/iterative-loop
Runs continuous AI iteration loops that repeat build-test-fix cycles until success criteria are met. Use when building features requiring test-driven refinement, implementing tasks with clear pass/fail criteria, or automating iterative improvement workflows.
$ npx -y skills add proffesor-for-testing/agentic-qe --skill iterative-loop --agent claude-codeHow it fires
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/iterative-loop
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Runs continuous AI iteration loops that repeat build-test-fix cycles until success criteria are met. Use when building features requiring test-driven refinement, implementing tasks with clear pass/fail criteria, or automating iterative improvement workflows.
SKILL.md
iterative-loop.SKILL.mdname: "iterative-loop"
description: "Runs continuous AI iteration loops that repeat build-test-fix cycles until success criteria are met. Use when building features requiring test-driven refinement, implementing tasks with clear pass/fail criteria, or automating iterative improvement workflows."
Iterative Loop
Overview
The Iterative Loop skill implements **continuous AI-driven development loops** that persist until completion criteria are met. Inspired by the Ralph Wiggum technique, this approach enables autonomous, self-correcting development cycles where the AI sees its previous work in files and git history, iteratively improving until success.
Core Philosophy
1. **Iteration > Perfection** - Don't aim for perfect on first try; let the loop refine the work 2. **Failures Are Data** - Each failure provides information to improve the next attempt 3. **Clear Criteria** - Success must be objectively measurable (tests, metrics, validations) 4. **Persistence Wins** - Keep trying until success; the loop handles retry logic automatically
Prerequisites
- Claude Code with session management
- Clear completion criteria (tests, linting, metrics)
- Version control (git) for tracking iterations
---
Quick Start
Basic Iterative Development Pattern
# Define task with clear completion criteria
TASK="Implement user authentication with JWT.
Success criteria:
- All unit tests pass
- Integration tests pass
- No TypeScript errors
- Security audit passes
Output <promise>COMPLETE</promise> when all criteria met."
# Execute iterative loop (conceptual)
while ! task_complete; do
claude_execute "$TASK"
check_completion_criteria
done
AQE v3 Integration Example
# Using claude-flow hooks for iterative task
npx --no-install ruflo hooks pre-task --description "Implement auth with iteration" --taskId "auth-impl"
# Store iteration state in memory
npx --no-install ruflo memory store \
--key "iteration-auth" \
--value '{"iteration": 1, "maxIterations": 20, "criteria": "all tests pass"}' \
--namespace iterations---
Step-by-Step Guide
Step 1: Define Clear Success Criteria
**Essential**: Every iterative task MUST have objectively measurable completion criteria.
**Good Criteria Examples:**
✅ All unit tests pass (npm test returns exit code 0)
✅ Coverage > 80% (coverage report shows 80%+)
✅ No TypeScript errors (tsc --noEmit returns 0)
✅ Linting passes (eslint returns 0)
✅ Performance < 100ms (benchmark shows < 100ms)
**Bad Criteria Examples:**
❌ "Code looks good" (subjective)
❌ "Works properly" (undefined)
❌ "Well-structured" (no measurable check)
Step 2: Structure the Task with Phases
Break complex tasks into incremental phases:
## Task: Implement User Authentication
### Phase 1: Data Layer
- Create User model with Prisma schema
- Write migration
- Run tests: `npm test -- --grep "User model"`
- Criteria: Model tests pass
### Phase 2: Service Layer
- Implement AuthService with JWT
- Add token generation/validation
- Run tests: `npm test -- --grep "AuthService"`
- Criteria: Service tests pass
### Phase 3: API Layer
- Create /auth/login endpoint
- Create /auth/register endpoint
- Run tests: `npm test -- --grep "auth API"`
- Criteria: API tests pass
### Phase 4: Integration
- End-to-end authentication flow
- Run tests: `npm test`
- Criteria: ALL tests pass
Output <promise>AUTH_COMPLETE</promise> when Phase 4 passes.
Step 3: Implement Safety Mechanisms
Always include escape conditions:
## Safety Rules
1. **Max Iterations**: Stop after 20 attempts
2. **Stuck Detection**: After 5 iterations without progress:
- Document what's blocking
- List attempted approaches
- Suggest alternative strategies
3. **Critical Errors**: Stop immediately if:
- Database corruption detected
- Security vulnerability introduced
- Breaking changes to existing features
Step 4: Execute with Verification
Each iteration should: 1. Make targeted changes 2. Run verification (tests, lint, build) 3. Analyze results 4. Plan next iteration based on feedback
# Iteration pattern
1. Read previous state (files, git log)
2. Identify remaining work
3. Implement specific change
4. Run verification suite
5. If all pass -> output completion promise
6. If failures -> analyze and continue iteration
---
Iterative Patterns
Pattern 1: Test-Driven Iteration
## TDD Iteration Task
1. Write failing test for [feature]
2. Implement minimal code to pass test
3. Run `npm test`
4. If test fails -> debug and fix implementation
5. If test passes -> check if more tests needed
6. Repeat until all acceptance tests pass
7. Refactor if needed
8. Output <promise>TDD_COMPLETE</promise>
Pattern 2: Bug Fix Iteration
## Bug Fix Task
1. Write failing test that reproduces bug
2. Implement fix
3. Run test suite
4. If reproduction test fails -> analyze why fix didn't work
5. If other tests fail -> fix regressions
6. If all tests pass -> output <promise>BUG_FIXED</promise>
Max iterations: 10
After 5 iterations without fix:
- Document root cause analysis
- Suggest alternative approaches
Pattern 3: Coverage Improvement Iteration
## Coverage Improvement Task
Target: 80% line coverage
1. Run coverage analysis
2. Identify uncovered code paths
3. Write test for highest-impact uncovered path
4. Run tests with coverage
5. If coverage >= 80% -> output <promise>COVERAGE_ACHIEVED</promise>
6. If coverage < 80% -> continue iteration
Max iterations: 30
Progress check: If coverage doesn't improve for 3 iterations -> analyze blockers
Pattern 4: Performance Optimization Iteration
## Performance Optimization Task
Target: Response time < 100ms
1. Run performance benchmark
2. Identify slowest operation
3. Implement optimization
4. Run benchmark again
5. If target met -> output <promise>PERF_TARGET_MET</promise>
6. If not
Read more
name: "iterative-loop" description: "Runs continuous AI iteration loops that repeat build-test-fix cycles until success criteria are met. Use when building features requiring test-driven refinement, implementing tasks with clear pass/fail criteria, or automating iterative improvement workflows."
Iterative Loop
Overview
The Iterative Loop skill implements **continuous AI-driven development loops** that persist until completion criteria are met. Inspired by the Ralph Wiggum technique, this approach enables autonomous, self-correcting development cycles where the AI sees its previous work in files and git history, iteratively improving until success.
Core Philosophy
1. **Iteration > Perfection** - Don't aim for perfect on first try; let the loop refine the work 2. **Failures Are Data** - Each failure provides information to improve the next attempt 3. **Clear Criteria** - Success must be objectively measurable (tests, metrics, validations) 4. **Persistence Wins** - Keep trying until success; the loop handles retry logic automatically
Prerequisites
- Claude Code with session management
- Clear completion criteria (tests, linting, metrics)
- Version control (git) for tracking iterations
---
Quick Start
Basic Iterative Development Pattern
# Define task with clear completion criteria TASK="Implement user authentication with JWT. Success criteria: - All unit tests pass - Integration tests pass - No TypeScript errors - Security audit passes Output <promise>COMPLETE</promise> when all criteria met." # Execute iterative loop (conceptual) while ! task_complete; do claude_execute "$TASK" check_completion_criteria done
AQE v3 Integration Example
# Using claude-flow hooks for iterative task
npx --no-install ruflo hooks pre-task --description "Implement auth with iteration" --taskId "auth-impl"
# Store iteration state in memory
npx --no-install ruflo memory store \
--key "iteration-auth" \
--value '{"iteration": 1, "maxIterations": 20, "criteria": "all tests pass"}' \
--namespace iterations---
Step-by-Step Guide
Step 1: Define Clear Success Criteria
**Essential**: Every iterative task MUST have objectively measurable completion criteria.
**Good Criteria Examples:**
✅ All unit tests pass (npm test returns exit code 0) ✅ Coverage > 80% (coverage report shows 80%+) ✅ No TypeScript errors (tsc --noEmit returns 0) ✅ Linting passes (eslint returns 0) ✅ Performance < 100ms (benchmark shows < 100ms)
**Bad Criteria Examples:**
❌ "Code looks good" (subjective) ❌ "Works properly" (undefined) ❌ "Well-structured" (no measurable check)
Step 2: Structure the Task with Phases
Break complex tasks into incremental phases:
## Task: Implement User Authentication ### Phase 1: Data Layer - Create User model with Prisma schema - Write migration - Run tests: `npm test -- --grep "User model"` - Criteria: Model tests pass ### Phase 2: Service Layer - Implement AuthService with JWT - Add token generation/validation - Run tests: `npm test -- --grep "AuthService"` - Criteria: Service tests pass ### Phase 3: API Layer - Create /auth/login endpoint - Create /auth/register endpoint - Run tests: `npm test -- --grep "auth API"` - Criteria: API tests pass ### Phase 4: Integration - End-to-end authentication flow - Run tests: `npm test` - Criteria: ALL tests pass Output <promise>AUTH_COMPLETE</promise> when Phase 4 passes.
Step 3: Implement Safety Mechanisms
Always include escape conditions:
## Safety Rules 1. **Max Iterations**: Stop after 20 attempts 2. **Stuck Detection**: After 5 iterations without progress: - Document what's blocking - List attempted approaches - Suggest alternative strategies 3. **Critical Errors**: Stop immediately if: - Database corruption detected - Security vulnerability introduced - Breaking changes to existing features
Step 4: Execute with Verification
Each iteration should: 1. Make targeted changes 2. Run verification (tests, lint, build) 3. Analyze results 4. Plan next iteration based on feedback
# Iteration pattern 1. Read previous state (files, git log) 2. Identify remaining work 3. Implement specific change 4. Run verification suite 5. If all pass -> output completion promise 6. If failures -> analyze and continue iteration
---
Iterative Patterns
Pattern 1: Test-Driven Iteration
## TDD Iteration Task 1. Write failing test for [feature] 2. Implement minimal code to pass test 3. Run `npm test` 4. If test fails -> debug and fix implementation 5. If test passes -> check if more tests needed 6. Repeat until all acceptance tests pass 7. Refactor if needed 8. Output <promise>TDD_COMPLETE</promise>
Pattern 2: Bug Fix Iteration
## Bug Fix Task 1. Write failing test that reproduces bug 2. Implement fix 3. Run test suite 4. If reproduction test fails -> analyze why fix didn't work 5. If other tests fail -> fix regressions 6. If all tests pass -> output <promise>BUG_FIXED</promise> Max iterations: 10 After 5 iterations without fix: - Document root cause analysis - Suggest alternative approaches
Pattern 3: Coverage Improvement Iteration
## Coverage Improvement Task Target: 80% line coverage 1. Run coverage analysis 2. Identify uncovered code paths 3. Write test for highest-impact uncovered path 4. Run tests with coverage 5. If coverage >= 80% -> output <promise>COVERAGE_ACHIEVED</promise> 6. If coverage < 80% -> continue iteration Max iterations: 30 Progress check: If coverage doesn't improve for 3 iterations -> analyze blockers
Pattern 4: Performance Optimization Iteration
## Performance Optimization Task Target: Response time < 100ms 1. Run performance benchmark 2. Identify slowest operation 3. Implement optimization 4. Run benchmark again 5. If target met -> output <promise>PERF_TARGET_MET</promise> 6. If not
AI-powered quality engineering agents that generate tests, find coverage gaps, detect flaky tests, and learn your codebase patterns — across 11 coding agent platforms.
Repo: proffesor-for-testing/agentic-qe
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