Quality Engineering iteration loops for autonomous test improvement, coverage achievement, and quality gate compliance.

MITAuto-check passedTesting & QA

Install Qe Iterative Loop

skills CLI
$ npx skills add proffesor-for-testing/agentic-qe --skill qe-iterative-loop -a claude-code

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

GitHub CLI
$ gh skill install proffesor-for-testing/agentic-qe qe-iterative-loop --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/proffesor-for-testing/agentic-qe.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.kiro/skills/qe-iterative-loop .claude/skills/qe-iterative-loop && 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
qe-iterative-loop
GitHub stars
494
Token cost
~3.2k tokens
SKILL.md length
402 words
Files
1
Skills in repo
95
Repo updated
First seen
Licence
MIT

At a glance

Quality Engineering iteration loops for autonomous test improvement, coverage achievement, and quality gate compliance.

  • Works in 4 steps: Stop iteration after 5 attempts on same… → Analyze if test expectation is correct → Review if production behavior is as… → …
  • Tests need to pass
  • SKILL.md covers Overview, Why QE Benefits from Iteration, Prerequisites and Quick Start, plus 6 more sections
  • Calls npm and npx

What it does

Qe Iterative Loop is an agent skill from proffesor-for-testing/agentic-qe. Quality Engineering iteration loops for autonomous test improvement, coverage achievement, and quality gate compliance. Use when tests need to pass, coverage targets must be met, quality gates require compliance, or flaky tests need stabilization. Integrates with AQE v3 fleet agents for coordinated quality iteration.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Testing & QA, covering Quality gates and Failing and flaky tests. It works with npm. The repository describes itself as: Agentic QE Fleet is an open-source AI-powered QA/QE platform designed for use with Coding Agents (works best with Claude Code) featuring specialized agents and skills to support… The licence is MIT.

When your agent uses it

  • Tests need to pass
  • Coverage targets must be met
  • Quality gates require compliance
  • Flaky tests need stabilization

Example prompts

  • “/qe-iterative-loop”

Requirements

  • Node.js

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  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

What it can do on your machine

Read from SKILL.md and the folder at commit 829d030. 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

    Shell commands in SKILL.md call:

    • npm
    • npx

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

  • Network

    Links to these hosts (documentation or services it may open):

    • ghuntley.com

    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

Qe Iterative Loop loads about 3.2k tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 402 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k

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 proffesor-for-testing/agentic-qe at commit 829d030, republished under its MIT licence (© proffesor-for-testing). 402 words, ~3,202 tokens.

Download SKILL.mdSave it as .claude/skills/qe-iterative-loop/SKILL.md (or your agent's skills folder).
name
qe-iterative-loop
description
Quality Engineering iteration loops for autonomous test improvement, coverage achievement, and quality gate compliance. Use when tests need to pass, coverage targets must be met, quality gates require compliance, or flaky tests need stabilization. Integrates with AQE v3 fleet agents for coordinated quality iteration.
inclusion
auto

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
bash
# 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
bash
# 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
bash
# 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

markdown
## 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

markdown
## 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
```bash
# 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

```markdown
## 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
```bash
# 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
bash
# 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

```markdown
## 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
```bash
# 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

```markdown
## 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
```bash
# 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

```bash
# 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
bash
# 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
markdown
# 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

markdown
## 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

Show full SKILL.md (178 more words)Show less
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

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

© proffesor-for-testing, 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 .kiro/skills/qe-iterative-loop of proffesor-for-testing/agentic-qe.

Open the folder on GitHubat commit 829d030

Compare with similar skills

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Works with

Categories

Questions about Qe Iterative Loop

What does Qe Iterative Loop do?

Quality Engineering iteration loops for autonomous test improvement, coverage achievement, and quality gate compliance. Qe Iterative Loop is an agent skill from proffesor-for-testing/agentic-qe. Quality Engineering iteration loops for autonomous test improvement, coverage achievement, and quality gate compliance.

When should I use Qe Iterative Loop?

Qe Iterative Loop fits situations like: tests need to pass; coverage targets must be met; quality gates require compliance; flaky tests need stabilization.

How do I install Qe Iterative Loop in Claude Code?

Run `npx skills add proffesor-for-testing/agentic-qe --skill qe-iterative-loop -a claude-code`. Or copy the skill folder (.kiro/skills/qe-iterative-loop in proffesor-for-testing/agentic-qe) into .claude/skills/qe-iterative-loop in your project. Claude Code loads it when a task matches its description.

How do I install Qe Iterative Loop in Codex?

Run `npx skills add proffesor-for-testing/agentic-qe --skill qe-iterative-loop -a codex`. Or copy the skill folder (.kiro/skills/qe-iterative-loop in proffesor-for-testing/agentic-qe) into .agents/skills/qe-iterative-loop in your project. Codex loads it when a task matches its description.

Can I use Qe Iterative Loop 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 proffesor-for-testing/agentic-qe --skill qe-iterative-loop -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/qe-iterative-loop, .gemini/skills/qe-iterative-loop, .github/skills/qe-iterative-loop and .opencode/skills/qe-iterative-loop in your project.

What does Qe Iterative Loop need to run?

Going by SKILL.md and its folder, Qe Iterative Loop needs the command-line tools its instructions call (npm and npx). Our summary lists: Node.js.

Does Qe Iterative Loop access the network?

SKILL.md names 1 domain. As links in the text: ghuntley.com. This is read from the text; nothing was executed.

Is Qe Iterative Loop 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 Qe Iterative Loop use?

Qe Iterative Loop 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 Qe Iterative Loop use?

About 3.2k tokens (SKILL.md is roughly 13k 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 Qe Iterative Loop?

Skills that share tags, products or a category with Qe Iterative Loop: Parse Error Logs (maslennikov-ig/claude-code-orchestrator-kit, 260 stars), Quality Scan (diegosouzapw/OmniRoute, 74k stars), Testing (trieb-work/nextjs-turbo-redis-cache, 151 stars) and Run Unit Tests (Azure/cosmos-explorer, 131 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Qe Iterative Loop?

proffesor-for-testing (a GitHub user) maintains it in proffesor-for-testing/agentic-qe, which has 494 GitHub stars. The repository holds 95 skills in this directory. The repository was last updated on October 4, 2026.

Source: proffesor-for-testing/agentic-qe on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.