@oaustegard/orchestrating-agents

---
name: orchestrating-agents
description: Orchestrates parallel API instances, delegated sub-tasks, and multi-agent workflows with streaming and tool-enabled delegation patterns. Use for parallel analysis, multi-perspective reviews, or complex task decomposition.
metadata:
  version: 0.3.0
---

# Orchestrating Agents

This skill enables programmatic API invocations for advanced workflows including parallel processing, task delegation, and multi-agent analysis using the Anthropic API.

## When to Use This Skill

**Primary use cases:**
- **Parallel sub-tasks**: Break complex analysis into simultaneous independent streams
- **Multi-perspective analysis**: Get 3-5 different expert viewpoints concurrently
- **Delegation**: Offload specific subtasks to specialized API instances
- **Recursive workflows**: Orchestrator coordinating multiple API instances
- **High-volume processing**: Batch process multiple items concurrently

**Trigger patterns:**
- "Parallel analysis", "multi-perspective review", "concurrent processing"
- "Delegate subtasks", "coordinate multiple agents"
- "Run analyses from different perspectives"
- "Get expert opinions from multiple angles"

## Quick Start

### Single Invocation

```python
import sys
sys.path.append('/home/user/claude-skills/orchestrating-agents/scripts')
from claude_client import invoke_claude

response = invoke_claude(
    prompt="Analyze this code for security vulnerabilities: ...",
    model="claude-sonnet-4-6"
)
print(response)
```

### Parallel Multi-Perspective Analysis

```python
from claude_client import invoke_parallel

prompts = [
    {
        "prompt": "Analyze from security perspective: ...",
        "system": "You are a security expert"
    },
    {
        "prompt": "Analyze from performance perspective: ...",
        "system": "You are a performance optimization expert"
    },
    {
        "prompt": "Analyze from maintainability perspective: ...",
        "system": "You are a software architecture expert"
    }
]

results = invoke_parallel(prompts, model="claude-sonnet-4-6")

for i, result in enumerate(results):
    print(f"\n=== Perspective {i+1} ===")
    print(result)
```

### Parallel with Shared Cached Context (Recommended)

For parallel operations with shared base context, use caching to reduce costs by up to 90%:

```python
from claude_client import invoke_parallel

# Large context shared across all sub-agents (e.g., codebase, documentation)
base_context = """
<codebase>
...large codebase or documentation (1000+ tokens)...
</codebase>
"""

prompts = [
    {"prompt": "Find security vulnerabilities in the authentication module"},
    {"prompt": "Identify performance bottlenecks in the API layer"},
    {"prompt": "Suggest refactoring opportunities in the database layer"}
]

# First sub-agent creates cache, subsequent ones reuse it
results = invoke_parallel(
    prompts,
    shared_system=base_context,
    cache_shared_system=True  # 90% cost reduction for cached content
)
```

### Multi-Turn Conversation with Auto-Caching

For sub-agents that need multiple rounds of conversation:

```python
from claude_client import ConversationThread

# Create a conversation thread (auto-caches history)
agent = ConversationThread(
    system="You are a code refactoring expert with access to the codebase",
    cache_system=True
)

# Turn 1: Initial analysis
response1 = agent.send("Analyze the UserAuth class for issues")
print(response1)

# Turn 2: Follow-up (reuses cached system + turn 1)
response2 = agent.send("How would you refactor the login method?")
print(response2)

# Turn 3: Implementation (reuses all previous context)
response3 = agent.send("Show me the refactored code")
print(response3)
```

### Streaming Responses

For real-time feedback from sub-agents:

```python
from claude_client import invoke_claude_streaming

def show_progress(chunk):
    print(chunk, end='', flush=True)

response = invoke_claude_streaming(
    "Write a comprehensive security analysis...",
    callback=show_progress
)
```

### Parallel Streaming

Monitor multiple sub-agents simultaneously:

```python
from claude_client import invoke_parallel_streaming

def agent1_callback(chunk):
    print(f"[Security] {chunk}", end='', flush=True)

def agent2_callback(chunk):
    print(f"[Performance] {chunk}", end='', flush=True)

results = invoke_parallel_streaming(
    [
        {"prompt": "Security review: ..."},
        {"prompt": "Performance review: ..."}
    ],
    callbacks=[agent1_callback, agent2_callback]
)
```

### Interruptible Operations

Cancel long-running parallel operations:

```python
from claude_client import invoke_parallel_interruptible, InterruptToken
import threading
import time

token = InterruptToken()

# Run in background
def run_analysis():
    results = invoke_parallel_interruptible(
        prompts=[...],
        interrupt_token=token
    )
    return results

thread = threading.Thread(target=run_analysis)
thread.start()

# Interrupt after 5 seconds
time.sleep(5)
token.interrupt()
```

## Core Functions

### `invoke_claude()`

Single synchronous invocation with full control:

```python
invoke_claude(
    prompt: str | list[dict],
    model: str = "claude-sonnet-4-6",
    system: str | list[dict] | None = None,
    max_tokens: int = 4096,
    temperature: float = 1.0,
    streaming: bool = False,
    cache_system: bool = False,
    cache_prompt: bool = False,
    messages: list[dict] | None = None,
    **kwargs
) -> str
```

**Parameters:**
- `prompt`: The user message (string or list of content blocks)
- `model`: Claude model to use (default: claude-sonnet-4-6)
- `system`: Optional system prompt (string or list of content blocks)
- `max_tokens`: Maximum tokens in response (default: 4096)
- `temperature`: Randomness 0-1 (default: 1.0)
- `streaming`: Enable streaming response (default: False)
- `cache_system`: Add cache_control to system prompt (requires 1024+ tokens, default: False)
- `cache_prompt`: Add cache_control to user prompt (requires 1024+ tokens, default: False)
- `messages`: Pre-built messages list for multi-turn (overrides prompt)
- `**kwargs`: Additional API parameters (top_p, top_k, etc.)

**Returns:** Response text as string

**Note:** Caching requires minimum 1,024 tokens per cache breakpoint. Cache lifetime is 5 minutes (refreshed on use).

### `invoke_parallel()`

Concurrent invocations using lightweight workflow pattern:

```python
invoke_parallel(
    prompts: list[dict],
    model: str = "claude-sonnet-4-6",
    max_tokens: int = 4096,
    max_workers: int = 5,
    shared_system: str | list[dict] | None = None,
    cache_shared_system: bool = False
) -> list[str]
```

**Parameters:**
- `prompts`: List of dicts with 'prompt' (required) and optional 'system', 'temperature', 'cache_system', 'cache_prompt', etc.
- `model`: Claude model for all invocations
- `max_tokens`: Max tokens per response
- `max_workers`: Max concurrent API calls (default: 5, max: 10)
- `shared_system`: System context shared across ALL invocations (for cache efficiency)
- `cache_shared_system`: Add cache_control to shared_system (default: False)

**Returns:** List of response strings in same order as prompts

**Note:** For optimal cost savings, put large common context (1024+ tokens) in `shared_system` with `cache_shared_system=True`. First invocation creates cache, subsequent ones reuse it (90% cost reduction).

### `invoke_claude_streaming()`

Stream responses in real-time with optional callbacks:

```python
invoke_claude_streaming(
    prompt: str | list[dict],
    callback: callable = None,
    model: str = "claude-sonnet-4-6",
    system: str | list[dict] | None = None,
    max_tokens: int = 4096,
    temperature: float = 1.0,
    cache_system: bool = False,
    cache_prompt: bool = False,
    **kwargs
) -> str
```

**Parameters:**
- `callback`: Optional function called with each text chunk (str) as it arrives
- (other parameters same as invoke_claude)

**Returns:** Complete accumulated response text

### `invoke_parallel_streaming()`

Parallel invocations with per-agent streaming callbacks:

```python
invoke_parallel_streaming(
    prompts: list[dict],
    callbacks: list[callable] = None,
    model: str = "claude-sonnet-4-6",
    max_tokens: int = 4096,
    max_workers: int = 5,
    shared_system: str | list[dict] | None = None,
    cache_shared_system: bool = False
) -> list[str]
```

**Parameters:**
- `callbacks`: Optional list of callback functions, one per prompt
- (other parameters same as invoke_parallel)

### `invoke_parallel_interruptible()`

Parallel invocations with cancellation support:

```python
invoke_parallel_interruptible(
    prompts: list[dict],
    interrupt_token: InterruptToken = None,
    # ... same other parameters as invoke_parallel
) -> list[str]
```

**Parameters:**
- `interrupt_token`: Optional InterruptToken to signal cancellation
- (other parameters same as invoke_parallel)

**Returns:** List of response strings (None for interrupted tasks)

### `ConversationThread`

Manages multi-turn conversations with automatic caching:

```python
thread = ConversationThread(
    system: str | list[dict] | None = None,
    model: str = "claude-sonnet-4-6",
    max_tokens: int = 4096,
    temperature: float = 1.0,
    cache_system: bool = True
)

response = thread.send(
    user_message: str | list[dict],
    cache_history: bool = True
) -> str
```

**Methods:**
- `send(message, cache_history=True)`: Send message and get response
- `get_messages()`: Get conversation history
- `clear()`: Clear conversation history
- `__len__()`: Get number of turns

**New in 0.3.0:**
- `turn_count` property: Number of completed turn pairs
- `send_continuation(guidance, cache_history)`: Lightweight continuation turn (requires prior `send()`)
- `max_turns` constructor parameter: Optional turn limit
- `continuation_prompt` constructor parameter: Default continuation guidance

### `StallDetector`

Monitors activity timestamps and detects unresponsive operations:

```python
from claude_client import StallDetector

def handle_stall(task_id, idle_seconds):
    print(f"Task {task_id} stalled for {idle_seconds:.1f}s")

detector = StallDetector(timeout=60.0, on_stall=handle_stall)
detector.register("task-1")
detector.start_monitoring(poll_interval=5.0)

# Call heartbeat() during streaming/progress
detector.heartbeat("task-1")

# When done
detector.unregister("task-1")
detector.stop_monitoring()
```

### `TaskTracker` (task_state module)

Formal task lifecycle state machine with enforced transitions:

```python
from task_state import TaskTracker, TaskState

tracker = TaskTracker(max_retries=3)
tracker.add("task-1", category="security")

tracker.claim("task-1")    # UNCLAIMED → CLAIMED
tracker.start("task-1")    # CLAIMED → RUNNING (increments attempt)
tracker.complete("task-1")  # RUNNING → COMPLETED

# On failure with retry:
tracker.fail("task-2", error="timeout")
tracker.retry("task-2")     # FAILED → RETRY_QUEUED (if under max_retries)
tracker.claim("task-2")     # RETRY_QUEUED → CLAIMED

# Query state
tracker.active_count(category="security")
tracker.get_by_state(TaskState.RUNNING)
tracker.summary()  # {"completed": 1, "running": 1, ...}
```

### `invoke_with_retry()` (orchestration module)

Single invocation with exponential backoff:

```python
from orchestration import invoke_with_retry

response = invoke_with_retry(
    "Analyze this code...",
    max_retries=3,
    base_delay_ms=1000,   # 1s, 2s, 4s backoff
    max_delay_ms=10000,   # capped at 10s
)
```

### `invoke_parallel_managed()` (orchestration module)

Full-featured parallel invocations with all Symphony patterns:

```python
from orchestration import invoke_parallel_managed, ConcurrencyLimiter

limiter = ConcurrencyLimiter(
    global_limit=10,
    category_limits={"security": 3, "perf": 3}
)

def reconcile(prompts, tracker):
    # Filter out invalid/duplicate work before dispatch
    return [p for p in prompts if should_run(p)]

results = invoke_parallel_managed(
    prompts=[
        {"prompt": "Security review...", "task_id": "sec-1", "category": "security"},
        {"prompt": "Perf review...", "task_id": "perf-1", "category": "perf"},
    ],
    reconcile=reconcile,
    concurrency_limiter=limiter,
    max_retries=3,
    stall_timeout=60.0,
    on_stall=lambda tid, idle: print(f"{tid} stalled"),
)
```

## Example Workflows

See [references/workflows.md](references/workflows.md) for detailed examples including:
- Multi-expert code review
- Parallel document analysis
- Recursive task delegation
- Advanced Agent SDK delegation patterns
- Prompt caching workflows



## Setup

**Prerequisites:**

1. Install anthropic library:
   ```bash
   uv pip install anthropic
   ```

2. Configure API key via project knowledge file:

   **Option 1 (recommended): Individual file**
   - Create document: `ANTHROPIC_API_KEY.txt`
   - Content: Your API key (e.g., `sk-ant-api03-...`)

   **Option 2: Combined file**
   - Create document: `API_CREDENTIALS.json`
   - Content:
     ```json
     {
       "anthropic_api_key": "sk-ant-api03-..."
     }
     ```

   Get your API key: https://console.anthropic.com/settings/keys

Installation check:
```bash
python3 -c "import anthropic; print(f'✓ anthropic {anthropic.__version__}')"
```

## Error Handling

The module provides comprehensive error handling:

```python
from claude_client import invoke_claude, ClaudeInvocationError

try:
    response = invoke_claude("Your prompt here")
except ClaudeInvocationError as e:
    print(f"API Error: {e}")
    print(f"Status: {e.status_code}")
    print(f"Details: {e.details}")
except ValueError as e:
    print(f"Configuration Error: {e}")
```

Common errors:
- **API key missing**: Add ANTHROPIC_API_KEY.txt to project knowledge (see Setup above)
- **Rate limits**: Reduce max_workers or add delays
- **Token limits**: Reduce prompt size or max_tokens
- **Network errors**: Automatic retry with exponential backoff


## Prompt Caching

For detailed caching workflows and best practices, see [references/workflows.md](references/workflows.md#prompt-caching-workflows).

## Performance Considerations

**Token efficiency:**
- Parallel calls use more tokens but save wall-clock time
- Use prompt caching for shared context (90% cost reduction)
- Use concise system prompts to reduce overhead
- Consider token budgets when setting max_tokens

**Rate limits:**
- Anthropic API has per-minute rate limits
- Default max_workers=5 is safe for most tiers
- Adjust based on your API tier and rate limits

**Cost management:**
- Each invocation consumes API credits
- Monitor usage in Anthropic Console
- Use smaller models (haiku) for simple tasks
- Use prompt caching for repeated context (90% savings)
- Cache lifetime: 5 minutes, refreshed on each use

## Best Practices

1. **Use parallel invocations for independent tasks only**
   - Don't parallelize sequential dependencies
   - Each parallel task should be self-contained

2. **Set appropriate system prompts**
   - Define clear roles/expertise for each instance
   - Keeps responses focused and relevant

3. **Handle errors gracefully**
   - Always wrap invocations in try-except
   - Provide fallback behavior for failures

4. **Test with small batches first**
   - Verify prompts work before scaling
   - Check token usage and costs

5. **Consider alternatives**
   - Not all tasks benefit from multiple instances
   - Sometimes sequential with context is better

## Token Efficiency

This skill uses ~800 tokens when loaded but enables powerful multi-agent patterns that can dramatically improve complex analysis quality and speed.

## See Also

- [references/api-reference.md](references/api-reference.md) - Detailed API documentation
- [Anthropic API Docs](https://docs.anthropic.com/claude/reference) - Official documentation