orchestrator
Claude as intelligent orchestrator for multi-agent workflows. Coordinates specialized agents (Gemini, local models, tools) using Shell-As-Bus pattern with capability-based delegation.
When & Why to Use This Skill
This Claude skill acts as an intelligent orchestrator for multi-agent workflows, implementing a 'Shell-As-Bus' architecture to coordinate specialized agents like Gemini and local models. It streamlines complex task execution through capability-based delegation, parallel processing, and robust error recovery, allowing users to scale AI operations beyond the limits of a single model while optimizing for cost, speed, and context windows.
Use Cases
- Multi-Agent Software Engineering: Coordinate Claude for high-level architecture design while delegating security audits to Gemini and boilerplate code generation to local models.
- Large-Scale Document Analysis: Process massive datasets exceeding 200k tokens by intelligently routing context-heavy tasks to Gemini-specialist agents.
- Privacy-Conscious Workflows: Route sensitive or private data processing to local models via CLI while utilizing cloud-based LLMs for non-sensitive reasoning and final synthesis.
- High-Efficiency Research Pipelines: Execute multiple search and data gathering tasks simultaneously using staggered parallel execution to bypass API bottlenecks and reduce total runtime.
- Automated Error Recovery: Implement a 'Try-Catch-Escalate' loop where specialized agents handle bulk tasks, and Claude intervenes only when errors or inconsistencies are detected.
| name | orchestrator |
|---|---|
| description | Claude as intelligent orchestrator for multi-agent workflows. Coordinates specialized agents (Gemini, local models, tools) using Shell-As-Bus pattern with capability-based delegation. |
| version | 1.1.0 |
Orchestrator - Multi-Agent Coordination
Claude orchestrates specialized agents to solve complex tasks efficiently.
Core Principle
"Shell-As-Bus": Every agent is a CLI tool. Bash is the universal communication bus. Files and stdout for data exchange.
Workflow
Analyze request (Planning Mode):
- Break down into sub-tasks
- Identify required capabilities
- Match tasks to agents via Decision Matrix
- Check quota constraints (PROVIDER-LIMITS.md)
Create execution plan:
- Sequential for dependent tasks
- Parallel for independent tasks
- Hybrid for complex workflows
Delegate intelligently:
Decision Matrix: High Creativity/Reasoning → Execute directly (Claude) Massive Context (>200k tokens) → gemini-specialist Bulk/Repetitive Processing → local-model Real-time Information → gemini-specialist (Search grounding) Sensitive/Private Data → local-modelMonitor execution:
- Check agent outputs (stdout, exit codes)
- Validate against expectations
- Intervene on errors (fallback to Claude)
Synthesize results:
- Combine agent outputs
- Validate consistency
- Present unified result
Handle failures (Try-Catch-Escalate):
- Try: Delegate to specialist
- Catch: Agent fails or produces bad output
- Escalate: Claude fixes directly
Agent Interface Standard
All delegated agents must:
- Be callable via CLI
- Return clean stdout (not debug logs)
- Use exit codes (0=success, non-zero=error)
- Save artifacts to known paths
Good output:
Task completed. Summary: [brief]. Artifacts: [path].
Bad output:
[DEBUG] Loading...
[INFO] Processing...
[WARNING] ...
Delegation Patterns
Pattern 1: Staggered Parallel Execution (Independent Tasks)
# CRITICAL: Use staggered launch (1s delay) to avoid Gemini CLI race condition
# Workers run simultaneously AFTER startup - 4-8x faster than sequential!
TASKS=("task1" "task2" "task3" "task4")
for i in "${!TASKS[@]}"; do
gemini -p "${TASKS[$i]}" --yolo -o text > /tmp/result$i.txt &
sleep 1 # ← CRITICAL: Prevents concurrent startup bug (Issue #3161)
done
wait # Wait for all to complete
# Synthesize results
for f in /tmp/result*.txt; do
cat "$f"
done
Why staggered? Gemini CLI v0.21.1 has race condition during startup. 500ms-1s delay fixes it. Workers still run in parallel after startup!
Pattern 2: Sequential Pipeline (Dependent Tasks)
# Output of one feeds the next
gemini -p "analyze code" < code.py --yolo -o text > analysis.md
# Claude reads analysis
cat analysis.md
# Claude implements based on analysis
Pattern 3: Evaluator-Optimizer (Review Loop)
# Claude generates code
cat > draft.py << EOF
...code...
EOF
# Gemini reviews
gemini -p "security audit this code" < draft.py --yolo -o text > review.md
# Claude refines based on review
cat review.md
# Apply improvements
When to Orchestrate
✅ Use orchestration when:
- Task requires multiple distinct capabilities
- Massive context exceeds Claude's window
- Parallel execution saves time
- Need specialized tools/search
- Cross-validation desired
❌ Don't orchestrate when:
- Task is simple (single-step)
- Only Claude's capabilities needed
- Overhead exceeds benefit
Key Principle
Delegation > Monopolization. Use specialized agents for their strengths. Claude orchestrates and synthesizes, not does everything.
See PATTERNS.md for detailed workflows, AGENTS.md for available agent catalog, and PROVIDER-LIMITS.md for quota constraints.