Orchestrating agents

SURFACE ROUTING — read first

This skill hand-rolls subagent orchestration via raw Anthropic API calls. A managed runtime now does the same job. Which one to use depends on your surface:

• In Claude Code (incl. CCotw): use the native runtime, NOT this skill. If you can invoke /deep-research, trigger a run with the workflow keyword, set /effort ultracode, or spawn Task subagents — do that instead. The runtime gives 16-concurrent / 1000-agent ceilings, an approval gate, adversarial cross-review, and in-session resume that this skill would otherwise reimplement badly. Dynamic workflows shipped in research preview (Claude Code v2.1.154+, 2026).
• In claude.ai chat or the bare API (no workflow runtime): use this skill. Parallel API instances over httpx is the only fan-out path here. Proceed below.

Discriminator: do you have a native subagent/Task tool or a workflow command? Yes → native. No → this skill. Never reimplement the runtime where it already exists.

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

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

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%:

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:

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:

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:

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:

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

Function	Module	Purpose
invoke_claude()	core	Single synchronous invocation, full parameter control
invoke_parallel()	core	Concurrent invocations, results in input order
invoke_claude_streaming()	core	Single invocation, token-by-token callback
invoke_parallel_streaming()	core	Concurrent invocations with per-agent stream callbacks
invoke_parallel_interruptible()	core	Concurrent invocations cancellable mid-flight
ConversationThread	core	Stateful multi-turn thread with cached history
StallDetector	core	Flags agents idle beyond a timeout
TaskTracker	task_state	Tracks task status across an orchestration run
invoke_with_retry()	orchestration	Single invocation with backoff on transient errors
invoke_parallel_managed()	orchestration	Concurrency-limited parallel run with retry, stall hooks, reconciliation

Full signatures, parameters, and worked examples for each: references/function-reference.md.

Example Workflows

See 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

Execute Mode (Default Sub-Agent Prompt)

For autonomous sub-agents that should execute without asking questions:

from claude_client import invoke_claude, EXECUTE_MODE

response = invoke_claude(
    prompt="Review auth.py for SQL injection vulnerabilities",
    system=f"You are a security expert.\n\n{EXECUTE_MODE}"
)

EXECUTE_MODE encodes these principles (adapted from OpenAI Codex):

• Make assumptions instead of asking questions; state them briefly
• Think ahead: what else might be needed?
• Report failures with what you tried and what you'll do next
• Summarize deliverables and how to validate them

Agent Pool (Named Agents with Messaging)

For workflows where multiple agents need to communicate:

from agent_pool import AgentPool

pool = AgentPool(
    shared_system="You are reviewing the auth module of a web app.",
    max_depth=3,    # prevent recursive spawn explosion
    max_agents=10,
)

# Spawn named agents with roles
pool.spawn("security", system=f"Focus on vulnerabilities.\n\n{pool.EXECUTE_MODE}")
pool.spawn("perf", system=f"Focus on performance.\n\n{pool.EXECUTE_MODE}")

# Run turns (pending inter-agent messages auto-injected)
sec_result = pool.run("security", "Review the login flow")

# Agent-to-agent messaging
pool.send("security", to="perf",
          content="Auth does N+1 queries in the session check loop",
          trigger_turn=True)  # auto-runs perf with this context

# Broadcast to all agents
pool.broadcast("security", "Auth uses bcrypt cost=12, 200ms per hash")

# Query pool state
pool.agents()           # ["security", "perf"]
pool.agent_info("perf") # {name, depth, children, pending_messages, turns}

Spawn Reservation (Atomic Agent Creation)

For complex workflows where agent creation might fail:

from agent_pool import AgentPool

pool = AgentPool(shared_system="Code review team")

# Reservation pattern: name is reserved, rolled back on exception
with pool.reserve("analyst", parent="lead") as res:
    res.configure(system="You analyze code complexity.", model="claude-opus-4-6")
    # If configure or any other work raises, the name is released
# Agent "analyst" is now live

# Depth limits prevent unbounded recursion
pool.spawn("sub-analyst", parent="analyst")  # depth=2, OK
pool.spawn("sub-sub", parent="sub-analyst")  # depth=3, raises ValueError

When to Use AgentPool vs invoke_parallel

Pattern	Use When
invoke_parallel()	Independent tasks, no inter-agent communication needed
AgentPool	Agents need to share findings, build on each other's work, or have parent/child relationships
invoke_parallel_managed()	Independent tasks with retry, stall detection, concurrency limits

Setup

Prerequisites:

1. Install anthropic library:

   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:

     {
       "anthropic_api_key": "sk-ant-api03-..."
     }
     

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

Installation check:

python3 -c "import anthropic; print(f'✓ anthropic {anthropic.__version__}')"

Error Handling

The module provides comprehensive error handling:

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.

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/function-reference.md - Full signatures for every function this skill exposes
• references/api-reference.md - Anthropic API details: models, rate limits, caching
• references/workflows.md - Worked orchestration examples
• Anthropic API Docs - Official documentation