What makes AI Workflow Automation Expert notable?

3.4K downloads. Trust evidence available

How should AI Workflow Automation Expert be evaluated before use?

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agentCLAWHUBUnverified

AI Workflow Automation Expert

AI Workflow Automation Expert skill. Helps users automate repetitive tasks using AI agents, OpenClaw skills, and multi-agent orchestration. Triggers on "work... Skill: AI Workflow Automation Expert Owner: xiatian5 Summary: AI Workflow Automation Expert skill. Helps users automate repetitive tasks using AI agents, OpenClaw skills, and multi-agent orchestration. Triggers on "work... Tags: agent:1.0.0, automation:1.0.0, latest:1.0.0, workflow:1.0.0 Version history: v1.0.0 | 2026-03-27T03:07:50.645Z | user Initial release Archive index: Archive v1.0.0: 5 files, 6004 bytes Files:

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

3.4k

Updated

Oct 9, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 3.4K downloads reported by the source. Last updated 10/9/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 9, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 9, 2026
Adoption signal: 3.4K downloadsadoption · observed Oct 9, 2026
Latest release: 1.0.0release · observed Mar 27, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s1764r9t6hqrdy95j73zbh974183mfa0:ai-workflow-automation

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-xiatian5-ai-workflow-automation/snapshot"

Documentation

CLAWHUB

12,065 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: ai-workflow-automation
description: AI Workflow Automation Expert skill. Helps users automate repetitive tasks using AI agents, OpenClaw skills, and multi-agent orchestration. Triggers on "workflow automation", "automate tasks", "AI agent setup", "build automation", "process automation", "OpenClaw automation", "multi-agent", "task orchestration". Provides end-to-end workflow design, skill recommendations, and implementation guides.
---

# AI Workflow Automation Expert

Turn repetitive work into autonomous AI workflows. This skill guides you through analyzing, designing, and implementing automation solutions using OpenClaw and its skill ecosystem.

## When This Skill Triggers

- "Help me automate [task/process]"
- "Build an AI agent workflow for..."
- "How do I set up automation with OpenClaw?"
- "I want to use multiple agents to..."
- "Create an automated pipeline for..."
- "Design a workflow that..."

## Core Workflow

### Step 1: Analyze the Process

Before automating, understand what needs automation:

1. **Map the current process**
   - What are the input and output?
   - What steps are currently manual?
   - What decisions require human judgment?
   - What tools/platforms are involved?

2. **Identify automation candidates**
   - Repetitive tasks (daily/weekly)
   - Rule-based decisions
   - Data transformation steps
   - Multi-platform sync needs

3. **Assess complexity**
   - Simple: Single tool, straightforward logic
   - Medium: Multiple tools, conditional branching
   - Complex: Multi-agent coordination, state management

### Step 2: Design the Workflow

Match complexity to the right approach:

| Complexity | Approach | Tools |
|------------|----------|-------|
| Simple | Single skill + cron | OpenClaw + cron skill |
| Medium | Multi-skill pipeline | agent-orchestrator + automation-workflows |
| Complex | Multi-agent system | autonomous-tasks + proactive-agent |

**Design principles:**
- Start small, iterate
- Each step should have clear input/output
- Include error handling and retries
- Log everything for debugging

### Step 3: Select Skills

Browse the skill ecosystem for relevant tools:

**Content Automation:**
- `content-repurposer` - Transform content across formats
- `twitter-autopilot` - Social media automation
- `newsletter-generator` - Email newsletter creation

**Data Processing:**
- `xlsx` / `xlsx-cn` - Spreadsheet manipulation
- `pdf` / `nano-pdf` - PDF operations
- `docx` / `docx-cn` - Word document handling

**Agent Orchestration:**
- `autonomous-tasks` - Self-driven task execution
- `agent-orchestrator` - Multi-agent coordination
- `proactive-agent` - Anticipatory actions

**API Integration:**
- `api-gateway` - 100+ API connections (OAuth managed)
- `brave-search` / `online-search` - Web search
- `tencent-docs` - Tencent Docs integration

### Step 4: Implement

**Pattern 1: Simple Cron Job**
```yaml
# Use OpenClaw cron skill
schedule: "0 9 * * *"  # Daily at 9am
task: "Check emails and summarize important ones"

_meta.json

{
  "ownerId": "kn71e5h8bg6mhk7gnmre2v00w583mgm1",
  "slug": "ai-workflow-automation",
  "version": "1.0.0",
  "publishedAt": 1774580870645
}

references/cron-patterns.md

# Cron Scheduling Patterns

Common scheduling patterns for AI workflow automation.

## Basic Patterns

| Pattern | Schedule | Description |
|---------|----------|-------------|
| Every hour | `0 * * * *` | Hourly execution |
| Every day at 9am | `0 9 * * *` | Daily morning |
| Every Monday 9am | `0 9 * * 1` | Weekly start |
| First of month | `0 9 1 * *` | Monthly report |
| Every 6 hours | `0 */6 * * *` | Periodic check |

## Use Case Patterns

### Content Distribution
```yaml
schedule: "0 10,14,18 * * *"  # 10am, 2pm, 6pm
timezone: "Asia/Shanghai"
task: "Publish scheduled content"
```

### Data Sync
```yaml
schedule: "*/30 * * * *"  # Every 30 minutes
task: "Sync data from API"
```

### Report Generation
```yaml
schedule: "0 18 * * 5"  # Friday 6pm
task: "Generate weekly report"
```

### Monitoring
```yaml
schedule: "*/15 * * * *"  # Every 15 minutes
task: "Check service health"
```

## OpenClaw Cron Syntax

Use the `cron` skill to create scheduled tasks:

```json
{
  "name": "daily-email-check",
  "schedule": { "kind": "cron", "expr": "0 9 * * *" },
  "payload": { "kind": "agentTurn", "message": "Check inbox and summarize" },
  "sessionTarget": "isolated"
}
```

## Timezone Considerations

- Default timezone: UTC
- Specify timezone in schedule: `{"kind": "cron", "expr": "0 9 * * *", "tz": "Asia/Shanghai"}`
- Common timezones:
  - `Asia/Shanghai` (Beijing, +8)
  - `America/New_York` (EST, -5)
  - `Europe/London` (GMT)

references/multi-agent-patterns.md

# Multi-Agent Patterns

Patterns for coordinating multiple AI agents.

## Pattern 1: Pipeline (Sequential)

Agents execute in order, each passing output to the next.

```
[Agent A] → [Agent B] → [Agent C]
   ↓            ↓            ↓
 Research → Writing → Publishing
```

Use when:
- Clear sequential dependency
- Each step transforms data
- No parallelization needed

Implementation: `agent-orchestrator` with sequential mode

## Pattern 2: Parallel (Concurrent)

Multiple agents work independently on the same input.

```
         → [Agent A] →
[Input] → [Agent B] → [Aggregator] → Output
         → [Agent C] →
```

Use when:
- Multiple perspectives needed
- Tasks don't depend on each other
- Speed is priority

Implementation: `agent-orchestrator` with parallel mode

## Pattern 3: Coordinator-Worker

One coordinator delegates tasks to specialized workers.

```
           → [Worker A: Research]
[Coordinator] → [Worker B: Writing] → [Coordinator] → Output
           → [Worker C: Review]
```

Use when:
- Dynamic task allocation
- Need centralized control
- Tasks vary by input

Implementation: `autonomous-tasks` + `agent-orchestrator`

## Pattern 4: Producer-Consumer

One agent produces work items, others consume them.

```
[Producer] → Queue → [Consumer A]
                   → [Consumer B]
                   → [Consumer C]
```

Use when:
- Continuous work stream
- Load balancing needed
- Variable processing time

Implementation: Use file-based queue or database

## Communication Patterns

### File-Based
```yaml
# Producer writes to file
output: "shared/tasks/inbox.json"

# Consumer reads from file
input: "shared/tasks/inbox.json"
```

### Memory-Based
```yaml
# Use OpenClaw memory system
memory_set: "shared/tasks/current"
memory_get: "shared/tasks/current"
```

### Message-Based
```yaml
# Use sessions_send for inter-agent messaging
target_session: "worker-agent"
message: "{\"task\": \"process\", \"data\": ...}"
```

## Error Handling

1. **Retry with backoff** - Transient failures
2. **Dead letter queue** - Failed messages
3. **Circuit breaker** - Prevent cascade failures
4. **Timeout + fallback** - Don't hang forever

## State Management

- Use `memory/` directory for persistent state
- Use `MEMORY.md` for long-term knowledge
- Use `memory/YYYY-MM-DD.md` for daily logs
- Consider `shared-memory` skill for multi-agent shared state

skill-card.md

## Description:

AI Workflow Automation Expert helps users automate repetitive tasks using AI agents, OpenClaw skills, and multi-agent orchestration.

This skill is ready for commercial/non-commercial use.

## Publisher:

[xiatian5](https://clawhub.ai/user/xiatian5)

### License/Terms of Use:

MIT-0

## Use Case:

Developers, operators, and teams use this skill to analyze manual processes, design AI-assisted workflows, select OpenClaw skills, and draft implementation patterns for scheduled jobs, pipelines, and multi-agent systems.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Automated workflows may send messages, post content, answer customers, or change accounts without enough oversight.

Mitigation: Require human approval for outbound email, social posting, customer responses, financial changes, and account changes.

Risk: Workflow integrations may expose sensitive data or overbroad credentials to unnecessary tools.

Mitigation: Use least-privilege credentials and avoid sending sensitive data into tools that do not need it.

Risk: Scheduled workflows can fail silently or produce changes that are hard to unwind.

Mitigation: Keep logs, monitor failures, and define rollback steps before enabling recurring automation.

## Reference(s):

- [Cron Scheduling Patterns](references/cron-patterns.md)
- [Multi-Agent Patterns](references/multi-agent-patterns.md)

## Skill Output:

**Output Type(s):** [guidance, markdown, code, configuration, shell commands]

**Output Format:** [Markdown with inline YAML, JSON, and shell command examples]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May include workflow designs, skill recommendations, schedules, pipeline templates, and implementation checklists.]

## Skill Version(s):

1.0.0 (source: server release metadata)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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