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Ai Workflow Architect

skill-jpeslar1-john-peslar-ai-skills-ai-workflow-architect · by jpeslar1

Design practical AI workflows, agents, automations, SOPs, and human approval loops for business operations. Use when turning a manual process into an AI-assisted workflow, mapping tools and data sources, deciding what to automate versus keep human, creating an implementation plan, or auditing an existing process for AI leverage. Trigger on "automate this", "AI workflow", "AI agent", "SOP", "manua…

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Install

$ agentstack add skill-jpeslar1-john-peslar-ai-skills-ai-workflow-architect

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

AI Workflow Architect

Use this skill to turn messy business operations into practical AI workflows. Optimize for reliable execution, clear handoffs, and fast implementation, not impressive diagrams.

Read references/workflow-map.md when the user wants a full workflow map, SOP, or implementation spec.

Core Principle

AI automation works when the workflow is explicit: clear trigger, clean inputs, defined decisions, bounded tool access, human approval points, and measurable output quality.

Workflow

  1. Map the current process
  • Trigger
  • Inputs
  • Tools
  • Human decisions
  • Outputs
  • Failure points
  • Time spent
  1. Classify each step
  • Automate: repetitive, rule-based, low-risk
  • Assist: judgment-heavy but draftable
  • Human approval: customer-facing, financial, legal, reputational, or irreversible
  • Do not automate: unclear, high-risk, or low-volume
  1. Design the AI workflow
  • Agent role
  • Data sources
  • Tool permissions
  • Prompts or skills needed
  • Memory or knowledge base needed
  • Human review gates
  • Logging and audit trail
  1. Write the implementation plan
  • Week 1: manual-assisted version
  • Week 2: partial automation
  • Week 3+: deeper integration only after quality proof

Output Format

**Current Workflow**
[summary]

**Automation Map**
| Step | Current Owner | AI Role | Human Gate | Risk |
|---|---|---|---|---|

**Recommended Agent Design**
- Trigger:
- Inputs:
- Tools:
- Knowledge:
- Output:
- Approval:
- Logs:

**Implementation Plan**
1. ...
2. ...
3. ...

**Risks**
- ...

**First Test**
[smallest useful workflow to run this week]

Rules

  • Start with the smallest workflow that proves value.
  • Never remove human approval from high-risk steps until quality is measured.
  • Prefer plain files, tables, forms, and checklists before complex infrastructure.
  • Name what can break.
  • Include observability: logs, status, error handling, and review queues.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.