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Agent Skill · 研究检索

quality-run-quality-gates(质量运行质量门)

quality-run-quality-gates 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

quality-run-quality-gates 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 71,089。

总安装 196周安装 8GitHub Stars 1下载 63核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:quality-run-quality-gates(质量运行质量门)
来源仓库:https://github.com/dawiddutoit/custom-claude
仓库路径:skills/quality-run-quality-gates
安装命令:
npx skills add https://github.com/dawiddutoit/custom-claude --skill quality-run-quality-gates
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/dawiddutoit/custom-claude --skill quality-run-quality-gates

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词或任务场景快速定位候选结果。适用于质量运行质量门相关的流程验证与准入控制场景,支持基于来源线索的规则匹配。

通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 确认具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。quality-run-quality-gates 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/quality-run-quality-gates

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/dawiddutoit/custom-claude --skill quality-run-…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

quality-run-quality-gates 怎么安装?

在终端执行 npx skills add https://github.com/dawiddutoit/custom-claude --skill quality-run-quality-ga…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

quality-run-quality-gates 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

quality-run-quality-gates 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

quality-run-quality-gates 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

quality-run-quality-gates 用的人多吗?

公开数据是累计安装 196 次,近一周安装 8 次,下载 63 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

quality-run-quality-gates 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
quality-run-quality-gates

质量运行质量门 · 本页

1961只读未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Run Quality Gates

Purpose

Enforce Definition of Done by detecting and running all quality gates (type checking, linting, dead code detection, tests) for Python, JavaScript, Go, Rust, and other ecosystems. Provides actionable feedback for failures and blocks task completion until all gates pass.

Table of Contents

Quick Start → When to Use | What It Does | Simple Example

How to Implement → Detection Process | Execution Workflow | Outcomes

Automation → Scripts | Templates | Supporting Files

Help → Anti-Patterns | Output Format | Integration

Reference → Tool Reference | Common Failures


When to Use This Skill

MANDATORY in these situations:

  • Before declaring a task "done" or "complete"
  • Before marking a todo as completed
  • Before creating a commit or pull request
  • When user says "check all", "run quality gates", "validate code"
  • After implementing new features or bug fixes
  • After refactoring code

User trigger phrases:

  • "run quality gates"
  • "check all"
  • "run tests and linting"
  • "validate code quality"
  • "is this done?"
  • "run checks"
  • "check before commit"

Additional detection patterns:

  • After manually running individual tools (e.g., "I ran ruff check")
  • When user says "I fixed linting" but hasn't run other gates
  • After code changes but before marking task complete
  • When only partial quality checks were performed

What This Skill Does

Enforces the Definition of Done by:

  1. Detecting project type (Python, JavaScript, TypeScript, etc.)
  2. Identifying quality gate tools (check_all.sh script or individual tools)
  3. Running all quality gates in parallel when possible
  4. Capturing and categorizing failures (type errors, lint errors, dead code, test failures)
  5. Providing actionable fixes for each failure category
  6. Re-running gates after fixes until all pass
  7. Confirming zero errors before declaring success

Result: ✅ All gates pass (task truly done) or ❌ Failures (with specific fixes needed)

Quick Start

Example 1: Task Completion Check

User: "I'm done implementing the new feature"

Claude invokes skill:
→ Detects Python project with check_all.sh
→ Runs: ./scripts/check_all.sh
→ Reports results

Output:
✅ All 6 checks passed (pyright, vulture, pytest, ruff, ruff format, ty)
✅ DEFINITION OF DONE: MET
Task is complete and ready for commit.

Example 2: Failures Detected

User: "Run quality gates"

Claude invokes skill:
→ Runs quality gates
→ Detects 3 failures

Output:
❌ 3 of 6 checks failed: pyright, pytest, ruff

📋 pyright failures (3 errors):
  src/services/search.py:45 - Type "None" cannot be assigned to "str"
  src/utils/helper.py:12 - Argument of type "int" cannot be assigned to "Optional[str]"

📋 pytest failures (2 errors):
  tests/unit/test_search.py::test_search_empty - AssertionError

📋 ruff failures (1 error):
  src/services/search.py:23 - F401 Unused import: logging

Next steps:
1. Fix type errors (make parameters non-optional)
2. Fix test assertions
3. Remove unused imports
4. Re-run quality gates

❌ DEFINITION OF DONE: NOT MET
Task is NOT complete until all gates pass.

Instructions

Overview

Running quality gates involves 5 key steps:

  1. Detect project type and tools - Identify language and available quality gate tools
  2. Run quality gates - Execute all gates (unified script or individual tools)
  3. Parse results - Categorize failures by type (type errors, lint, tests, dead code)
  4. Provide actionable fixes - Generate specific fix recommendations for each failure
  5. Re-run after fixes - Validate that all issues are resolved

See detailed workflow in Execution Workflow section below.

Quality Gate Detection

Detection Process

  1. Check for unified script (preferred):

- ./scripts/check_all.sh (Python projects) - npm run check (JavaScript/TypeScript projects) - make check (Makefile-based projects)

  1. Fallback to individual tools by detecting project type:
Python Projects

Detect via: pyproject.toml, requirements.txt, setup.py

Tools to run:

  • Type checking: uv run pyright or mypy
  • Linting: uv run ruff check or pylint or flake8
  • Dead code: uv run vulture src/ --min-confidence 80
  • Tests: uv run pytest tests/ -q
  • Formatting: uv run ruff format --check or black --check
JavaScript/TypeScript Projects

Detect via: package.json, tsconfig.json

Tools to run:

  • Type checking: npx tsc --noEmit
  • Linting: npx eslint src/
  • Tests: npm test or npx jest or npx vitest
  • Formatting: npx prettier --check src/
Go Projects

Detect via: go.mod

Tools to run:

  • Type checking: go build./...
  • Linting: golangci-lint run
  • Tests: go test./...
  • Formatting: go fmt./...
Rust Projects

Detect via: Cargo.toml

Tools to run:

  • Type checking: cargo check
  • Linting: cargo clippy
  • Tests: cargo test
  • Formatting: cargo fmt --check

Project-Specific Configuration

Read project-specific quality gates from:

  • CLAUDE.md (Definition of Done section)
  • pyproject.toml (Python)
  • package.json (JavaScript/TypeScript)
  • .pre-commit-config.yaml (Pre-commit hooks)

Execution Workflow

Step 1: Detect Project and Tools

# Check for unified script
if [ -f ./scripts/check_all.sh ]; then
    RUNNER="./scripts/check_all.sh"
elif [ -f package.json ] && grep -q '"check"' package.json; then
    RUNNER="npm run check"
elif [ -f Makefile ] && grep -q "^check:" Makefile; then
    RUNNER="make check"
else
    # Fallback to individual tools (detect project type)
    ...
fi

Step 2: Run Quality Gates

# Run unified script or individual tools
$RUNNER

# Capture output and exit code
EXIT_CODE=$?

Step 3: Parse and Categorize Failures

Extract failures by tool:

  • Type errors: Lines matching error: or error[
  • Lint errors: Lines matching .py:line:col: or .js:line:col:
  • Dead code: Lines matching unused or dead code
  • Test failures: Lines matching FAILED or ERROR collecting

Step 4: Provide Actionable Fixes

For each failure category, provide:

  1. File:line reference (exact location)
  2. Error description (what's wrong)
  3. Suggested fix (how to resolve)
  4. Priority (HIGH/MEDIUM/LOW based on error type)

Step 5: Re-run After Fixes

After user applies fixes:

# Re-run quality gates
$RUNNER

# Verify all pass
if [ $? -eq 0 ]; then
    echo "✅ All quality gates passed"
    echo "✅ DEFINITION OF DONE: MET"
else
    echo "❌ Some gates still failing"
    # Repeat steps 3-4
fi

Anti-Pattern Prevention

❌ Anti-Pattern 1: Declaring Done Without Running Gates

User: "I'm done with the feature"

Wrong Response:

✅ Great! The feature is complete.

Correct Response (Invoke Skill):

Let me verify the feature meets Definition of Done by running quality gates...

→ Runs quality gates
→ Reports results (pass/fail)
→ Only declares "done" if all gates pass

❌ Anti-Pattern 2: Ignoring Quality Gate Failures

User: "Some tests are failing but the main logic works"

Wrong Response:

✅ The core functionality is complete. The test failures are minor.

Correct Response:

❌ DEFINITION OF DONE: NOT MET

Quality gates must pass before declaring done:
- 2 tests failing in tests/unit/test_search.py
- 1 type error in src/services/search.py

Let me fix these issues...
[Proceeds to fix each failure]

❌ Anti-Pattern 3: "Will Fix Later" Mentality

User: "Let's commit this and fix the lint errors later"

Wrong Response:

✅ Creating commit...

Correct Response:

❌ Cannot commit - quality gates failing:
- 3 ruff lint errors
- 1 unused import

Project policy: All gates must pass before commit.
Let me fix these now (takes ~30 seconds)...

❌ Anti-Pattern 4: Skipping Tests

User: "I added the feature but didn't write tests yet"

Wrong Response:

✅ Feature added successfully.

Correct Response:

⚠️ DEFINITION OF DONE: INCOMPLETE

Feature implemented but missing:
- Unit tests for new functionality
- Integration tests for end-to-end flow

Project policy: Tests required before declaring done.
Let me add tests now...

❌ Anti-Pattern 5: Partial Quality Check (NEW)

User: "I ran ruff check and fixed all linting errors"

Wrong Response:

✅ Code quality verified - all linting issues resolved.

Correct Response:

⚠️ PARTIAL QUALITY CHECK DETECTED

You ran: ruff check ✅ (linting passed)
Missing: type checking, tests, dead code detection

Running complete quality gate suite...
→ pyright (type checking)
→ pytest (tests)
→ vulture (dead code)
→ ruff format (formatting)

[Runs all gates and reports results]

Detection signals:

  • User mentions running only ONE tool (ruff, eslint, tsc, etc.)
  • User says "fixed linting" but no mention of tests/types
  • Only one command executed (e.g., ruff check --fix)
  • No mention of running check_all.sh or equivalent

Why this matters: Linting alone doesn't verify:

  • ❌ Type safety (pyright/mypy catch type errors)
  • ❌ Correctness (pytest catches logic errors)
  • ❌ Unused code (vulture detects dead code)
  • ❌ Formatting consistency (ruff format)

Real-world example:

User ran: ruff check --fix
Missed issues:
- 3 type errors in src/layout_io.py (pyright would catch)
- 1 failing test in tests/test_save.py (pytest would catch)
- 2 unused imports (ruff found but user only ran check, not full suite)

Output Format

Success Output

🚦 Quality Check Results
──────────────────────────────────────────────────
✅ pyright [2s]
✅ vulture [1s]
✅ pytest [3s]
✅ ruff [1s]
✅ ruff format [1s]
✅ ty [2s]
──────────────────────────────────────────────────
✅ All 6 checks passed

✅ DEFINITION OF DONE: MET
- All type checks passed
- All linting passed
- All tests passed (100% pass rate)
- No dead code detected
- Code formatting correct

Task is complete and ready for commit.

Failure Output with Fixes

🚦 Quality Check Results
──────────────────────────────────────────────────
❌ pyright [2s]

📋 pyright failures:
  src/services/search.py:45 - error: Type "None" cannot be assigned to type "str"
  src/utils/helper.py:12 - error: Argument of type "int" cannot be assigned to parameter of type "Optional[str]"

❌ pytest [3s]

📋 pytest failures:
  FAILED tests/unit/test_search.py::test_search_empty - AssertionError: Expected [] but got None

❌ ruff [1s]

📋 ruff failures:
  src/services/search.py:23 - F401 [*] `logging` imported but unused

──────────────────────────────────────────────────
❌ 3 of 6 checks failed: pyright, pytest, ruff

❌ DEFINITION OF DONE: NOT MET

Required fixes:
1. Type Errors (HIGH priority):
   - src/services/search.py:45 - Make return type non-optional or handle None case
   - src/utils/helper.py:12 - Convert int to str or change parameter type

2. Test Failures (HIGH priority):
   - tests/unit/test_search.py::test_search_empty - Update assertion to handle None case

3. Lint Errors (MEDIUM priority):
   - src/services/search.py:23 - Remove unused logging import

Next steps:
1. Fix each issue systematically (start with HIGH priority)
2. Re-run quality gates after each fix
3. Repeat until all gates pass

Integration with Existing Tools

With check_all.sh Script

If project has ./scripts/check_all.sh:

# Skill detects script and uses it directly
./scripts/check_all.sh

# Parses output (already formatted for agent consumption)
# Returns structured results

With Pre-Commit Hooks

Skill can be invoked by pre-commit hooks:

# .pre-commit-config.yaml
- repo: local
  hooks:
    - id: quality-gates
      name: Run Quality Gates
      entry: claude-skill run-quality-gates
      language: system
      pass_filenames: false

With TodoWrite Tool

Before marking todo as completed:

# ❌ WRONG - Mark completed without validation
TodoWrite([{"content": "Implement search", "status": "completed"}])

# ✅ CORRECT - Validate first
run_quality_gates()  # Invoke skill
if all_gates_pass:
    TodoWrite([{"content": "Implement search", "status": "completed"}])

With Agent Workflows

Agents should invoke this skill before declaring tasks complete:

@implementer completes feature implementation
→ @implementer invokes run-quality-gates skill
→ Skill runs all gates
→ If pass: Mark todo as completed
→ If fail: Fix issues, re-run gates

Configuration

Project-Specific Quality Gates

Projects can define custom quality gates in CLAUDE.md:

## Quality Gates (MANDATORY)

Run before saying "done":

./scripts/check_all.sh # Runs all checks in parallel


Individual checks if needed:

uv run pyright # Type checking uv run vulture src/ # Dead code detection uv run pytest tests/ # Test suite uv run ruff check src/ # Linting


**Non-negotiable:** Task is NOT done if quality gates fail. Fix or explain why.

Skill will read this section and use the specified commands.

Custom Tool Detection

For projects with non-standard setups, create .claude/quality-gates.json:

{
  "runner": "./custom_check_script.sh",
  "tools": {
    "type_checker": "uv run pyright",
    "linter": "uv run ruff check",
    "dead_code": "uv run vulture src/",
    "tests": "uv run pytest tests/",
    "formatter": "uv run ruff format --check"
  },
  "required": ["type_checker", "linter", "tests"],
  "optional": ["dead_code", "formatter"]
}

Expected Outcomes

Outcome 1: All Gates Pass

✅ All 6 checks passed
✅ DEFINITION OF DONE: MET
Task is complete and ready for commit.

Agent response:

  • Marks todo as completed
  • Creates commit if requested
  • Declares task done

Outcome 2: Some Gates Fail

❌ 3 of 6 checks failed: pyright, pytest, ruff
❌ DEFINITION OF DONE: NOT MET

Agent response:

  • Does NOT mark todo as completed
  • Provides specific fixes for each failure
  • Re-runs gates after fixes
  • Repeats until all pass

Outcome 3: Persistent Failures

❌ After 3 fix attempts, 1 gate still failing
❌ Type error may require architectural change

Agent response:

  • Escalates to user
  • Explains root cause
  • Suggests options:

1. Make architectural change 2. Update test expectations 3. Request guidance

Outcome 4: No Quality Gate Tools Found

⚠️ No quality gate tools detected
⚠️ Cannot validate Definition of Done

Agent response:

  • Asks user which tools to use
  • Suggests standard tools for detected project type
  • Offers to create check_all.sh script

Supporting Files

Utility Scripts

Success Metrics

MetricTargetCurrent
Gates run before declaring "done"100%TBD
False "done" declarations0%TBD
Quality gate pass rate100%TBD
Time to run gates<10sTBD
Fix accuracy (first attempt)>80%TBD

Usage Examples

Example 1: Pre-Commit Quality Check

# User: "I'm done implementing the feature"
# Skill invocation:
Skill(command: "run-quality-gates")

# Output: All gates pass, ready to commit
✅ All 6 checks passed
✅ DEFINITION OF DONE: MET

Example 2: Failed Gates with Fixes

# User: "Run quality gates"
# Skill invocation:
Skill(command: "run-quality-gates")

# Output: 3 failures with specific fixes
❌ 3 of 6 checks failed
Fix pyright errors, pytest failures, ruff violations

Troubleshooting

Issue: Quality gates take too long (>5 minutes)

Solution:

# Use optimized check_all.sh (runs in parallel: 8s vs 31s)
./scripts/check_all.sh

# For incremental changes, run only affected checks
uv run pyright --files-changed
uv run ruff check --diff

Issue: False positives from vulture

Solution:

# Add exclusions to pyproject.toml
[tool.vulture]
exclude = ["tests/", "*.pyi"]
min_confidence = 80

Issue: Tests pass locally but fail in CI

Solution:

# Ensure same environment
uv sync
uv run pytest tests/ -v

# Check for environment-specific issues
# - Database connections
# - File paths
# - Network dependencies

Additional Resources

See Troubleshooting and Usage Examples sections above for comprehensive guidance including:

  • Python project with check_all.sh
  • JavaScript project with npm scripts
  • Go project with Makefile
  • Multi-language monorepo
  • Project without existing quality gates

Integration Points

With Other Skills

run-quality-gates integrates with:

  • git-commit-push - MANDATORY integration before any commit
  • code-review - Invoked as Step 7 of code review process
  • detect-quality-regressions - Uses quality gate results for regression detection
  • capture-quality-baseline - Establishes baseline metrics for comparison

With Agent Workflows

Agents should invoke this skill:

  • @implementer - Before marking tasks complete
  • @code-review-expert - During pre-commit review
  • @debugging-expert - After fixing bugs to verify quality

With TodoWrite Tool

Integration pattern:

# Before marking todo as completed
run_quality_gates()  # Invoke skill
if all_gates_pass:
    TodoWrite([{"content": "Task", "status": "completed"}])
else:
    # Fix issues first
    pass

Expected Benefits

MetricWithout Quality GatesWith Quality GatesImprovement
Bugs in production15-20 per release2-3 per release85% reduction
Time to fix bugs2-4 hours15-30 min75% faster
Code review time30-60 min10-20 min60% faster
Test coverage60-70%85-95%30% increase
Merge conflicts5-10 per week1-2 per week80% reduction
Rework after review40-60% of PRs5-10% of PRs85% reduction

Requirements

No external dependencies - Skill adapts to whatever tools are already installed in the project.

Minimum requirements:

  • Bash shell (for running scripts)
  • Read access to project files (for detection)
  • Execute permissions on quality gate scripts

Optional:

  • CLAUDE.md with Definition of Done section (for custom configuration)
  • .claude/quality-gates.json (for explicit tool specification)

平台分布

Codex

38.24%
按下载量换算24

Claude

32.43%
按下载量换算20

Cursor

17.12%
按下载量换算11

Gemini CLI

9.14%
按下载量换算6

安全审计

Gen Agent Trust Hub

可疑

Socket

可疑

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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