v0.6.0 to v0.7.0

---

name: orchestrating-agents

description: Orchestrates parallel API instances, delegated sub-tasks, and multi-agent workflows with streaming and tool-enabled delegation patterns. Routes by surface — native subagents in Cowork and Claude Code, httpx fan-out on claude.ai — and covers Gemini delegation via the Cloudflare AI Gateway on every surface. Use for parallel analysis, multi-perspective reviews, or complex task decomposition.

metadata:

---

## SURFACE ROUTING — read first

Fan-out has three possible engines. Which exist depends on where you are running.

**Pick the engine before writing any orchestration code.**

| Engine | claude.ai | Cowork | Claude Code / CCotw |

|---|:---:|:---:|:---:|

| Native subagents (`Agent` / `Task` / `Workflow`) | ✗ | ✓ | ✓ |

| Gemini via CF AI Gateway (`invoking-gemini`) | ✓ | ✓ | ✓ |

| This skill's httpx fan-out (raw Anthropic API) | ✓ | last resort | last resort |

**Primary discriminator — check the tool list, not the filesystem.** If an `Agent`,

`Task`, or `Workflow` tool is callable, native subagents exist. That single fact

decides the row. Everything below is elaboration.

### If native subagents exist (Cowork, Claude Code, CCotw)

**Use them. Do not hand-roll from this skill.** The managed runtime gives

16-concurrent / 1000-agent ceilings, an approval gate, adversarial cross-review,

and in-session resume — all of which this skill would reimplement worse. Route

model and effort per **`agent-routing`** (calibrated on 300 measured Haiku calls);

do not re-derive that here.

Cowork adds one option Claude Code doesn't: subagents can be **declared** rather

than spawned ad hoc, as `agents/*.md` in a plugin — frontmatter `name`,

`description`, `model`, `effort`, `maxTurns`, `tools`, `disallowedTools`,

`skills`, `memory`, `background`, `isolation: worktree`. They appear as

`plugin-name:agent-name`. Note `hooks`, `mcpServers`, and `permissionMode` are

refused in plugin agents for security, so a declared agent inherits the session's

MCP connections and cannot bring its own.

Reach back into this skill on those surfaces only for what the runtime lacks:

stall detection, or a long-lived `ConversationThread`. **Inter-agent messaging is

NOT on that list** — the runtime ships `SendMessage` and `ListAgents`, and

#### Native inter-agent messaging — `SendMessage` / `ListAgents`

`ListAgents` discovers reachable agents; `SendMessage` delivers plain text to one

by name or id. Both reach subagents, agent-team teammates, and independent

sessions. Official docs: `code.claude.com/docs/en/cross-session-messaging`

(shipped v2.1.224, macOS and Linux).

Four measured behaviors the docs do not state. Each cost a round trip to find;

full method and verbatim receipts in `oaustegard/experiments` →

`subagent-messaging/RESULTS.md`.

- **NEVER reply using the incoming envelope's `from` attribute.** For subagents

that value is the agent *type* (`general-purpose`), not an address, and the

send fails with `No agent named 'general-purpose' is reachable`. Two

same-type peers emit identical `from` values, so it cannot distinguish

senders even in principle. Both the `SendMessage` description and the harness

footer on every delivered message instruct otherwise. **Capture the agentId

from the spawn result and address that.**

- **Subagents have no `ListAgents`.** `ToolSearch("select:ListAgents")` returns

`No matching deferred tools found` — absent, not unloaded. A subagent reaches

`"main"` and any address handed to it in its prompt, and nothing else. **The

topology is a star through the main conversation, not a mesh: hand every peer

its siblings' ids at spawn, or they cannot coordinate.**

- **Delivery queues and never interrupts.** The receiver reads between tool

calls, so a peer inside a long `Bash` call is unreachable until it surfaces.

- **A send to a completed agent resumes it with full context, and the agent

cannot tell.** Asked directly, a resumed agent reports no gap or restart

marker. Instructions shaped as "if you were resumed, do X" never fire —

**state the resume in the message.** Each resume replays the transcript:

~40k tokens for a small agent, and a measured eight-round chain ran

199k → 324k. **Batch questions into one send.**

Contested: `anthropics/claude-code#48160` and `ruvnet/ruflo#2028` report that

subagents can receive but not originate `SendMessage`. A CCotw subagent

originated three sends successfully on 2026-08-12 with no

`CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS` set. **Verify origination in your own

environment before designing around either claim.**

### If native subagents do NOT exist (claude.ai chat and project sessions)

Two engines, and **Gemini is the default** — see `subagent-delegation-protocol`

in ops. Use this skill's httpx fan-out when you specifically want Claude-family

output, multi-turn threads with cached history, or inter-agent messaging.

### Gemini via Cloudflare — available on every surface

Even where native subagents exist, Gemini is the right call for

mechanical-but-large work (extractions, ports, boilerplate, schema transforms)

and for a genuinely independent second opinion in a judge panel — a different

model family fails differently, which is the whole point of a panel.

Call mechanics live in **`invoking-gemini`**; do not duplicate them here. Three

things that bite:

- Pass the **explicit model string** `gemini-3.6-flash`. The `flash` alias still

resolves to 3.5 until that plugin's model table regenerates.

- `thinking_level` is a string in `{minimal, low, medium, high}`, default

`medium`. Set `minimal` for mechanical generation or the model silently spends

its output budget reasoning — symptom is an empty or truncated response.

- Credentials come from the CF AI Gateway config, BYOK. Requests route through

the gateway rather than Google directly.

### Non-negotiable on all surfaces

**Review is not delegable.** Diff security- and protocol-critical paths

line-by-line against source, run syntax/lint checks, live-test whatever is

network-testable. Delegated output ships only after your own review, regardless

of which model produced it or which engine ran it.

Cross-model review tools (`challenge`, `verify_patch`) keep their own model

config, often deliberately a Claude. This routing does not silently repoint them.

# Orchestrating Agents

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

## Quick Start

### Single Invocation

```python

import sys

from claude_client import invoke_claude

response = invoke_claude(

prompt="Analyze this code for security vulnerabilities: ...",

)

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"

}

]

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

| 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](references/function-reference.md).

## 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

## Execute Mode (Default Sub-Agent Prompt)

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

```python

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:

```python

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:

```python

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:

# 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:

```bash

uv pip install anthropic

```

2. Configure the API key **as a file the shell reads directly** — never as

something a tool call returns.

On claude.ai the project's files are mounted at `/mnt/project`, so the key can

be sourced without ever entering context:

```bash

set -a; . /mnt/project/ANTHROPIC.env 2>/dev/null; set +a

```

⚠️ **Do not use `project_read` to fetch a credential, on any surface.** Small

docs are returned *inline*, so the key lands in the transcript — verified

2026-07-30: the documented "large text is written to a local file" branch does

not fire even at 64 KB. In Cowork there is no `/mnt/project` mount at all and

no safe read path, so the key must arrive by a route the shell can read

(synced skill directory, or fetched by a script from the CF config store).

Writing is safe in both directions — `project_write` with `local_path` keeps

contents out of context — but reading is not.

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

- Verify prompts work before scaling

- Check token usage and costs

## Token Efficiency

Loading this skill costs roughly 2k tokens. On surfaces with native subagents the

routing table at the top is usually all you need — read it, spawn natively, and

skip the rest of the file.

## See Also

**Routing companions — read these before choosing an engine:**

- `agent-routing` skill — model + effort selection for **native** subagents

(Haiku/Sonnet/Opus, cascades, verifier gates). Calibrated on measured data.

Applies to Cowork and Claude Code; explicitly not to claude.ai.

- `invoking-gemini` skill — call mechanics for the CF AI Gateway path, model

table, and `thinking_level` semantics.

- `subagent-delegation-protocol` (ops config) — why Gemini is the claude.ai

default, the Sonnet fallback config, and the non-delegable-review rule.

**This skill's own internals:**

- [references/function-reference.md](references/function-reference.md) - Full signatures for every function this skill exposes

- [references/api-reference.md](references/api-reference.md) - Anthropic API details: models, rate limits, caching

- [references/workflows.md](references/workflows.md) - Worked orchestration examples

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