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Create Workflow Command

OrganizationPopular
NeoLabHQ
create-workflow-command

Create a workflow command that orchestrates multi-step execution through sub-agents with file-based task prompts

Overview

PublisherNeoLabHQ
Repositorycontext-engineering-kit
Skill namecreate-workflow-command
Stars
1.7K
Forks
159
Bundled files
Instructions only
LicenseGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by NeoLabHQ on GitHub. Read the source before you install it.

Installation

Install the Create Workflow Command AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/NeoLabHQ/context-engineering-kit.git /tmp/context-engineering-kit
mkdir -p .claude/skills
cp -r /tmp/context-engineering-kit/antigravity/skills/create-workflow-command .claude/skills/create-workflow-command
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Create Workflow Command in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Create Workflow Command on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Create Workflow Command is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

text
Workflow Name: $1
Description: $2

Architecture 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 workflows

Key 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.

ComponentContext CostPurpose
Orchestrator command~50-100 tokens/stepDispatch and coordinate
Task file~500+ tokensDetailed step instructions
Sub-agent base~294 tokensSystem prompt overhead

2. Sub-Agent Capabilities

Sub-agents spawned via Task tool:

CapabilityAvailableNotes
Read tool✅ YesCan read any file
Write tool✅ YesIf not restricted
Grep/Glob✅ YesFor code search
Skills loading❌ NoSkills don't auto-load in sub-agents
Spawn sub-agents❌ NoCannot nest Task tool
Resume context✅ YesVia resume parameter

3. File Reference Pattern

Use ${CLAUDE_PLUGIN_ROOT} for portable paths within plugin:

markdown
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):

  1. Workflow name: kebab-case identifier (e.g., feature-implementation)
  2. Description: What the workflow accomplishes
  3. Steps: List of discrete steps with:
    • Step name
    • Step goal
    • Required tools
    • Expected output
  4. Execution mode: Sequential or parallel steps
  5. Agent type: general-purpose or custom agent

Step 2: Create Directory Structure

bash
# 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}/agents

Note: 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:

markdown
# 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:

markdown
---
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
FieldPurposeDefault
descriptionBrief description of workflow purposeRequired
argument-hintExpected arguments descriptionNone
allowed-toolsTools the command can useInherits from conversation
modelSpecific Claude model (sonnet, opus, haiku)Inherits from conversation

Model selection:

  • haiku - Fast, efficient for simple workflows
  • sonnet - 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:

markdown
### 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 → Complete

Pattern B: Parallel Independent Steps

Steps can run concurrently:

markdown
### 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:

markdown
### 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

markdown
---
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 work

Task File Example (step-1-feature-impl-research.md)

markdown
# 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 requirements
   - Identify acceptance criteria
   - Note any constraints mentioned

2. **Codebase Analysis**
   - Search for similar existing features
   - Identify relevant patterns and conventions
   - Find reusable components/utilities

3. **Dependency Check**
   - What existing code will this feature interact with?
   - Are there breaking change risks?
   - What tests exist for related functionality?

4. **Gap Analysis**
   - What's missing from the request?
   - What clarifications might be needed?
   - What edge cases should be considered?

## Constraints
- Do NOT write any implementation code
- Do NOT modify any files
- Focus purely on research and analysis

## Expected Output

Return a structured research summary:

\`\`\`markdown
## Feature Understanding
- Core requirement: <summary>
- Acceptance criteria: <list>

## Codebase Context
- Similar features: <list with file paths>
- Patterns to follow: <list>
- Reusable code: <list with file paths>

## Dependencies
- Files affected: <list>
- Tests to consider: <list>

## Open Questions
- <Question 1>
- <Question 2>

## Recommendation
<Brief recommendation for architecture phase>
\`\`\`

## Success Criteria
- [ ] Feature requirements clearly articulated
- [ ] Relevant existing code identified
- [ ] No implementation attempted
- [ ] Clear handoff to architecture phase

Known Limitations

LimitationImpactWorkaround
No nested sub-agentsSub-agents can't spawn Task toolKeep all orchestration in main command
No skill auto-loadingSub-agents don't trigger skillsPass explicit file paths or inline context
Fresh context per agentEach dispatch starts emptyUse resume pattern OR pass summaries
File read latencyExtra tool call per stepAcceptable trade-off for context savings

Validation Checklist

Before finalizing workflow command:

  • Each step has clear, specific goal
  • Task files are self-contained (sub-agent doesn't need external context)
  • File paths use ${CLAUDE_PLUGIN_ROOT} for portability
  • Context passed between steps is minimal (summaries, not full data)
  • Orchestrator command stays lean (<100 tokens per step dispatch)
  • Error handling defined for step failures
  • Success criteria measurable for each step

Create the Workflow

Based on user input, create:

  1. Directories:

    • ${CLAUDE_PLUGIN_ROOT}/tasks/ - All task files directly here
    • ${CLAUDE_PLUGIN_ROOT}/agents/ - (Optional) Custom agent definitions
  2. Task files: Create in tasks/ directory with naming pattern step-N-<workflow>-<name>.md

    • Example: step-1-feature-impl-research.md
    • Example: step-2-feature-impl-architecture.md
    • Shared context: common-context.md directly in tasks/
  3. Orchestrator command: Lean dispatch logic in commands/<workflow-name>.md

  4. Custom agents (Optional): If workflow needs specialized agent behavior in agents/

  5. Update plugin.json: Add command to plugin manifest if needed

After creation, suggest testing with /customaize-agent:test-prompt command.

Frequently asked questions

What does the Create Workflow Command AI skill do?

Create a workflow command that orchestrates multi-step execution through sub-agents with file-based task prompts

Why use Create Workflow Command on TypingMind?

Because you install it once and use it with any model. Create Workflow Command is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Create Workflow Command in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NeoLabHQ/context-engineering-kit/tree/master/antigravity/skills/create-workflow-command. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Create Workflow Command?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Create Workflow Command?

As many as you like. As long as a model supports skills, you can use Create Workflow Command with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Create Workflow Command AI skill free?

Yes. It is published on GitHub by NeoLabHQ under the GPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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