Token导航 LogoToken导航

Agent Skill · 研究检索

qe-iterative-loop(qe 迭代循环)

qe-iterative-loop 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。

qe-iterative-loop 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,权限为执行命令;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 42,402。

总安装 1,077周安装 44GitHub Stars 329下载 345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
MIT

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:qe-iterative-loop(qe 迭代循环)
来源仓库:https://github.com/proffesor-for-testing/agentic-qe
仓库路径:skills/qe-iterative-loop
安装命令:
npx skills add https://github.com/proffesor-for-testing/agentic-qe --skill qe-iterative-loop
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/proffesor-for-testing/agentic-qe --skill qe-iterative-loop

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 中快速定位候选结果。支持基于关键词、任务场景或来源线索进行信息检索与筛选。可通过 npx skills add 命令从 GitHub 安装使用。建议确认权限范围和维护状态,避免触发联网或文件读写操作。

qe-iterative-loop 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/qe-iterative-loop

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/proffesor-for-testing/agentic-qe --skill qe-it…;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

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

qe-iterative-loop 怎么安装?

在终端执行 npx skills add https://github.com/proffesor-for-testing/agentic-qe --skill qe-iterative-lo…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

qe-iterative-loop 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。

qe-iterative-loop 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/proffesor-for-testing/agentic-qe --skill qe-iterative-loop;npx skills add proffesor-for-testing/agentic-qe --skill "qe-iterative-loop" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

qe-iterative-loop 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

qe-iterative-loop 用的人多吗?

公开数据是累计安装 1,077 次,近一周安装 44 次,下载 345 次,GitHub 329 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。

qe-iterative-loop 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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
qe-iterative-loop

qe 迭代循环 · 本页

1,077329执行命令MIT通过

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

QE Iterative Loop

Overview

QE Iterative Loop is a specialized adaptation of the Ralph Wiggum technique for Quality Engineering workflows. It enables autonomous, self-correcting quality cycles where AI agents iterate until quality objectives are achieved - tests pass, coverage targets met, quality gates satisfied, or flaky tests stabilized.

Why QE Benefits from Iteration

Quality Engineering has objective, measurable success criteria:

  • Tests either pass or fail (exit code 0 vs non-zero)
  • Coverage is quantifiable (78.5% vs 80% target)
  • Quality gates have binary outcomes (pass/fail)
  • Contract validation has clear schemas

This makes QE ideal for iterative loops - we know exactly when we're done.

Prerequisites

  • AQE v3 fleet initialized
  • Test framework configured (Jest, Vitest, Pytest, etc.)
  • Coverage tooling (c8, istanbul, coverage.py)
  • Quality gate definitions

Quick Start

Pattern 1: Test Fix Iteration

# Task: Fix all failing tests
/qe-loop "Run npm test and fix all failing tests.
Success: npm test exits with code 0
Output <promise>TESTS_GREEN</promise> when all tests pass."

Pattern 2: Coverage Target Iteration

# Task: Achieve 80% coverage
/qe-loop "Increase test coverage to 80%.
Success: Coverage report shows >= 80%
Output <promise>COVERAGE_MET</promise> when target achieved."

Pattern 3: Quality Gate Iteration

# Task: Pass all quality gates
/qe-loop "Pass all quality gates for deployment.
Gates:
- Unit tests: pass
- Integration tests: pass
- Coverage: >= 80%
- No critical vulnerabilities
- Performance < 200ms P95
Output <promise>QUALITY_GATES_PASSED</promise> when all pass."

QE Iteration Patterns

Pattern 1: Test-Fix Iteration Loop

Goal: All tests pass

## QE Test-Fix Loop

### Success Criteria
- `npm test` (or test command) returns exit code 0
- No skipped tests (unless explicitly allowed)
- No pending tests

### Iteration Steps
1. Run full test suite
2. Parse output for failures
3. Analyze first failure:
   - Identify failing test file
   - Understand assertion that failed
   - Check if production code or test is wrong
4. Fix the issue
5. Re-run failed test file only (faster feedback)
6. If file passes, run full suite
7. If all pass -> output <promise>TESTS_GREEN</promise>
8. If failures remain -> continue to next failure

### Safety
- Max iterations: 30
- After 10 iterations: report remaining failures
- Stop if same test fails 5 times (possible design issue)

Pattern 2: Coverage Improvement Loop

Goal: Achieve coverage target

## QE Coverage Loop

### Success Criteria
- Line coverage >= {target}%
- Branch coverage >= {target - 5}% (typically lower target)
- No critical paths uncovered

### Iteration Steps
1. Run tests with coverage: `npm test -- --coverage`
2. Parse coverage report
3. If target met -> output <promise>COVERAGE_MET</promise>
4. Identify uncovered files, sorted by:
   - Critical business logic (highest priority)
   - Lines uncovered (most impact)
   - Complexity (McCabe score)
5. Generate test for highest-impact uncovered code
6. Run tests to verify new test passes
7. Check coverage improvement
8. Continue until target met

### Intelligence Integration
- Store successful test patterns in memory
- Learn from coverage achievements
- Predict best coverage strategies

### Commands

Check coverage status (via AQE MCP)

aqe memory get --key "coverage-status" --namespace "coverage"

Store coverage achievement pattern (via AQE MCP)

aqe memory store \ --key "coverage-pattern-auth" \ --value '{"approach": "mock external deps", "improvement": "12%"}' \ --namespace "coverage-patterns"

Pattern 3: Quality Gate Compliance Loop

Goal: Pass all quality gates

## QE Quality Gate Loop

### Gate Definitions
| Gate | Criteria | Priority |
|------|----------|----------|
| unit-tests | All pass | P0 |
| integration-tests | All pass | P0 |
| coverage | >= 80% | P1 |
| lint | No errors | P1 |
| typecheck | No errors | P1 |
| security | No critical/high CVEs | P0 |
| performance | P95 < 200ms | P2 |

### Iteration Strategy
1. Run all gate checks
2. Identify failing gates (sorted by priority)
3. Fix highest-priority failing gate
4. Re-run that gate to verify
5. When gate passes, move to next failing gate
6. When all pass -> output <promise>QUALITY_GATES_PASSED</promise>

### Gate Check Commands

Check all gates

npm test && npm run lint && npm run typecheck && npm run coverage && npm audit

Individual gate checks

npm test # unit-tests npm run test:integration # integration-tests npm run coverage # coverage npm run lint # lint npx tsc --noEmit # typecheck npm audit --audit-level=high # security npm run benchmark # performance


### Integration with AQE v3

Submit quality gate assessment task

aqe quality --runGate true

Task orchestration for gate compliance

aqe task submit --task "Pass all quality gates" --strategy adaptive

Pattern 4: Flaky Test Stabilization Loop

Goal: Eliminate test flakiness

## QE Flaky Test Loop

### Flakiness Detection
1. Run test suite N times (e.g., 5 runs)
2. Identify tests that pass/fail inconsistently
3. Calculate flakiness score: (inconsistent runs / total runs)

### Iteration Steps
1. Run: `for i in {1..5}; do npm test; done`
2. Aggregate results per test
3. Identify flaky tests (passed some, failed some)
4. For each flaky test:
   - Analyze failure modes
   - Common causes:
     - Timing issues (add retries/waits)
     - Shared state (isolate test data)
     - Network calls (mock external services)
     - Random data (use deterministic seeds)
   - Apply appropriate fix
   - Re-run 5 times to verify stability
5. When all tests stable -> output <promise>TESTS_STABLE</promise>

### AQE v3 Flaky Detection

Use qe-flaky-hunter agent

Task("Hunt flaky tests", "Detect and stabilize flaky tests", "qe-flaky-hunter")

Or submit flaky detection task

aqe task submit --type "flaky-detection" --priority "p1"

Pattern 5: Contract Validation Loop

Goal: API contracts aligned

## QE Contract Loop

### Success Criteria
- Provider implements all consumer contracts
- No breaking changes detected
- Schema validation passes

### Iteration Steps
1. Run contract tests: `npm run test:contracts`
2. Parse contract violations
3. For each violation:
   - Determine if provider or consumer needs update
   - Update appropriate side
   - Re-run contract tests
4. When all contracts valid -> output <promise>CONTRACTS_VALID</promise>

### AQE v3 Integration

Validate contracts

aqe test contract --contractPath "./contracts"

Or use specialized agent

Task("Validate API contracts", "Check consumer-provider alignment", "qe-contract-validator")


AQE v3 Fleet Integration

Spawning QE Iteration Agents

# Initialize AQE fleet for QE iteration
aqe fleet init --topology "hierarchical" --maxAgents 8

# Spawn specialized QE iterators using Task tool
Task("Fix failing tests", "Iterate until all tests pass", "qe-tdd-green", {run_in_background: true})
Task("Improve coverage", "Iterate until 80% coverage", "qe-coverage-analyzer", {run_in_background: true})
Task("Fix security issues", "Iterate until security scan passes", "qe-security-scanner", {run_in_background: true})
Task("Stabilize flaky tests", "Iterate until tests stable", "qe-flaky-hunter", {run_in_background: true})

Memory-Enhanced QE Iteration

# Store iteration patterns for learning (via AQE MCP)
aqe memory store \
  --key "qe-iteration-test-fix" \
  --value '{"approach": "mock external deps", "success_rate": 0.85}' \
  --namespace "qe-patterns"

# Search for relevant QE patterns (via AQE MCP)
aqe memory search \
  --pattern "test-fix-*" \
  --namespace "qe-patterns"

# Record successful iteration completion (via AQE task tracking)
aqe task status --taskId "test-fix-iteration"

QE-Specific Agent Routing

QE TaskRecommended AgentIteration Goal
Test fixesqe-tdd-greenAll tests pass
Coverage gapsqe-coverage-analyzerTarget coverage met
Quality gatesqe-quality-gateAll gates pass
Flaky testsqe-flaky-hunterTests stable
Contract validationqe-contract-validatorContracts aligned
Security fixesqe-security-scannerNo vulnerabilities
Performanceqe-performance-validatorBenchmarks pass

Completion Promises for QE

Standard QE Promises

# Test-related
<promise>TESTS_GREEN</promise>       # All tests pass
<promise>TESTS_STABLE</promise>      # Flaky tests fixed
<promise>TDD_COMPLETE</promise>      # TDD cycle done

# Coverage-related
<promise>COVERAGE_MET</promise>      # Target coverage achieved
<promise>GAPS_FILLED</promise>       # Coverage gaps addressed

# Quality gates
<promise>QUALITY_GATES_PASSED</promise>  # All gates pass
<promise>DEPLOYMENT_READY</promise>      # Ready for deploy

# Contract/API
<promise>CONTRACTS_VALID</promise>   # Contracts aligned
<promise>API_COMPLIANT</promise>     # API matches spec

# Security
<promise>SECURITY_CLEARED</promise>  # No vulnerabilities
<promise>COMPLIANCE_MET</promise>    # Compliance requirements met

# Performance
<promise>PERF_TARGET_MET</promise>   # Benchmarks satisfied

Example: Full QE Iteration Workflow

## Complete QE Iteration Task

### Objective
Achieve deployment readiness through iterative quality improvement

### Phase 1: Test Health (Priority)
1. Run `npm test`
2. Fix failing tests iteratively
3. Success: <promise>TESTS_GREEN</promise>

### Phase 2: Coverage (After Phase 1)
1. Run `npm test -- --coverage`
2. Write tests for uncovered critical paths
3. Success: Coverage >= 80% -> <promise>COVERAGE_MET</promise>

### Phase 3: Quality Gates (After Phase 2)
1. Run lint: `npm run lint`
2. Run typecheck: `npx tsc --noEmit`
3. Fix any violations
4. Success: <promise>LINT_PASS</promise> + <promise>TYPES_PASS</promise>

### Phase 4: Security (Parallel with Phase 3)
1. Run `npm audit`
2. Fix critical/high vulnerabilities
3. Success: <promise>SECURITY_CLEARED</promise>

### Phase 5: Integration
1. Run `npm run test:integration`
2. Fix any integration failures
3. Success: <promise>INTEGRATION_PASS</promise>

### Final Gate
When ALL phases complete -> <promise>DEPLOYMENT_READY</promise>

### Safety Limits
- Max iterations per phase: 15
- Total max iterations: 50
- Stuck detection: 5 iterations without progress triggers escalation

Troubleshooting

Issue: Tests Keep Failing Same Assertion

Cause: Likely a design issue, not implementation bug

Solution:

  1. Stop iteration after 5 attempts on same test
  2. Analyze if test expectation is correct
  3. Review if production behavior is as designed
  4. Escalate to human review if unclear

Issue: Coverage Plateau

Cause: Remaining uncovered code is complex/conditional

Solution:

  1. Identify uncovered branches (not just lines)
  2. Generate edge case tests
  3. Consider if uncovered code is dead code
  4. Accept lower target for genuinely untestable code

Issue: Flaky Tests Won't Stabilize

Cause: Deep timing or state issues

Solution:

  1. Add explicit waits/retries
  2. Mock time-dependent behavior
  3. Isolate test environment
  4. Consider marking as skip with explanation

Related Skills

Resources


Origin: Adapted from Ralph Wiggum plugin (anthropics/claude-code) Specialized for: Agentic QE v3 Fleet with 60 QE agents Domains: test-generation, test-execution, coverage-analysis, quality-assessment

平台分布

Claude Code

31.58%
按下载量换算109

windsurf

22.6%
按下载量换算78

trae

18.17%
按下载量换算63

OpenCode

12.92%
按下载量换算45

Codex

8.72%
按下载量换算30

Antigravity

3.74%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/proffesor-for-testing/agentic-qe --skill qe-iterative-loop;npx skills add proffesor-for-testing/agentic-qe --skill "qe-iterative-loop" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。

继续浏览同类 Skills