parallel-execution skill
✓ VerifiedEnables an agent to spawn multiple subagents simultaneously to execute independent tasks in parallel.
- GitHub stars
- 1,423
- Files
- 1
- Last commit
- Oct 6, 2026
- License
- MIT
- Allowed tools
- —
What does the parallel-execution skill do?
parallel-execution enables an agent to spawn multiple subagents simultaneously to execute independent tasks in parallel.
Summary generated from the skill's SKILL.md.
About the parallel-execution skill
Spawning multiple subagents to handle independent work simultaneously is what this skill enables. Developers use it to reduce total execution time by running N tasks concurrently instead of one after another. An agent should load this skill when coordinating multi-agent tasks, performing concurrent operations, or implementing features that can be parallelized across different files or concerns.
To achieve true parallelism, the agent must include all Agent tool calls within a single assistant message. The skill relies on the Agent tool, which was previously known as Task. For certain environments, users may need to include the run_in_background: true parameter to ensure subagents do not block the main process. Once the subagents finish their specific roles, they automatically return their results to the parent conversation for synthesis.
This approach is highly effective for directory-based analysis, perspective-based reviews, or implementing independent feature components. It is not suitable for sequential workflows where one task depends on the output of another or when multiple subagents attempt to modify the same files.
What an agent can do with it
- 1Analyze multiple directories at onceAn agent can spawn separate subagents to examine different parts of a codebase like auth, api, and db simultaneously.
- 2Perform multi-perspective code reviewsA developer can request a single review that covers security, performance, testing, and architecture through concurrent subagents.
- 3Accelerate feature implementationYou can implement several independent parts of a feature, such as different API endpoints, at the same time.
Written from the skill's SKILL.md. Every use case is checked against a quote from it.
How to install the parallel-execution skill
Each command copies the skill folder into the client's skills directory. Restart the client or start a new session to load it.Install the parallel-execution skill in Plugin
/plugin marketplace add CloudAI-X/claude-workflow-v2 /plugin install project-starter@claude-workflow
Install the parallel-execution skill in Claude Code
~/.claude/skills/parallel-execution/SKILL.md
$ npx degit CloudAI-X/claude-workflow-v2/skills/parallel-execution ~/.claude/skills/parallel-executionInstall the parallel-execution skill in Claude Code (project)
.claude/skills/parallel-execution/SKILL.md
$ npx degit CloudAI-X/claude-workflow-v2/skills/parallel-execution .claude/skills/parallel-executionInstall the parallel-execution skill in Codex
~/.agents/skills/parallel-execution/SKILL.md
$ npx degit CloudAI-X/claude-workflow-v2/skills/parallel-execution ~/.agents/skills/parallel-executionInstall the parallel-execution skill in Gemini CLI
~/.gemini/skills/parallel-execution/SKILL.md
$ npx degit CloudAI-X/claude-workflow-v2/skills/parallel-execution ~/.gemini/skills/parallel-executionInstall the parallel-execution skill in Claude.ai
npx degit CloudAI-X/claude-workflow-v2/skills/parallel-execution parallel-execution && zip -r parallel-execution.zip parallel-execution # claude.ai → Customize → Skills → + → Create skill → Upload a skill → parallel-execution.zip
Automated checks
Ran Nov 14, 2026Full report →Passed our automated checks. That is not a security audit: read the scripts before you let an agent run them. How checks work
- Source repository linked and reachablecloudai-x/claude-workflow-v2/skills/parallel-execution is public and reachablegithub.com/cloudai-x/claude-workflow-v2/tree/HEAD/skills/par ↗Pass
- Open source license (OSI)MITspdx.org/licenses/MIT.html ↗Pass
- Commit or release in the last yearLast commit 2026-10-06github.com/cloudai-x/claude-workflow-v2/commits ↗Pass
- No download-and-run or credential theft in scriptsNo scripts in the skill folderPass
- No hidden instructions in SKILL.mdNo override phrases, safeguard switches, credential requests or hidden charactersgithub.com/cloudai-x/claude-workflow-v2/blob/HEAD/skills/par ↗Pass
- Pre-approved tools are scopedNo pre-approved tools; the agent asks before using onegithub.com/cloudai-x/claude-workflow-v2/blob/HEAD/skills/par ↗Pass
- SKILL.md follows the Agent Skills formatValid name and description; says when to use it; 236 linesgithub.com/cloudai-x/claude-workflow-v2/blob/HEAD/skills/par ↗Pass
SKILL.md
View on GitHubParallel Execution Patterns
When to Load
- Trigger: Multi-agent tasks, concurrent operations, spawning subagents, parallelizing independent work
- Skip: Single-step tasks or sequential workflows with no parallelization opportunity
Core Concept
Parallel execution spawns multiple subagents simultaneously using the Agent tool (named Task before Claude Code 2.1.63; Task still works as an alias). Subagents run in the background by default, so N tasks run concurrently, dramatically reducing total execution time.
Critical Rule: ALL Agent calls MUST be in a SINGLE assistant message for true parallelism. If the calls are in separate messages, they launch one after another.
Execution Protocol
Step 1: Identify Parallelizable Tasks
Before spawning, verify tasks are independent:
- No task depends on another's output
- Tasks target different files or concerns
- Can run simultaneously without conflicts
Step 2: Prepare Dynamic Subagent Prompts
Each subagent receives a custom prompt defining its role:
You are a [ROLE] specialist for this specific task.
Task: [CLEAR DESCRIPTION]
Context:
[RELEVANT CONTEXT ABOUT THE CODEBASE/PROJECT]
Files to work with:
[SPECIFIC FILES OR PATTERNS]
Output format:
[EXPECTED OUTPUT STRUCTURE]
Focus areas:
- [PRIORITY 1]
- [PRIORITY 2]
Step 3: Launch All Tasks in ONE Message
CRITICAL: Make ALL Agent calls in the SAME assistant message:
I'm launching N parallel subagents:
[Agent 1]
description: "Subagent A - [brief purpose]"
prompt: "[detailed instructions for subagent A]"
[Agent 2]
description: "Subagent B - [brief purpose]"
prompt: "[detailed instructions for subagent B]"
[Agent 3]
description: "Subagent C - [brief purpose]"
prompt: "[detailed instructions for subagent C]"
On Claude Code versions that still run subagents in the foreground by default, add run_in_background: true to each call.
Step 4: Collect Results
Each subagent returns its final result to the parent conversation automatically when it finishes. Wait until every subagent has reported before synthesizing; do not poll, and do not start dependent work early. (The separate TaskOutput call is deprecated.)
Step 5: Synthesize Results
Combine all subagent outputs into unified result:
- Merge related findings
- Resolve conflicts between recommendations
- Prioritize by severity/importance
- Create actionable summary
Dynamic Subagent Patterns
Pattern 1: Task-Based Parallelization
When you have N tasks to implement, spawn N subagents:
Plan:
1. Implement auth module
2. Create API endpoints
3. Add database schema
4. Write unit tests
5. Update documentation
Wave 1 - spawn 3 subagents (independent of each other):
- Subagent 1: Implements auth module
- Subagent 2: Creates API endpoints
- Subagent 3: Adds database schema
Wave 2 - after wave 1 has finished (these depend on its output):
- Subagent 4: Writes unit tests
- Subagent 5: Updates documentation
Pattern 2: Directory-Based Parallelization
Analyze multiple directories simultaneously:
Directories: src/auth, src/api, src/db
Spawn 3 subagents:
- Subagent 1: Analyzes src/auth
- Subagent 2: Analyzes src/api
- Subagent 3: Analyzes src/db
Pattern 3: Perspective-Based Parallelization
Review from multiple angles simultaneously:
Perspectives: Security, Performance, Testing, Architecture
Spawn 4 subagents:
- Subagent 1: Security review
- Subagent 2: Performance analysis
- Subagent 3: Test coverage review
- Subagent 4: Architecture assessment
Task List Integration
When using parallel execution, task tracking (TaskCreate/TaskUpdate, or TodoWrite on older versions) differs:
Sequential execution: Only ONE task in_progress at a time
Parallel execution: MULTIPLE tasks can be in_progress simultaneously
# Before launching parallel tasks
todos = [
{ content: "Task A", status: "in_progress" },
{ content: "Task B", status: "in_progress" },
{ content: "Task C", status: "in_progress" },
{ content: "Synthesize results", status: "pending" }
]
# As each subagent reports back, mark its task completed
todos = [
{ content: "Task A", status: "completed" },
{ content: "Task B", status: "completed" },
{ content: "Task C", status: "completed" },
{ content: "Synthesize results", status: "in_progress" }
]
When to Use Parallel Execution
Good candidates:
- Multiple independent analyses (code review, security, tests)
- Multi-file processing where files are independent
- Exploratory tasks with different perspectives
- Verification tasks with different checks
- Feature implementation with independent components
Avoid parallelization when:
- Tasks have dependencies (Task B needs Task A's output)
- Sequential workflows are required (commit -> push -> PR)
- Tasks modify the same files (risk of conflicts)
- Order matters for correctness
Performance Benefits
| Approach | 5 Tasks @ 30s each | Total Time |
|---|---|---|
| Sequential | 30s + 30s + 30s + 30s + 30s | ~150s |
| Parallel | All 5 run simultaneously | ~30s |
Parallel execution is approximately Nx faster where N is the number of independent tasks.
Example: Feature Implementation
User request: "Implement user authentication with login, registration, and password reset"
Orchestrator creates plan:
- Implement login endpoint
- Implement registration endpoint
- Implement password reset endpoint
- Add authentication middleware
- Write integration tests
Parallel execution:
Wave 1 - launching 4 subagents in parallel:
[Agent 1] Login endpoint implementation
[Agent 2] Registration endpoint implementation
[Agent 3] Password reset endpoint implementation
[Agent 4] Auth middleware implementation
[Results arrive as each subagent finishes]
Wave 2 - depends on wave 1:
[Agent 5] Integration test writing
[Synthesize into cohesive implementation]
Troubleshooting
Tasks running sequentially?
- Verify ALL Agent calls are in a SINGLE message
- On older Claude Code versions, check
run_in_background: trueis set for each
Results not available?
- Results are delivered when each subagent finishes; wait for all of them
- A subagent that was denied a permission may return without finishing its work; check its report
Conflicts in output?
- Ensure tasks don't modify same files
- Add conflict resolution in synthesis step
Files in this skill
1parallel-execution skill questions
How do I install the parallel-execution skill in Claude Code?
Run npx degit CloudAI-X/claude-workflow-v2/skills/parallel-execution ~/.claude/skills/parallel-execution. Claude Code loads skills from ~/.claude/skills (all projects) or .claude/skills (one project) and uses a skill when its description matches the task.
Which clients can use the parallel-execution skill?
Any client that reads the Agent Skills format: Claude Code, Codex, Gemini CLI and claude.ai (upload as a ZIP). Scripts in the skill run only where the client allows code execution.
How do I ensure subagents run in parallel rather than sequentially?
All Agent calls must be included in a single assistant message. If they are in separate messages, they will launch one after another.
When should I avoid using parallel execution?
Avoid parallelization if tasks have dependencies, if the workflow must be sequential, or if tasks modify the same files which creates a risk of conflicts.
How do I collect the results from multiple subagents?
Each subagent automatically returns its final result to the parent conversation upon completion; you should wait for all subagents to report before synthesizing the results.