SOP Standardized Process Extractor

Problem Background

In team operations, operational experiences are often documented in informal ways—such as oral descriptions, chat logs, or fragmented notes—without standardized formats. This leads to high training costs for newcomers, frequent oversight in execution steps, and difficulty in quality assessment. Manually converting these experiences into SOP documents is time-consuming and labor-intensive, typically taking hours and resulting in incomplete step coverage.

Core Capabilities and Workflow

The SOP Standardized Process Extractor automates this process with the following key steps:

• Intelligent Parsing: The sop_extractor.py script analyzes input text to identify step boundaries, operation types, decision points, and checkpoints, transforming unstructured data into structured formats.
• Quality Assessment: Using sop_optimizer.py, it performs a 7-dimension percentage scoring, covering completeness, clarity, operability, safety, measurability, efficiency, and maintainability, and generates a quality report.
• Gap Analysis: Automatically detects missing prerequisites, checkpoints, exception handling, and provides improvement suggestions to enhance process robustness.
• Multi-format Output: The sop_formatter.py script outputs interactive HTML, Markdown, Mermaid flowcharts, execution checklists, and training cards, tailored to various use cases. The entire workflow can be executed via run_all.py for automation or run step-by-step for debugging.

Applicable Scenarios and Limitations

Well-suited for:

• Linear operational processes, such as customer service handling, content publishing, or software deployment.
• Personal or small team SOPs with ≤ 20 steps, suitable for operation norms executed by 1-3 people.
• Oral descriptions, like transcribed recordings, chat records, or casually noted process instructions.
• Structured enhancement of existing documents, upgrading inconsistent old docs to standardized formats.

Not ideal for:

• Very long processes (100+ steps), which should be broken into sub-processes for separate extraction first.
• Non-operational discussions, where concrete actions need to be extracted beforehand from conversations.
• Documents with existing formatting standards, such as Word/Excel-based SOPs; direct revision is more efficient.

Input quality affects output: high-quality inputs (≥100 words with clear steps) yield complete SOPs directly, while short or messy inputs trigger degradation with output frameworks and prompts for additional details.