// Creates sophisticated workflow cascades coordinating multiple micro-skills with sequential pipelines, parallel execution, conditional branching, and Codex sandbox iteration. Enhanced with multi-model routing (Gemini/Codex), ruv-swarm coordination, memory persistence, and audit-pipeline patterns for production workflows.
| name | cascade-orchestrator |
| description | Creates sophisticated workflow cascades coordinating multiple micro-skills with sequential pipelines, parallel execution, conditional branching, and Codex sandbox iteration. Enhanced with multi-model routing (Gemini/Codex), ruv-swarm coordination, memory persistence, and audit-pipeline patterns for production workflows. |
| tags | ["orchestration","workflows","cascades","multi-model","codex-integration","tier-2"] |
| version | 2.0.0 |
Manages workflows (cascades) that coordinate multiple micro-skills into cohesive processes. This enhanced version integrates Codex sandbox iteration, multi-model routing, ruv-swarm coordination, and memory persistence across stages.
Complex capabilities emerge from composing simple, well-defined components.
Enhanced Capabilities:
Key Principles:
Extended Stage Types:
stages:
- type: sequential # One after another
- type: parallel # Simultaneous execution
- type: conditional # Based on runtime conditions
- type: codex-sandbox # NEW: Iterative testing with auto-fix
- type: multi-model # NEW: Intelligent AI routing
- type: swarm-parallel # NEW: Coordinated via ruv-swarm
Enhanced Data Flow:
data_flow:
- stage_output: previous stage results
- shared_memory: persistent across stages
- multi_model_context: AI-specific formatting
- codex_sandbox_state: isolated test environment
Advanced Error Handling:
error_handling:
- retry_with_backoff
- fallback_to_alternative
- codex_auto_fix # NEW: Auto-fix via Codex
- model_switching # NEW: Try different AI
- swarm_recovery # NEW: Redistribute tasks
Stage Scheduling with AI Selection:
for stage in cascade.stages:
if stage.type == "codex-sandbox":
execute_with_codex_iteration(stage)
elif stage.type == "multi-model":
model = select_optimal_model(stage.task)
execute_on_model(stage, model)
elif stage.type == "swarm-parallel":
execute_via_ruv_swarm(stage)
else:
execute_standard(stage)
Codex Sandbox Iteration Loop:
def execute_with_codex_iteration(stage):
"""
From audit-pipeline Phase 2: functionality-audit pattern
"""
results = execute_tests(stage.tests)
for test in failed_tests(results):
iteration = 0
max_iterations = 5
while test.failed and iteration < max_iterations:
# Spawn Codex in sandbox
fix = spawn_codex_auto(
task=f"Fix test failure: {test.error}",
sandbox=True,
context=test.context
)
# Re-test
test.result = rerun_test(test)
iteration += 1
if test.passed:
apply_fix_to_main(fix)
break
if still_failed(test):
escalate_to_user(test)
return aggregate_results(results)
Multi-Model Routing:
def select_optimal_model(task):
"""
Route to best AI based on task characteristics
"""
if task.requires_large_context:
return "gemini-megacontext" # 1M tokens
elif task.needs_current_info:
return "gemini-search" # Web grounding
elif task.needs_visual_output:
return "gemini-media" # Imagen/Veo
elif task.needs_rapid_prototype:
return "codex-auto" # Full Auto
elif task.needs_alternative_view:
return "codex-reasoning" # GPT-5-Codex
else:
return "claude" # Best overall
cascade:
name: enhanced-data-pipeline
stages:
- stage: extract
model: auto-select
skill: extract-data
- stage: validate
model: auto-select
skill: validate-data
error_handling:
strategy: codex-auto-fix # NEW
- stage: transform
model: codex-auto # Fast prototyping
skill: transform-data
- stage: report
model: gemini-media # Generate visuals
skill: generate-report
cascade:
name: code-quality-swarm
stages:
- stage: quality-checks
type: swarm-parallel # NEW: Via ruv-swarm
skills:
- lint-code
- security-scan
- complexity-analysis
- test-coverage
swarm_config:
topology: mesh
max_agents: 4
strategy: balanced
- stage: aggregate
skill: merge-quality-reports
cascade:
name: test-and-fix
stages:
- stage: functionality-audit
type: codex-sandbox # NEW
test_suite: comprehensive
codex_config:
mode: full-auto
max_iterations: 5
sandbox: true
error_recovery:
auto_fix: true
escalate_after: 5
- stage: validate-fixes
skill: regression-tests
cascade:
name: adaptive-workflow
stages:
- stage: analyze
model: gemini-megacontext # Large context
skill: analyze-codebase
- stage: decide
type: conditional
condition: ${analyze.quality_score}
branches:
high_quality:
model: codex-auto # Fast path
skill: deploy-fast
low_quality:
model: multi-model # Comprehensive path
cascade: deep-quality-audit
cascade:
name: iterative-refinement
stages:
- stage: refactor
model: auto-select
skill: refactor-code
memory: persistent # NEW
- stage: check-quality
skill: quality-metrics
- stage: repeat-decision
type: conditional
condition: ${quality < threshold}
repeat: refactor # Loop back
max_iterations: 3
memory_shared: true # Context persists
Identify Model Requirements:
For each stage, determine:
- Large context needed? → Gemini
- Current web info needed? → Gemini Search
- Visual output needed? → Gemini Media
- Rapid prototyping needed? → Codex
- Testing with auto-fix? → Codex Sandbox
- Best overall reasoning? → Claude
When to Use Swarm:
Swarm Configuration:
swarm_config:
topology: mesh | hierarchical | star
max_agents: number
strategy: balanced | specialized | adaptive
memory_shared: true | false
Pattern from audit-pipeline:
stages:
- type: codex-sandbox
tests: ${test_suite}
fix_strategy:
auto_fix: true
max_iterations: 5
sandbox_isolated: true
network_disabled: true
regression_check: true
Shared Memory Across Stages:
memory:
persistence: enabled
scope: cascade | global
storage: mcp__ruv-swarm__memory
keys:
- analysis_results
- intermediate_outputs
- learned_patterns
cascade:
name: cascade-name
description: What this accomplishes
version: 2.0.0
config:
multi_model: enabled
swarm_coordination: enabled
memory_persistence: enabled
github_integration: enabled
inputs:
- name: input-name
type: type
description: description
stages:
- stage_id: stage-1
name: Stage Name
type: sequential | parallel | codex-sandbox | multi-model | swarm-parallel
model: auto-select | gemini | codex | claude
# For micro-skill execution
skills:
- skill: micro-skill-name
inputs: {...}
outputs: {...}
# For Codex sandbox
codex_config:
mode: full-auto
sandbox: true
max_iterations: 5
# For swarm execution
swarm_config:
topology: mesh
max_agents: 4
# For memory
memory:
read_keys: [...]
write_keys: [...]
error_handling:
strategy: retry | codex-auto-fix | model-switch | swarm-recovery
max_retries: 3
fallback: alternative-skill
memory:
persistence: enabled
scope: cascade
github_integration:
create_pr: on_success
report_issues: on_failure
cascade: complete-dev-workflow
stages:
1. gemini-search: "Research latest framework best practices"
2. codex-auto: "Rapid prototype with best practices"
3. codex-sandbox: "Test everything, auto-fix failures"
4. gemini-media: "Generate architecture diagrams"
5. style-audit: "Polish code to production standards"
6. github-pr: "Create PR with comprehensive report"
cascade: modernize-legacy-code
stages:
1. gemini-megacontext: "Analyze entire 50K line codebase"
2. theater-detection: "Find all mocks and placeholders"
3. [swarm-parallel]:
- codex-auto: "Complete implementations" (parallel)
- gemini-media: "Document architecture"
4. codex-sandbox: "Test all changes with auto-fix"
5. style-audit: "Final polish"
6. generate-pr: "Create PR with before/after comparison"
cascade: intelligent-bug-fix
stages:
1. root-cause-analyzer: "Deep RCA analysis"
2. multi-model-decision:
condition: ${rca.complexity}
simple: codex-auto (quick fix)
complex: [
gemini-megacontext (understand broader context),
codex-reasoning (alternative approaches),
claude (implement best approach)
]
3. codex-sandbox: "Test fix thoroughly"
4. regression-suite: "Ensure no breakage"
5. github-issue-update: "Document fix with RCA report"
Invocation: "Create a cascade that [end goal] using [micro-skills] with [Codex/Gemini/swarm] capabilities"
The orchestrator will:
Advanced Features:
Version 2.0 Enhancements:
Creates specialized AI agents with optimized system prompts using the official 4-phase SOP methodology from Desktop .claude-flow, combined with evidence-based prompting techniques and Claude Agent SDK implementation. Use this skill when creating production-ready agents for specific domains, workflows, or tasks requiring consistent high-quality performance with deeply embedded domain knowledge.
Loop 3 of the Three-Loop Integrated Development System. CI/CD automation with intelligent failure recovery, root cause analysis, and comprehensive quality validation. Receives implementation from Loop 2, feeds failure patterns back to Loop 1. Achieves 100% test success through automated repair and theater validation. v2.0.0 with explicit agent SOPs.
Comprehensive PR review using multi-agent swarm with specialized reviewers for security, performance, style, tests, and documentation. Provides detailed feedback with auto-fix suggestions and merge readiness assessment.
Comprehensive dependency mapping, analysis, and visualization tool for software projects
Automated comprehensive code documentation generation with API docs, README files, inline comments, and architecture diagrams
Complete feature development lifecycle from research to deployment. Uses Gemini Search for best practices, architecture design, Codex prototyping, comprehensive testing, and documentation generation. Full 12-stage workflow.