---
name: Workflow Helper
slug: workflow-helper-2
category: Automation
description: Workflow Helper guides the creation, execution, and monitoring of multi-step automated workflows. Use it to design pipelines, chain tools or agents, and handle status, retries, and data flow.
github: "https://github.com/vixues/LeAgent/tree/main/backend/leagent/skills/builtin/workflow-helper"
language: Python
stars: 218
forks: 40
install: "npx degit https://github.com/vixues/LeAgent/tree/main/backend/leagent/skills/builtin/workflow-helper ~/.claude/skills/workflow-helper"
installs_to: ~/.claude/skills/workflow-helper
source_path: backend/leagent/skills/builtin/workflow-helper/SKILL.md
collection_size: 7
category_size: 1860
collection_url: "https://dirskills.com/collections/vixues/LeAgent"
added: 2026-09-04T05:25:22.497Z
last_synced: 2026-09-04T05:25:22.497Z
canonical_url: "https://dirskills.com/skills/workflow-helper-2"
---

# Workflow Helper

Workflow Helper guides the creation, execution, and monitoring of multi-step automated workflows. Use it to design pipelines, chain tools or agents, and handle status, retries, and data flow.

**Install:**

```bash
npx degit https://github.com/vixues/LeAgent/tree/main/backend/leagent/skills/builtin/workflow-helper ~/.claude/skills/workflow-helper
```

## README

# Workflow Helper

You are assisting with workflow design and automation. Follow these guidelines.

## Workflow Design

1. **Define the goal**: what outcome should the workflow produce?
2. **Identify steps**: break the goal into discrete, ordered operations.
3. **Map tools**: select the right tool or agent for each step.
4. **Plan data flow**: what inputs each step needs and what outputs it produces.
5. **Handle errors**: decide on retry policy, fallbacks, and failure escalation.

## Workflow Patterns

- **Sequential**: Step A → Step B → Step C. Each step uses the previous step's output.
- **Parallel**: Steps run concurrently when they have no dependencies.
- **Conditional**: Branch based on intermediate results or user input.
- **Loop**: Iterate over a collection with a consistent body.
- **Pipeline**: Stream data through stages (extract → transform → load).

## Common Operations

### Creating a Workflow

- Start from a clear problem statement and acceptance criteria.
- Prefer reusing existing workflow templates before writing a new one.
- Parameterize inputs so the workflow can be reused with different data.

### Running and Monitoring

- Present workflow status clearly: pending, running, succeeded, failed, paused.
- Surface intermediate outputs so the user can diagnose stalled runs.
- Respect cancel / pause signals and clean up subprocesses.

### Integrating with Other Skills

- Use `load_skill` to pull in a specialised skill when a step needs domain expertise.
- Pass structured JSON between steps to keep data shape predictable.

## Best Practices

- Keep individual steps small and idempotent where possible.
- Log meaningful progress updates — workflow runs can be long.
- Document expected inputs, outputs, and failure modes near the workflow definition.
- Use consistent naming conventions (snake_case for workflow IDs).
- Test workflows with small, representative datasets before production runs.