aiskillstore Cascade Orchestrator (Enhanced)
Overview
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.
Philosophy: Composable Excellence
Complex capabilities emerge from composing simple, well-defined components.
Enhanced Capabilities:
- Codex Sandbox Iteration: Auto-fix failures in isolated environment (from audit-pipeline)
- Multi-Model Routing: Use Gemini/Codex based on stage requirements
- Swarm Coordination: Parallel execution via ruv-swarm MCP
- Memory Persistence: Maintain context across stages
- GitHub Integration: CI/CD pipeline automation
Key Principles:
- Separation of concerns (micro-skills execute, cascades coordinate)
- Reusability through composition
- Flexible orchestration patterns
- Declarative workflow definition
- Intelligent model selection
Cascade Architecture (Enhanced)
Definition Layer
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
Execution Engine (Enhanced)
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
Enhanced Cascade Patterns
Pattern 1: Linear Pipeline with Multi-Model
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
Pattern 2: Parallel Fan-Out with Swarm
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
Pattern 3: Codex Sandbox Iteration
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
Pattern 4: Conditional with Model Switching
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
Pattern 5: Iterative with Memory
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
Creating Enhanced Cascades
Step 1: Define with AI Considerations
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
Step 2: Design with Swarm Parallelism
When to Use Swarm:
- Multiple independent tasks
- Resource-intensive operations
- Need load balancing
- Want fault tolerance
Swarm Configuration:
swarm_config:
topology: mesh | hierarchical | star
max_agents: number
strategy: balanced | specialized | adaptive
memory_shared: true | false
Step 3: Add Codex Iteration for Quality
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
Step 4: Enable Memory Persistence
Shared Memory Across Stages:
memory:
persistence: enabled
scope: cascade | global
storage: mcp__ruv-swarm__memory
keys:
- analysis_results
- intermediate_outputs
- learned_patterns
Enhanced Cascade Definition Format
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
Real-World Enhanced Cascades
Example 1: Complete Development Workflow
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"
Example 2: Legacy Modernization
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"
Example 3: Bug Fix with RCA
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"
Integration Points
With Micro-Skills
- Executes micro-skills in stages
- Passes data between skills
- Handles skill errors gracefully
With Multi-Model System
- Routes stages to optimal AI
- Uses gemini-* skills for unique capabilities
- Uses codex-* skills for prototyping/fixing
- Uses Claude for best reasoning
With Audit Pipeline
- Incorporates theater → functionality → style pattern
- Uses Codex sandbox iteration from Phase 2
- Applies quality gates throughout
With Slash Commands
- Commands trigger cascades
- Parameter mapping from command to cascade
- Progress reporting to command interface
With Ruv-Swarm MCP
- Parallel stage coordination
- Memory persistence
- Neural training
- Performance tracking
Working with Enhanced Cascade Orchestrator
Invocation: "Create a cascade that [end goal] using [micro-skills] with [Codex/Gemini/swarm] capabilities"
The orchestrator will:
- Design workflow with optimal AI model selection
- Configure Codex sandbox for testing stages
- Set up swarm coordination for parallel stages
- Enable memory persistence across stages
- Integrate with GitHub for CI/CD
- Generate executable cascade definition
Advanced Features:
- Automatic model routing based on task
- Codex iteration loop for auto-fixing
- Swarm coordination for parallelism
- Memory sharing across stages
- GitHub PR/issue integration
- Performance monitoring and optimization
Version 2.0 Enhancements:
- Codex sandbox iteration pattern
- Multi-model intelligent routing
- Ruv-swarm MCP integration
- Memory persistence
- GitHub workflow automation
- Enhanced error recovery