Create Workflow Command
Create a workflow command that orchestrates multi-step execution through sub-agents with file-based task prompts
- Skill ID
- neolabhq/context-engineering-kit/create-workflow-command
- Publisher
- neolabhq
- Repository
- context-engineering-kit
- Installs
- 0
- Files
- 1
- Synced
- Sep 23, 2026
Open any RiverX project, open the Skills panel in the chat, and search for this identifier. The files are fetched from the source repository at install time.
neolabhq/context-engineering-kit/create-workflow-commandInstalls these files- SKILL.md
What this skill tells the agent
Create Workflow Command
Create a command that orchestrates multi-step workflows by dispatching sub-agents with task-specific instructions stored in separate files.
User Input
Workflow Name: $1
Description: $2Architecture Overview
Workflow commands solve the context bloat problem: instead of embedding detailed step instructions in the main command (polluting orchestrator context), store them in separate task files that sub-agents read on-demand.
plugins/<plugin-name>/
├── commands/
│ └── <workflow>.md # Lean orchestrator (~50-100 tokens per step)
├── agents/ # Optional: reusable executor agents
│ └── step-executor.md # Custom agent with specific tools/behavior
└── tasks/ # All task instructions directly here
├── step-1-<name>.md # Full instructions (~500+ tokens each)
├── step-2-<name>.md
├── step-3-<name>.md
└── common-context.md # Shared context across workflowsKey Principles
1. Context Isolation
Each sub-agent gets its own isolated context window. The main orchestrator stays lean while sub-agents load detailed instructions from files.
| Component | Context Cost | Purpose |
|---|---|---|
| Orchestrator command | ~50-100 tokens/step | Dispatch and coordinate |
| Task file | ~500+ tokens | Detailed step instructions |
| Sub-agent base | ~294 tokens | System prompt overhead |
2. Sub-Agent Capabilities
Sub-agents spawned via Task tool:
| Capability | Available | Notes |
|---|---|---|
| Read tool | ✅ Yes | Can read any file |
| Write tool | ✅ Yes | If not restricted |
| Grep/Glob | ✅ Yes | For code search |
| Skills loading | ❌ No | Skills don't auto-load in sub-agents |
| Spawn sub-agents | ❌ No | Cannot nest Task tool |
| Resume context | ✅ Yes | Via resume parameter |
3. File Reference Pattern
Use ${CLAUDE_PLUGIN_ROOT} for portable paths within plugin:
Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-1-workflow-name.md and execute.Sub-agent will use Read tool to fetch the file content.
Implementation Process
Step 1: Gather Requirements
Ask user (if not provided):
- Workflow name: kebab-case identifier (e.g.,
feature-implementation) - Description: What the workflow accomplishes
- Steps: List of discrete steps with:
- Step name
- Step goal
- Required tools
- Expected output
- Execution mode: Sequential or parallel steps
- Agent type:
general-purposeor custom agent
Step 2: Create Directory Structure
# Create tasks directory (if it doesn't exist)
mkdir -p ${CLAUDE_PLUGIN_ROOT}/tasks
# Optional: Create agents directory (if using custom agents)
mkdir -p ${CLAUDE_PLUGIN_ROOT}/agentsNote: All task files (both workflow-specific steps and shared context) are placed directly in tasks/ without subdirectories.
Step 3: Create Task Files
For each step, create a task file with this structure:
# Step N: <Step Name>
## Context
You are executing step N of the <workflow-name> workflow.
## Goal
<Clear, specific goal for this step>
## Input
<What this step receives from previous steps or user>
## Instructions
1. <Specific action>
2. <Specific action>
3. <Specific action>
## Constraints
- <Limitation or boundary>
- <What NOT to do>
## Expected Output
<What to return to orchestrator>
## Success Criteria
- [ ] <Measurable outcome>
- [ ] <Measurable outcome>Step 4: Create Orchestrator Command
Create the main command file with this pattern:
---
description: <Workflow description>
argument-hint: <Required arguments>
allowed-tools: Task, Read
model: sonnet
---
# <Workflow Name>
## User Input
\`\`\`text
$ARGUMENTS
\`\`\`
## Workflow Execution
### Step 1: <Step Name>
Launch general-purpose agent:
- **Description**: "<3-5 word summary>"
- **Prompt**:
\`\`\`
Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-1-<workflow>-<name>.md and execute.
Context:
- TARGET: $1
- MODE: $2
\`\`\`
**Capture**: <What to extract from result>
### Step 2: <Step Name>
Launch general-purpose agent:
- **Description**: "<3-5 word summary>"
- **Prompt**:
\`\`\`
Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-2-<workflow>-<name>.md and execute.
Context from Step 1:
- <Key data from previous step>
\`\`\`
### Step 3: <Step Name>
[Continue pattern...]
## Completion
Summarize workflow results:
1. <What was accomplished>
2. <Key outputs>
3. <Next steps if any>Frontmatter Options
| Field | Purpose | Default |
|---|---|---|
description | Brief description of workflow purpose | Required |
argument-hint | Expected arguments description | None |
allowed-tools | Tools the command can use | Inherits from conversation |
model | Specific Claude model (sonnet, opus, haiku) | Inherits from conversation |
Model selection:
haiku- Fast, efficient for simple workflowssonnet- Balanced performance (recommended default)opus- Maximum capability for complex orchestration
Execution Patterns
Pattern A: Sequential Steps (Default)
Each step depends on previous step's output:
### Step 1: Analyze
Launch agent → Get analysis result
### Step 2: Plan (uses Step 1 result)
Launch agent with Step 1 context → Get plan
### Step 3: Execute (uses Step 2 result)
Launch agent with Step 2 context → CompletePattern B: Parallel Independent Steps
Steps can run concurrently:
### Analysis Phase (Parallel)
Launch 3 agents simultaneously:
1. Agent 1: Security analysis → Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-1a-security.md
2. Agent 2: Performance analysis → Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-1b-performance.md
3. Agent 3: Code quality analysis → Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-1c-quality.md
**Wait for all**, then consolidate results.
### Synthesis Phase
Launch agent with all analysis results...Pattern C: Stateful Multi-Step (Resume)
When steps need shared context:
### Step 1: Initialize
Launch agent, **capture agent_id**
### Step 2: Continue (same context)
Resume agent using agent_id:
- **resume**: <agent_id from Step 1>
- **prompt**: "Proceed to phase 2: <additional instructions>"Example: Feature Implementation Workflow
Orchestrator Command
---
description: Execute feature implementation through research, planning, and coding phases
argument-hint: [feature-description]
allowed-tools: Task, Read, TodoWrite
model: sonnet
---
# Implement Feature
## User Input
\`\`\`text
$ARGUMENTS
\`\`\`
Create TodoWrite with workflow steps.
## Phase 1: Research
Launch general-purpose agent:
- **Description**: "Research feature requirements"
- **Prompt**:
\`\`\`
Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-1-feature-impl-research.md
Feature: $ARGUMENTS
\`\`\`
**Extract**: Key findings, constraints, existing patterns
## Phase 2: Architecture
Launch general-purpose agent:
- **Description**: "Design feature architecture"
- **Prompt**:
\`\`\`
Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-2-feature-impl-architecture.md
Feature: $ARGUMENTS
Research findings: <summary from Phase 1>
\`\`\`
**Extract**: File structure, components, interfaces
## Phase 3: Implementation
Launch developer agent:
- **Description**: "Implement feature code"
- **Prompt**:
\`\`\`
Read ${CLAUDE_PLUGIN_ROOT}/tasks/step-3-feature-impl-implement.md
Architecture: <summary from Phase 2>
\`\`\`
## Completion
Mark todos complete. Report:
1. Files created/modified
2. Tests added
3. Remaining workTask File Example (step-1-feature-impl-research.md)
# Step 1: Feature Research
## Context
You are the research phase of a feature implementation workflow.
## Goal
Thoroughly understand the feature requirements and existing codebase context before any implementation begins.
## Instructions
1. **Parse Feature Request**
- Extract core requirementsMore from neolabhq
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