Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Dispatching Parallel Agents
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
skill openclawclawhub Free
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- zlc000190
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Overview
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
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
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
// In Claude Code / AI environment Task("Fix agent-tool-abort.test.ts failures") Task("Fix batch-completion-behavior.test.ts failures") Task("Fix tool-approval-race-conditions.test.ts failures") // All three run concurrently
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: Focused - One clear problem domain Self-contained - All context needed to understand the problem Specific about output - What should the agent return? 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
β Too broad: "Fix all the tests" - agent gets lost β Specific: "Fix agent-tool-abort.test.ts" - focused scope β No context: "Fix the race condition" - agent doesn't know where β Context: Paste the error messages and test names β No constraints: Agent might refactor everything β Constraints: "Do NOT change production code" or "Fix tests only" β Vague output: "Fix it" - you don't know what changed β 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 β Fix agent-tool-abort.test.ts Agent 2 β Fix batch-completion-behavior.test.ts Agent 3 β 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
Parallelization - Multiple investigations happen simultaneously Focus - Each agent has narrow scope, less context to track Independence - Agents don't interfere with each other Speed - 3 problems solved in time of 1
Verification
After agents return: Review each summary - Understand what changed Check for conflicts - Did agents edit same code? Run full suite - Verify all fixes work together 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
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md