workflows-expert
Activate when requests involve workflow execution, CI/CD pipelines, git automation, or multi-step task orchestration. This skill provides workflows-mcp MCP server integration with tag-based workflow discovery, DAG-based execution, and variable syntax expertise. Trigger on phrases like "run workflow", "execute workflow", "orchestrate tasks", "automate CI/CD", or "workflow information".
Best use case
workflows-expert is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Activate when requests involve workflow execution, CI/CD pipelines, git automation, or multi-step task orchestration. This skill provides workflows-mcp MCP server integration with tag-based workflow discovery, DAG-based execution, and variable syntax expertise. Trigger on phrases like "run workflow", "execute workflow", "orchestrate tasks", "automate CI/CD", or "workflow information".
Teams using workflows-expert should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/workflows-expert/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How workflows-expert Compares
| Feature / Agent | workflows-expert | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Activate when requests involve workflow execution, CI/CD pipelines, git automation, or multi-step task orchestration. This skill provides workflows-mcp MCP server integration with tag-based workflow discovery, DAG-based execution, and variable syntax expertise. Trigger on phrases like "run workflow", "execute workflow", "orchestrate tasks", "automate CI/CD", or "workflow information".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Workflows MCP Expert Skill
Activate when requests involve workflow execution, multi-step task orchestration, or CI/CD automation using the workflows-mcp MCP server.
## Activation Triggers
Activate when user requests mention:
- "run workflow", "execute workflow", "list workflows"
- "orchestrate tasks", "multi-step process", "DAG execution"
- "CI/CD pipeline", "git automation", "automated testing"
- "workflow information", "workflow details"
- Task coordination with dependencies
- "Chain commands", "automate workflow"
Common user phrases: "run tests before deploying", "create build pipeline", "execute only if previous succeeds", "run tasks in parallel"
## Core Workflow Pattern
### Standard execution: Discover → Inspect → Execute
Always use tag-based discovery instead of guessing workflow names. Inspect workflows before execution to understand required inputs.
### Step 1: Discover by Tags
Use `list_workflows` with tags (AND logic - workflows must have ALL tags):
```text
Tool: list_workflows
Parameters:
tags: ['python', 'ci']
format: 'markdown'
```
**Common tag combinations:**
- Python: `['python']`, `['python', 'testing']`, `['python', 'ci']`
- Git: `['git']`, `['git', 'commit']`, `['git', 'checkout']`
- CI/CD: `['ci']`, `['ci', 'deployment']`
- TDD: `['tdd']`, `['tdd', 'phase1']`
### Step 2: Inspect Workflow
Call `get_workflow_info` before executing to understand inputs, outputs, and structure:
```text
Tool: get_workflow_info
Parameters:
workflow: 'python-ci-pipeline'
format: 'markdown'
```
### Step 3: Execute Workflow
```text
Tool: execute_workflow
Parameters:
workflow: 'python-ci-pipeline'
inputs: {project_path: '/path/to/project'}
response_format: 'minimal'
```
**Response status values:**
- `success` - Workflow completed successfully
- `failure` - Workflow failed (check `error` field)
- `paused` - Workflow paused for user input (use `resume_workflow`)
## Available MCP Tools
### Discovery & Information
**list_workflows(tags, format)** - Discover workflows by tags
- Filter with AND logic: `tags=['python', 'testing']`
- Returns workflow names only
**get_workflow_info(workflow, format)** - Get detailed workflow metadata
- Shows inputs, outputs, blocks, dependencies
- Call before executing
### Execution
**execute_workflow(workflow, inputs, response_format)** - Execute registered workflow
- Provide required inputs from `get_workflow_info`
- Use `response_format: 'minimal'` (default) or `'detailed'` (debugging only)
**execute_inline_workflow(workflow_yaml, inputs, response_format)** - Execute YAML directly
- For one-off or custom workflows
- Validate first with `validate_workflow_yaml`
**validate_workflow_yaml(yaml_content)** - Validate workflow before execution
- Catches syntax errors early
- Always validate inline workflows
### Checkpoint Management
**resume_workflow(checkpoint_id, response, response_format)** - Resume paused workflow
- For interactive workflows with Prompt blocks
- Provide user response to continue
**list_checkpoints(workflow_name, format)** - View saved checkpoints
**get_checkpoint_info(checkpoint_id, format)** - Inspect checkpoint details
**delete_checkpoint(checkpoint_id)** - Clean up old checkpoints
## Variable Syntax Quick Reference
All variables use four-namespace architecture:
**Inputs:** `{{inputs.project_name}}`, `{{inputs.workspace}}`
**Metadata:** `{{metadata.workflow_name}}`, `{{metadata.start_time}}`
**Blocks:** `{{blocks.run_tests.outputs.exit_code}}`, `{{blocks.test.succeeded}}`
**Status shortcuts (use for 90% of conditionals):**
- `{{blocks.test.succeeded}}` - True if completed successfully
- `{{blocks.build.failed}}` - True if failed
- `{{blocks.optional.skipped}}` - True if skipped
For detailed variable syntax, load `./references/variable-syntax.md`.
## Essential Patterns
### Conditional Execution
```yaml
blocks:
- id: run_tests
type: Shell
inputs:
command: pytest tests/
- id: deploy
type: Shell
inputs:
command: ./deploy.sh
condition: "{{blocks.run_tests.succeeded}}"
depends_on: [run_tests]
```
### Workflow Composition
```yaml
blocks:
- id: ci_pipeline
type: Workflow
inputs:
workflow: "python-ci-pipeline"
inputs:
project_path: "{{inputs.workspace}}"
```
### Parallel Execution
Blocks without dependencies run in parallel automatically.
## Best Practices
1. **Use tag-based discovery** - Call `list_workflows(tags=[...])` instead of guessing names
2. **Inspect before executing** - Call `get_workflow_info()` to understand requirements
3. **Use status shortcuts** - Prefer `.succeeded` over `.outputs.exit_code == 0`
4. **Minimal response format** - Use `response_format='minimal'` unless debugging
5. **Validate inline workflows** - Use `validate_workflow_yaml()` before `execute_inline_workflow()`
6. **Explicit namespaces** - Always use `inputs.*`, `blocks.*`, `metadata.*`
## Reference Documentation
Load these files as needed using the Read tool:
### Variable Syntax Reference
[`./references/variable-syntax.md`](./references/variable-syntax.md)
**Load when:** Resolving variable syntax errors, understanding namespace architecture, debugging variable resolution, or learning advanced patterns.
**Contains:** Complete variable resolution rules, recursive resolution, cross-block references, common mistakes, debugging techniques.
### Block Executors Reference
[`./references/block-executors.md`](./references/block-executors.md)
**Load when:** Understanding available block types (Shell, Workflow, CreateFile, ReadFile, etc.), checking input/output specs, learning execution patterns, troubleshooting block issues.
**Contains:** Complete block type reference, input/output specifications, execution states, status vs outcome distinction, patterns and troubleshooting.
### Complete Workflow Examples
[`./references/examples.md`](./references/examples.md)
**Load when:** Implementing complex multi-stage workflows, building parallel execution pipelines, creating file processing workflows, designing interactive approval workflows, learning advanced patterns.
**Contains:** Full workflow examples with documentation, multi-stage deployments, parallel testing with aggregation, file processing pipelines, interactive deployments.
## Example Workflow Templates
**Directory:** `./examples/`
**Available templates:**
- `./examples/simple-ci-pipeline.yaml` - Basic CI pipeline with sequential execution
- `./examples/conditional-deploy.yaml` - Environment-based deployment with conditions
- `./examples/parallel-testing.yaml` - Parallel test execution with result aggregation
**Usage:** Copy and modify YAML templates for custom workflows. Execute using `execute_inline_workflow` tool.
## Troubleshooting Quick Guide
**Variable errors:** Verify namespace (`inputs.*`, `blocks.*`, `metadata.*`), check block ID case-sensitivity, ensure `depends_on` for output references
**Execution failures:** Check `error` field, use `response_format: "detailed"` for debugging, verify required inputs, validate condition syntax
**Workflow not found:** Use `list_workflows()` to see available workflows, check name spelling (case-sensitive), verify MCP connection
## Summary
The workflows-mcp server enables workflow orchestration through:
1. **Tag-based discovery** - Find workflows by purpose
2. **Workflow inspection** - Understand requirements before execution
3. **DAG-based execution** - Automatic dependency resolution and parallel execution
4. **Conditional logic** - Boolean shortcuts for clean conditions
5. **Workflow composition** - Build complex pipelines from reusable workflows
### Key Pattern
Always follow: Discover → Inspect → ExecuteRelated Skills
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