Workflow Refactor
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Please install @user_e02e04b8/workflow-refactor according to https://skillhub.cn/install/skillhub.md.
About this skill
What It Solves
Many steps in traditional workflows do not come from the work itself, but from compensating human limits: task decomposition, intermediate documents, coordination meetings, and format approvals. Replacing one step with AI does not reduce process complexity. Workflow Refactor addresses workflows inflated by collaboration overhead: multi-role handoffs, document churn, high coordination time, and repeated rework. The goal is to rebuild the process around AI capability, not to automate each step in place.
How It Works
The skill provides a concrete refactoring method:
- Decompose: ask why each step exists and whether it is required by the task or by human limitations.
- Classify: label steps as
✅ core,🔶 calibration,❌ transfer,❌ coordination,⚡ validation, or❌ format. - Remove and keep: eliminate transfer, coordination, and format layers; trim validation to key nodes; keep outputs that act as correction anchors as in-primitive calibration points.
- Reassemble: organize retained steps into an
I/P/Oprimitive chain, usually with≤5primitives, and mark autonomy as⬛ full auto,🟨 semi-auto, or⬜ assist.
Refactoring is not finished by deleting documents. It requires seven checks: task completeness, compensation-layer removal, calibration preservation, end-to-end executability, complexity regression, quality preservation, and compliance retention. Human review must remain for legal, safety, and quality-critical nodes, and ⬛ full auto must not be applied to compliance-sensitive steps.
Boundaries and Caveats
It fits process refactoring for decomposable deliverables such as documents, proposals, designs, and code, especially when handoffs are ≥3, intermediate documents are ≥3, coordination consumes ≥30% of time, or rework is high. It does not promise automatic quality improvement: the process may become simpler, but quality preservation must be verified against actual outputs. If a team relies on an intermediate artifact for correction, keep it as a calibration point even when AI could produce the result in one step.
Use Cases
- Rebuild a multi-role product, engineering, QA, and ops delivery flow into executable AI-ready IPO primitives.
- Identify intermediate documents and coordination steps before launch, then remove transfer and format overhead.
- Design a delivery chain for proposals, designs, or code that keeps calibration points and uses no more than 5 primitives.
- Preserve compliance review nodes while labeling full-auto, semi-auto, and assist steps during workflow refactoring.
Best For
- Product owners responsible for multi-role delivery who want fewer intermediate documents and coordination steps.
- AI engineering leads who need to restructure design-to-code workflows while preserving human calibration points.
- Team leads who need to refactor requirement, development, and testing flows without removing compliance review.
- Domain experts who want one person plus AI to assemble complex deliverables while keeping critical decision gates.
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