names no scripts, command-line tools or credentials: Mc Render Compile is instructions for the agent only. Our summary lists: Python 3.

Does Mc Render Compile access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Mc Render Compile safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Mc Render Compile use?

Mc Render Compile is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mc Render Compile use?

About 2.3k tokens (SKILL.md is roughly 9.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Mc Render Compile?

Skills that share tags, products or a category with Mc Render Compile: Audio Jingle (sanqiufong/slides-from-anything, 132 stars), Minimax DOCX (poco-ai/poco-claw, 1.4k stars), AI Image Generation and Editing (zhayujie/CowAgent, 47k stars) and Minimax XLSX (poco-ai/poco-claw, 1.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mc Render Compile?

jtydhr88 (a GitHub user) maintains it in jtydhr88/music-composition-skills, which has 150 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on September 22, 2026.

Source: jtydhr88/music-composition-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.