Portfolio Case Study Writer
davila7/claude-code-templates
Transform resume bullets into detailed portfolio case studies with context, action, and outcome.
The corpus layer (语料层) - how real recordings are made to testify for or against the textbook rules.
MITAuto-check passed
Install Mc Case Studies
$ npx skills add jtydhr88/music-composition-skills --skill mc-case-studies -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install jtydhr88/music-composition-skills mc-case-studies --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-case-studies .claude/skills/mc-case-studies && rm -rf skills-srcUse ~/.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/
Install the "mc-case-studies" agent skill from https://github.com/jtydhr88/music-composition-skills/tree/main/plugins/music-composition/skills/mc-case-studies into .claude/skills/mc-case-studies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mc-case-studies", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/jtydhr88/music-composition-skills/tree/main/plugins/music-composition/skills/mc-case-studiesType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add jtydhr88/music-composition-skills --skill mc-case-studies -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install jtydhr88/music-composition-skills mc-case-studies --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jtydhr88/music-composition-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/music-composition/skills/mc-case-studies .agents/skills/mc-case-studies && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mc-case-studies" agent skill from https://github.com/jtydhr88/music-composition-skills/tree/main/plugins/music-composition/skills/mc-case-studies into .agents/skills/mc-case-studies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mc-case-studies", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jtydhr88/music-composition-skills --skill mc-case-studies -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install jtydhr88/music-composition-skills mc-case-studies --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jtydhr88/music-composition-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/music-composition/skills/mc-case-studies .cursor/skills/mc-case-studies && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "mc-case-studies" agent skill from https://github.com/jtydhr88/music-composition-skills/tree/main/plugins/music-composition/skills/mc-case-studies into .cursor/skills/mc-case-studies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mc-case-studies", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/jtydhr88/music-composition-skills.git --path plugins/music-composition/skills/mc-case-studies--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add jtydhr88/music-composition-skills --skill mc-case-studies -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install jtydhr88/music-composition-skills mc-case-studies --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jtydhr88/music-composition-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/music-composition/skills/mc-case-studies .gemini/skills/mc-case-studies && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "mc-case-studies" agent skill from https://github.com/jtydhr88/music-composition-skills/tree/main/plugins/music-composition/skills/mc-case-studies into .gemini/skills/mc-case-studies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mc-case-studies", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install jtydhr88/music-composition-skills mc-case-studiesInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add jtydhr88/music-composition-skills --skill mc-case-studies -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/jtydhr88/music-composition-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/music-composition/skills/mc-case-studies .github/skills/mc-case-studies && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "mc-case-studies" agent skill from https://github.com/jtydhr88/music-composition-skills/tree/main/plugins/music-composition/skills/mc-case-studies into .github/skills/mc-case-studies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mc-case-studies", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jtydhr88/music-composition-skills --skill mc-case-studies -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install jtydhr88/music-composition-skills mc-case-studies --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jtydhr88/music-composition-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/music-composition/skills/mc-case-studies .opencode/skills/mc-case-studies && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "mc-case-studies" agent skill from https://github.com/jtydhr88/music-composition-skills/tree/main/plugins/music-composition/skills/mc-case-studies into .opencode/skills/mc-case-studies/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mc-case-studies", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
mc-case-studies- GitHub stars
- 151
- Token cost
- ~1.6k tokens
- SKILL.md length
- 426 words
- Files
- 2
- Skills in repo
- 29
- Repo updated
- First seen
- Licence
- MIT
At a glance
The corpus layer (语料层) - how real recordings are made to testify for or against the textbook rules.
- Works in 8 steps: 语料的三条原则 → 反向 ARR-SPEC → 真值与标注 → …
- Checking whether a rule actually holds in real music
- SKILL.md covers 按任务读哪几节, 边界, 1. 语料的三条原则 and 2. 反向 ARR-SPEC, plus 7 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Mc Case Studies is an agent skill from jtydhr88/music-composition-skills. The corpus layer (语料层) - how real recordings are made to testify for or against the textbook rules. Covers the reverse ARR-SPEC as the unit of the corpus, the corroboration table that turns rules times corpus into hit rates and tiers them as strong rule / tendency / not a rule, the two honesty constraints that keep the table from being circular, why naturally sampled corpora beat curated ones, what this corpus can and cannot measure, and how to look up a worked example. Use when checking whether a rule actually…
Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `reference.md`).
The repository describes itself as: Professional agent skills for composing and arranging popular music. The licence is MIT.
When your agent uses it
- Checking whether a rule actually holds in real music
- A rule and the data disagree
- Annotating a track
- You need a real-world example of a technique
Example prompts
- “/mc-case-studies”
Workflow steps
8 steps, taken from the step headings in SKILL.md.
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 (its code samples are yaml).
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 Case Studies loads about 1.6k tokens when it runs. Until then it costs about 181 tokens; SKILL.md has 426 words of instructions outside code blocks.
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). 426 words, ~1,569 tokens.
Download SKILL.mdSave it as
.claude/skills/mc-case-studies/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- mc-case-studies
- description
- The corpus layer (语料层) - how real recordings are made to testify for or against the textbook rules. Covers the reverse ARR-SPEC as the unit of the corpus, the corroboration table that turns rules times corpus into hit rates and tiers them as strong rule / tendency / not a rule, the two honesty constraints that keep the table from being circular, why naturally sampled corpora beat curated ones, what this corpus can and cannot measure, and how to look up a worked example. Use when checking whether a rule actually holds in real music, when adding a rule, when a rule and the data disagree, when annotating a track, or when you need a real-world example of a technique. 语料、佐证、命中率、实测 ARR-SPEC、反向规格、案例拆解、规则验证。
语料层（Case Studies & Corroboration）
这一层回答一个问题：
教材说的，真实作品到底做不做？
本库的规则不是抄来的，是抄来之后拿 62 首真实多轨验过的。 验过的结果分三档，只有强规则才进 20 条自查表。
按任务读哪几节
| 任务 | 读 |
|---|---|
| 想知道某条规则靠不靠谱 | §4 佐证表 ＋ 仓库规则表 |
| 要加一条新规则 | §4.3 流程 |
| 规则和数据打架了 | ★ §5 |
| 要找一个真实例子 | §6 |
| 要标注一首曲子 | §3.3 |
| 想知道这套语料能测什么 | §2.2 |
边界
| 不归这里 | 归哪 |
|---|---|
| 规则本身的内容 | L1／L2 各 skill |
| 生成结果的验收 | mc-ai-tell-audit |
| 规格写完的检查 | mc-workflow §3.0 |
| 某个风格的语汇 | mc-style-*（L3） |
| 具体曲目的逐段拆解 | reference.md |
1. 语料的三条原则
1.1 ★ 只存结构与参数，不存作品
语料条目里没有音频、没有谱面、没有歌词——只有测出来的数值。
这既是版权上的必需，也是方法上的正确： 我们要的是"真实作品的参数分布"，不是作品本身。
1.2 ★ 自然抽样胜过策展
语料不精选，用自然抽样。
策展会把语料偏向"规则成立"的那一侧—— 你挑的是你认为的好例子，而你认为的好例子就是符合你已有理论的例子。
自然抽样自动供给反例。
所以最终用的是 Cambridge-MT 的 60+ 首自然样本，不筛选。 这也顺带取消了另找 MIDI/分轨语料的必要（它原本的唯一用途是给分轨工具做标定， 而真分轨直接消除了这个需求）。
1.3 语料的单元是"反向 ARR-SPEC"
不是音频文件，是从音频反推测出来的一份 ARR-SPEC。 选这个单元是因为它和我们的产出用同一套字段：结构、编制进退场、能量曲线都记在同样的位置， 所以佐证表能直接拿语料条目和规则判据对齐，不用先做一层格式转换。 字段长什么样、哪些标记是诚实留白而非漏填，见 §2.1。
2. 反向 ARR-SPEC
2.1 长什么样
仓库实测语料，63 条。每条头部就写明了性质：
# 实测 ARR-SPEC（语料条目）—— 由 仓库的反推工具 自动生成
# ★ 只存结构与参数，不存谱面与歌词。intent / hooks 等 TODO 项需人工填。★ 注意那些 # TODO 和 # 估算，非实测 的注释——
它们是诚实标注，不是没做完。
intent.one_thing 这类东西机器测不出来，硬填就是造假。
2.2 ★ 这套语料能测什么、不能测什么
Cambridge-MT 是未混的原始多轨，不是母带成品。 所以：
| 能测 | 不能测 |
|---|---|
| 段落边界、小节数 | DR / 响度（未混） |
| 拍速 | 立体声宽度（未混） |
| 能量曲线、减法事件 | 频谱质心的绝对值（未混 + 无母带） |
| 编制进退场（有真分轨！） | 制作质感 |
| onset 相对网格偏移 | |
| 和弦（chroma 匹配） |
★ 测不了的规则要标出来，不许硬跑。
rules.yaml 里这类规则标 evidence: unusable_here，
仓库的佐证工具 跳过而不是给一个假数字。
本库的语料是多轨分轨，不含成品混音，所以这类规则目前没有数字。
3. 真值与标注
3.1 为什么需要人工真值
算法测出来的段落边界本身可信度存疑（rules.yaml 里标 no_truth）。
没有一份独立的真值，就没法判断一个异常的命中率到底是规则不成立，还是算法本身测错了——
§5.2 那次"硬编码阈值把命中率压到 16%"就是靠人工真值才翻案的实例。
所以做了一批人工标注当真值，校准结果见 §3.2。
3.2 ★ 一个被验证过的担心
用户担心"我不是专家，标的边界不准"。实测结果推翻了这个担心：
人工标记与算法的偏差中位数 = 0.0 秒。
★ 结论：段落边界是感知判断，不是品味判断。 非专家标出来的边界和算法一致——这说明这件事本来就不需要专家。 （能标的和不能标的要分清：边界能标，"这段好不好听"不能标。）
校准结果：7 首人工标注，算法命中 72%，偏差中位 0.0 s。
3.3 怎么标注
标注工具与 7 份标注文件在仓库里，不随包发布。
已标注的 7 首： AMContra_HeartPeripheral、APZX_CyberMower、DigitalHumans_Electrvm、 Forkupines_Semantics、MERCMusic_Knockout、MR1103_Flags、SimonLyn_Copper
4. ★ 佐证表：规则 × 语料 → 命中率 → 分档
4.1 两条诚实约束
违反了，佐证表就是自欺：
| # | 约束 |
|---|---|
| 1 | ★ 判据不能复用生成器的定义。 反向 ARR-SPEC 里的 subtraction_events 是 反推工具 按"较前段降 >1 dB"自动填的；拿它去验"真编曲有没有减法"是循环论证。所以涉及减法的判据一律直接从 energy_curve 重算，用一个有音乐意义的阈值（≥3 dB），不读 subtraction_events 字段 |
| 2 | ★ 语料测不了的规则要标出来，不许硬跑（见 §2.2） |
evidence 的三个取值：
| 值 | 含义 |
|---|---|
ok | 本语料能验 |
unusable_here | 本语料性质不对，需要成品混音语料 |
no_truth | 缺真值（如段落边界），判据本身可信度存疑 |
4.2 第一轮结果
| 规则 | 命中 | 档 |
|---|---|---|
| 能量曲线必须有下降 | 62/62 | 强规则 |
| 编制必须有乐器中途进场 | 59/59 | 强规则 |
| 至少一件乐器提前退场 | 59/59 | 强规则 |
| 不该全声部严格对齐网格 | 62/62 | 强规则 |
| 开场不该把乐器一次铺满 | 54/59 | 强规则 |
| 全曲至少一处明显减法 | 36/62 = 70% | 倾向 |
| 最高潮前应有能量回落 | 全量 22% ／ 人工真值 5/7 | ★ 矛盾，只当手法 |
| 编制在最高潮最密 | 25/59 = 42% | 挂起 |
4.3 分档决定什么
| 档 | 在 skill 里怎么写 | 进 lint 吗 |
|---|---|---|
| 强规则 | "必须" | ✅ 硬检查 |
| 倾向 | "通常应该" | ❌，进"写完后必过"清单 |
| 手法 | "可以这么做" | ❌，只在诊断路径里出现 |
| 挂起 | 不写进 skill | ❌，留在实验记录里等更多数据 |
Show full SKILL.md (176 more words)Show less
4.4 加一条规则的流程
- 在 仓库规则表 里写：规则 + 可跑的判据 +
evidence - 检查是否违反 §4.1 的两条约束
- 用仓库的 corroborate 工具跨语料跑判据（维护者步骤，不随包发布）
- 按命中率分档（§4.3）
- ★ 把结果写进 仓库实验记录，然后才改 skill
规则集是有版本的。 改了规则集就要重跑，否则前后轮不可比。
5. ★ 规则和数据打架时怎么办
这是本 skill 最重要的一节。
5.1 先问：指标测的是不是那回事
不要拿指标去推翻教科书。
实例：「最高潮前应有能量回落」在全量 62 首里只有 22%， 但在 7 首人工标注真值里是 5/7。
差距的来源：算法用能量峰值定位"最高潮"，而人耳不是。 所以 22% 那个数测的不是这条规则，测的是"能量峰值前有没有回落"。
★ 结论：这条不降级为伪规则，也不升为强规则——列为手法。
5.2 另一个实例：硬编码阈值造成的假低命中率
仓库的佐证工具 里曾硬编码"12 dB 动态范围"， 把规则 ARR-R-012 的命中率压到 16%，差点被报成"教科书是错的"。
证伪方法：拿 7 首人工标注真值测 → 5/7 = 71%。 修法：改成在归一化的 0–10 单位上打分，不假装能还原 dB。
★ 通用教训：一个异常低的命中率，先怀疑判据，再怀疑规则。
5.3 佐证表与 lint 问的不是同一个问题
| 问什么 | |
|---|---|
| 佐证表 | 人类一般会不会这么做 |
| 20 条自查 | 不这么做会不会听起来像 AI |
★ 所以 70% 的命中率不会机械地把一条 lint 检查降级。 一件事可能只有 70% 的人做，但不做的那 30% 恰好都听起来像机器。
6. 找一个真实例子
| 你要 | 去哪 |
|---|---|
| 某个技法的真实用例 | reference.md 的逐段拆解 |
| 某个和弦进行用在哪首歌 | mc-progressions reference.md §7 名曲索引 |
| 一首曲子的完整实测参数 | 仓库实测语料（63 条） |
| 段落边界的人工真值 | 仓库标注数据（7 条） |
| 配器/混合音色的谱例 | mc-orchestration reference.md §4 |
| 对位的谱例 | mc-counterpoint reference.md |
6.1 逐段拆解的格式
四者对齐：时间码 / 段落 / 编制事件 / 和声。 格式与反向 ARR-SPEC 一致，人工补上机器测不出的部分（intent、hooks、为什么）。
7. 与其他层的接口
| 方向 | 内容 |
|---|---|
| → L1/L2 各 skill | 提供"实测：62/62，强规则"这样的证据等级标注 |
| → 20 条自查 | 只有强规则才能进自查表 |
→ mc-ai-tell-audit | 零点校准（不相关成品得 63–66）就是在这套语料上测的 |
| ← 仓库的反推工具 | 语料条目的生成器 |
| ← 成品混音语料（★ 缺） | §2.2 那些 unusable_here 的规则需要它 |
8. 用完必过
- 引用规则时带了证据等级（强规则/倾向/手法），不是光说"教材说"
- 新加的判据没有复用生成器的定义（§4.1 约束 1）
- 语料测不了的规则标了
unusable_here，没有硬跑出假数字 - 命中率异常低时，先怀疑了判据（§5.2）
- 没有拿佐证表的命中率机械地给 lint 检查降级（§5.3）
- 改了规则集就重跑并记进 仓库实验记录
附：现状
已有：
- 仓库实测语料 —— 63 条实测 ARR-SPEC（Cambridge-MT）
- 仓库规则表（规则集带版本号）
- 仓库标注数据 —— 标注工具 + 7 条人工真值
_corpus/multitracks/—— 11 项（含_index.json、_parts.json）- 仓库实验记录：度量标定 / 拍速校准 / 规则佐证第一轮
- 拍速校准：56 首真值
© 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
SKILL.md and 1 other file in plugins/music-composition/skills/mc-case-studies of jtydhr88/music-composition-skills.
- SKILL.md
- reference.md
Open the folder on GitHubat commit 7adca0c
Compare with similar skills
Mc Case Studies next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Mc Case Studies this skilljtydhr88/music-composition-skills | 151 | — | ~1.6k | Automated safety check: Pass | MIT | |
| Portfolio Case Study Writerdavila7/claude-code-templates | 32k | 2 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Web3 Role Misconfiguration Case Studytradecatlabs/vibe-coding-cn | 17k | 2 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Case StudyOwl-Listener/designer-skills | 2.9k | 1 repos | ~523 | Automated safety check: Pass | MIT | |
| Recordingcodewhale-hq/Codewhale | 41k | — | ~540 | Automated safety check: Pass | MIT | |
| Case Study Writeupmohitagw15856/pm-claude-skills | 1.4k | — | ~936 | Automated safety check: Pass | MIT |
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Questions about Mc Case Studies
What does Mc Case Studies do?
The corpus layer (语料层) - how real recordings are made to testify for or against the textbook rules. Mc Case Studies is an agent skill from jtydhr88/music-composition-skills. The corpus layer (语料层) - how real recordings are made to testify for or against the textbook rules.
When should I use Mc Case Studies?
Mc Case Studies fits situations like: checking whether a rule actually holds in real music; A rule and the data disagree; annotating a track; you need a real-world example of a technique.
How do I install Mc Case Studies in Claude Code?
Run `npx skills add jtydhr88/music-composition-skills --skill mc-case-studies -a claude-code`. Or copy the skill folder (plugins/music-composition/skills/mc-case-studies in jtydhr88/music-composition-skills) into .claude/skills/mc-case-studies in your project. Claude Code loads it when a task matches its description.
How do I install Mc Case Studies in Codex?
Run `npx skills add jtydhr88/music-composition-skills --skill mc-case-studies -a codex`. Or copy the skill folder (plugins/music-composition/skills/mc-case-studies in jtydhr88/music-composition-skills) into .agents/skills/mc-case-studies in your project. Codex loads it when a task matches its description.
Can I use Mc Case Studies 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-case-studies -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-case-studies, .gemini/skills/mc-case-studies, .github/skills/mc-case-studies and .opencode/skills/mc-case-studies in your project.
What does Mc Case Studies need to run?
SKILL.md names no scripts, command-line tools or credentials: Mc Case Studies is instructions for the agent only.
Does Mc Case Studies 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 Case Studies 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 Case Studies use?
Mc Case Studies 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 Case Studies use?
About 1.6k tokens (SKILL.md is roughly 6.3k 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 Case Studies?
Skills that share tags, products or a category with Mc Case Studies: Portfolio Case Study Writer (davila7/claude-code-templates, 32k stars), Web3 Role Misconfiguration Case Study (tradecatlabs/vibe-coding-cn, 17k stars), Case Study (Owl-Listener/designer-skills, 2.9k stars) and Recording (codewhale-hq/Codewhale, 41k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Mc Case Studies?
jtydhr88 (a GitHub user) maintains it in jtydhr88/music-composition-skills, which has 151 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.