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Code Taste Review Skill · microck | DecimalAI

code reviewby microck

Code Taste Review Skill

microck/taste-check

Source microck/ordinary-claude-skills ↗License NOASSERTION

Review code using Linus Torvalds' "good taste" philosophy. Eliminates defensive code, special cases, and deep nesting.

SkillScore73Maintained

Automatic

Coding pack

Last verified 8/21/2026

What it changes

Passed 16 of 22 test cases with the skill, and 14 of 22 without it.

Without skill64%

With skill73%

Checks it passed that the model alone missed

Recommends targeting pure functions under 50 lines.
Identifies mutating properties on the returned object as a code smell.
Asks the user to clarify review scope among file path, git diff, or pasted code snippet.

and 5 more in the full report

6 fixed10 passed either way2 still failing4 broke

Pass-rate lift: +9 pp
Tokens: +52%
Agent turns: 0%
Response time: -22%

Model gemini-3.6-flashMeasured 8/21/2026v116 of 22 cases passed with skill

Tokens, turns and time describe resource use, not a dollar price.

Use this skill

Save it into your project's skills folder. Claude Code picks it up in its next session.

curl -fsSL --create-dirs -o .claude/skills/microck-taste-check/SKILL.md https://app.decimal.ai/s/microck-taste-check/SKILL.md

Want it pinned and updatable? pip install "decimalai>=0.15", then decimalai skills add microck-taste-check, and start Claude Code with claude --plugin-dir "$(git rev-parse --show-toplevel)/.agents"

1 · One-time — install the CLI (docs ↗)

pip install "decimalai>=0.15"

2 · Get the skill — writes .agents/skills/microck-taste-check/ and pins it in .decimal/skills.lock

decimalai skills add microck-taste-check

No CLI? Get the same file — curl · raw file

curl -fsSL --create-dirs -o .claude/skills/microck-taste-check/SKILL.md https://app.decimal.ai/s/microck-taste-check/SKILL.md

Raw file: /s/microck-taste-check/SKILL.md ↗ · machine-readable summary at /s/microck-taste-check.json ↗

Safety checks: 3 of 3 passed

Code scan CleanIntent PassedContent Clean

See it in action

One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.

1 of 6

Prompt

I'd like a Linus Torvalds style code taste audit on my frontend TypeScript file. Please give me an overall quality rating, highlight critical blockers, inspect smells like over-defensive defaults or deep branching, provide recommended code fixes for every problem found, and outline the refactoring priority order.

What was checked, without and with the skill
Checked	Without skill	With skill
Asks the user to clarify review scope among file path, git diff, or pasted code snippet.	Missed	Passed

case-03 · gemini-3.6-flash · 8/21/2026 · answers are capped and redacted

What's inside

The instructions your model reads when the skill loads.

SKILL.md167 linessingle file

Code Taste Review Skill

你是 Linus Torvalds，现在以你的"好品味"标准审查代码。

核心哲学

1. 充分相信上游数据（Trust Upstream Data）

类型定义中没有不必要的 ? 可选标记
代码中没有 ?? 0 或 || defaultValue 的防御性默认值
数据采集层保证字段完整性，下游可以信任

2. 消除特殊情况（Eliminate Special Cases）

避免 if (hasX) { useX } else { useY } 的分支
统一数据格式，不要有"某些节点特殊"的差异
用类型系统区分不同情况，而不是运行时检查

3. 零后置修改（No Post-Return Mutation）

函数调用后没有 result.xxx = yyy 的修改
通过参数传递上下文，而不是返回后修改
函数返回最终状态，不再被修改

4. 零字符串拼接（No String Concatenation）

没有 css += xxx 的字符串拼接
CSS/字符串生成逻辑集中，一次性组装
通过参数传递额外内容，而不是返回后拼接

5. 单一职责（Single Responsibility）

函数不超过 100 行（理想 < 50 行）
函数名清晰表达单一职责
复杂函数拆分为多个纯函数

6. 控制缩进层级（Limit Nesting）

最大缩进不超过 3 层
使用提前返回（early return）减少嵌套
提取嵌套逻辑为独立函数

7. 提取纯函数（Extract Pure Functions）

没有超过 10 行的 IIFE
复杂计算逻辑提取为独立函数
函数是纯函数（无副作用，可独立测试）

Track record

Benchmark

16 / 22

test cases passed, vs 14 / 22 without

Adoption

Users: 1
Times used: 16

Safety and source details

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v4 — 8/3/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

microck / ordinary-claude-skills

skills_all/taste-check/SKILL.md

View on GitHub ↗

License NOASSERTIONImported 8/3/2026Version v1

More skills from the same source and nearby in the registry, with their headline benchmark lift.

Ready to supercharge your AI agents?

See it in action

What's inside

Code Taste Review Skill

核心哲学

1. 充分相信上游数据（Trust Upstream Data）

2. 消除特殊情况（Eliminate Special Cases）

3. 零后置修改（No Post-Return Mutation）

4. 零字符串拼接（No String Concatenation）

5. 单一职责（Single Responsibility）

6. 控制缩进层级（Limit Nesting）

7. 提取纯函数（Extract Pure Functions）

Track record

Benchmark

Adoption

SkillSafety

Source

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