Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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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.
# 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
# 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
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)
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.
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
Each iteration should:
# 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
## 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>
## 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
## 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
## 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 improved -> try different approach
Max iterations: 15
Record metrics each iteration for trend analysis
# Store iteration state
npx --no-install ruflo memory store \
--key "current-iteration" \
--value '{"task": "auth", "iteration": 5, "lastResult": "2 tests failing"}' \
--namespace iterations
# Search for similar past iterations
npx --no-install ruflo memory search \
--query "auth implementation" \
--namespace iterations
# Learn from successful completions
npx --no-install ruflo hooks post-task \
--taskId "auth-impl" \
--success true \
--quality 0.9
For complex tasks, use multiple agents iterating in parallel:
# Initialize swarm for parallel iteration
npx --no-install ruflo swarm init --topology mesh --max-agents 5
# Spawn specialized iterators
Task("Iterate on unit tests", "Fix failing unit tests until all pass", "tester")
Task("Iterate on integration", "Fix integration tests until all pass", "tester")
Task("Iterate on performance", "Optimize until benchmarks pass", "performance-engineer")
Include:
Example Well-Structured Prompt:
## Task: Implement Feature X
### Success Criteria (ALL must pass):
1. `npm test` exits with code 0
2. `npm run lint` exits with code 0
3. `npm run typecheck` exits with code 0
4. No console.log statements in production code
### Phases:
1. Write failing tests
2. Implement feature
3. Fix any failures
4. Clean up and refactor
### Safety:
- Max iterations: 20
- After 10 iterations: summarize blockers
- Stop if security issues detected
### Completion:
When ALL success criteria pass, output:
<promise>FEATURE_X_COMPLETE</promise>
Ideal for:
Not ideal for:
Symptoms: Same errors repeat without improvement
Solutions:
Symptoms: Loop ends but task not actually complete
Solutions:
Symptoms: Previously passing tests fail after new changes
Solutions:
Origin: Based on Ralph Wiggum plugin from claude-code repository (anthropics/claude-code) Adapted for: Agentic QE v3 with Claude Flow integration
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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
日本語の概要は準備中です。原文の説明を表示しています。
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
日本語の概要は準備中です。原文の説明を表示しています。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
日本語の概要は準備中です。原文の説明を表示しています。