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Qe Iterative Loop

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proffesor-for-testing
qe-iterative-loop

Runs autonomous red-green-refactor loops to fix failing tests, reach coverage targets, and satisfy quality gates. Use when tests need to pass, coverage thresholds must be met, quality gates require compliance, or flaky tests need stabilization.

Overview

Publisherproffesor-for-testing
Repositoryagentic-qe
Skill nameqe-iterative-loop
Stars
480
Forks
92
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by proffesor-for-testing on GitHub. Read the source before you install it.

Installation

Install the Qe Iterative Loop AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/proffesor-for-testing/agentic-qe.git /tmp/agentic-qe
mkdir -p .claude/skills
cp -r /tmp/agentic-qe/assets/skills/qe-iterative-loop .claude/skills/qe-iterative-loop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qe Iterative Loop in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Qe Iterative Loop on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Qe Iterative Loop is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Qe Iterative Loop AI skill do?

Runs autonomous red-green-refactor loops to fix failing tests, reach coverage targets, and satisfy quality gates. Use when tests need to pass, coverage thresholds must be met, quality gates require compliance, or flaky tests need stabilization.

Why use Qe Iterative Loop on TypingMind?

Because you install it once and use it with any model. Qe Iterative Loop is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Qe Iterative Loop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/proffesor-for-testing/agentic-qe/tree/main/assets/skills/qe-iterative-loop. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Qe Iterative Loop?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Qe Iterative Loop?

As many as you like. As long as a model supports skills, you can use Qe Iterative Loop with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Qe Iterative Loop AI skill free?

Yes. It is published on GitHub by proffesor-for-testing under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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