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docs(superpowers): point the vendored cluster at the tools this harness has
The vendored skills were written against a generic `Task()` and an operator
called "your human partner". Neither exists here. An agent following
dispatching-parallel-agents literally would call a tool that is not in its list.

`Task tool` becomes the `Agent` tool with `subagent_type` across all six prompt
templates and the Gemini mapping table, and the dispatch example gains the three
options that actually matter for parallel work: issuing every dispatch in ONE
message (several Agent uses in a single turn run concurrently; one per turn
serialises them), `isolation: "worktree"` so two agents cannot fight over the
same tree, and per-agent `model` override for cheap mechanical fan-out.

`Workflow` is named as the structured alternative — phases, per-finding verify,
fan-in — with its opt-in condition stated, because a skill that told an agent to
reach for it unprompted would be teaching a policy violation.

"your human partner" becomes "the operator" in five skills. Beyond phrasing, it
was factually wrong in executing-plans, which told the reader to *ask* whether
subagents are available; on this fleet Claude Code has them and Crush/Pi/OpenCode
do not, so it now says which skill to use where.

Closes #17
Bump-Type: patch
2026-08-29 11:43:46 +02:00

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7.5 KiB
Markdown

---
name: dispatching-parallel-agents
surfaces: [claude-code, library]
summary: "Fan work out across parallel subagents"
use_when: "Decomposing work for concurrent agents"
description: Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
---
# Dispatching Parallel Agents
## Overview
You delegate tasks to specialized agents with isolated context. By precisely crafting their instructions and context, you ensure they stay focused and succeed at their task. They should never inherit your session's context or history — you construct exactly what they need. This also preserves your own context for coordination work.
When you have multiple unrelated failures (different test files, different subsystems, different bugs), investigating them sequentially wastes time. Each investigation is independent and can happen in parallel.
**Core principle:** Dispatch one agent per independent problem domain. Let them work concurrently.
## When to Use
```dot
digraph when_to_use {
"Multiple failures?" [shape=diamond];
"Are they independent?" [shape=diamond];
"Single agent investigates all" [shape=box];
"One agent per problem domain" [shape=box];
"Can they work in parallel?" [shape=diamond];
"Sequential agents" [shape=box];
"Parallel dispatch" [shape=box];
"Multiple failures?" -> "Are they independent?" [label="yes"];
"Are they independent?" -> "Single agent investigates all" [label="no - related"];
"Are they independent?" -> "Can they work in parallel?" [label="yes"];
"Can they work in parallel?" -> "Parallel dispatch" [label="yes"];
"Can they work in parallel?" -> "Sequential agents" [label="no - shared state"];
}
```
**Use when:**
- 3+ test files failing with different root causes
- Multiple subsystems broken independently
- Each problem can be understood without context from others
- No shared state between investigations
**Don't use when:**
- Failures are related (fix one might fix others)
- Need to understand full system state
- Agents would interfere with each other
## The Pattern
### 1. Identify Independent Domains
Group failures by what's broken:
- File A tests: Tool approval flow
- File B tests: Batch completion behavior
- File C tests: Abort functionality
Each domain is independent - fixing tool approval doesn't affect abort tests.
### 2. Create Focused Agent Tasks
Each agent gets:
- **Specific scope:** One test file or subsystem
- **Clear goal:** Make these tests pass
- **Constraints:** Don't change other code
- **Expected output:** Summary of what you found and fixed
### 3. Dispatch in Parallel
Issue every dispatch in **one** message — several `Agent` tool uses in a single
assistant turn run concurrently; one per turn runs them in series.
```
Agent(description: "fix abort tests", subagent_type: "general-purpose", prompt: ...)
Agent(description: "fix batch tests", subagent_type: "general-purpose", prompt: ...)
Agent(description: "fix approval tests", subagent_type: "general-purpose", prompt: ...)
```
Options that matter for this pattern:
- `run_in_background: true` (the default) returns immediately and notifies on
completion. Set it to `false` only when your very next action depends on the result.
- `isolation: "worktree"` gives an agent its own git worktree. Use it whenever two
agents might touch the same files — parallel writers to one tree is the failure this
skill's independence rule exists to avoid, and a worktree makes it structural rather
than a matter of discipline.
- `model` overrides the model per agent; cheap mechanical fan-out does not need the
same model as the reasoning that planned it.
For a fan-out with real structure — phases, a verify pass per finding, fan-in — the
`Workflow` tool expresses it directly with `agent()`, `parallel()` and `pipeline()`.
It spawns many agents and costs accordingly, so it runs **only on explicit opt-in from
the operator**; do not reach for it just because a task looks parallel.
### 4. Review and Integrate
When agents return:
- Read each summary
- Verify fixes don't conflict
- Run full test suite
- Integrate all changes
## Agent Prompt Structure
Good agent prompts are:
1. **Focused** - One clear problem domain
2. **Self-contained** - All context needed to understand the problem
3. **Specific about output** - What should the agent return?
```markdown
Fix the 3 failing tests in src/agents/agent-tool-abort.test.ts:
1. "should abort tool with partial output capture" - expects 'interrupted at' in message
2. "should handle mixed completed and aborted tools" - fast tool aborted instead of completed
3. "should properly track pendingToolCount" - expects 3 results but gets 0
These are timing/race condition issues. Your task:
1. Read the test file and understand what each test verifies
2. Identify root cause - timing issues or actual bugs?
3. Fix by:
- Replacing arbitrary timeouts with event-based waiting
- Fixing bugs in abort implementation if found
- Adjusting test expectations if testing changed behavior
Do NOT just increase timeouts - find the real issue.
Return: Summary of what you found and what you fixed.
```
## Common Mistakes
**❌ Too broad:** "Fix all the tests" - agent gets lost
**✅ Specific:** "Fix agent-tool-abort.test.ts" - focused scope
**❌ No context:** "Fix the race condition" - agent doesn't know where
**✅ Context:** Paste the error messages and test names
**❌ No constraints:** Agent might refactor everything
**✅ Constraints:** "Do NOT change production code" or "Fix tests only"
**❌ Vague output:** "Fix it" - you don't know what changed
**✅ Specific:** "Return summary of root cause and changes"
## When NOT to Use
**Related failures:** Fixing one might fix others - investigate together first
**Need full context:** Understanding requires seeing entire system
**Exploratory debugging:** You don't know what's broken yet
**Shared state:** Agents would interfere (editing same files, using same resources)
## Real Example from Session
**Scenario:** 6 test failures across 3 files after major refactoring
**Failures:**
- agent-tool-abort.test.ts: 3 failures (timing issues)
- batch-completion-behavior.test.ts: 2 failures (tools not executing)
- tool-approval-race-conditions.test.ts: 1 failure (execution count = 0)
**Decision:** Independent domains - abort logic separate from batch completion separate from race conditions
**Dispatch:**
```
Agent 1 → Fix agent-tool-abort.test.ts
Agent 2 → Fix batch-completion-behavior.test.ts
Agent 3 → Fix tool-approval-race-conditions.test.ts
```
**Results:**
- Agent 1: Replaced timeouts with event-based waiting
- Agent 2: Fixed event structure bug (threadId in wrong place)
- Agent 3: Added wait for async tool execution to complete
**Integration:** All fixes independent, no conflicts, full suite green
**Time saved:** 3 problems solved in parallel vs sequentially
## Key Benefits
1. **Parallelization** - Multiple investigations happen simultaneously
2. **Focus** - Each agent has narrow scope, less context to track
3. **Independence** - Agents don't interfere with each other
4. **Speed** - 3 problems solved in time of 1
## Verification
After agents return:
1. **Review each summary** - Understand what changed
2. **Check for conflicts** - Did agents edit same code?
3. **Run full suite** - Verify all fixes work together
4. **Spot check** - Agents can make systematic errors
## Real-World Impact
From debugging session (2025-10-03):
- 6 failures across 3 files
- 3 agents dispatched in parallel
- All investigations completed concurrently
- All fixes integrated successfully
- Zero conflicts between agent changes