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Zhixu, AI system architecture

LEARN BY DOING

RPA process blueprint: design for failure first, then discuss automation

Break processes into deterministic, assisted, and fully manual steps; define states, idempotency, authoritative readback, failure paths, and takeover materials; stop immediately at CAPTCHAs - do not auto-recognize, bypass, or retry.

Look at the input first, then see what can be delivered.

The following is a fictional teaching example, not a client testimonial. Templates, full samples, and verification steps are all included in the download pack.

Sample Input

The fictional Starlink ticket triage includes 50 synthetic cases: 5 purely manual, 45 automation-eligible; of those, 36 direct pass, 6 transferred to human, 3 technical failures.

Expected Output

Direct pass rate 80.0% (36/45), technical failure rate 6.7% (3/45); does not meet the project threshold of at least 90% direct pass and no more than 2% failure, so not going live. Stop on CAPTCHA detection, no automatic retry.

What you'll take away

Create a process state diagram and success evidence that business, technical, and risk teams can all review; design explicit failure paths for duplicates, timeouts, permissions, partial success, and CAPTCHAs; use direct pass rate, failure rate, and human takeover rate to determine go-live readiness

Lixing, FDE Pre-Deployment Engineering

LEARN BY DOING

Follow the steps and complete your first deliverable.

First validate results with the sample pack, then copy the blank template to process your own anonymized data.

DISCOVER

Restore business control objectives

Record triggers, inputs, systems, roles, outputs, and authoritative evidence, avoiding mistaking current click sequences for real requirements.

Deliverables
Current process and control objective diagram
Confirmation point
Business owner confirms definitions of success and failure

LEARN BY DOING

Download contents and version notes

A document-type skill is not the same as deployed software. Models, tools, and real business systems require separate configuration; static review does not guarantee absolute safety of any future version or runtime environment.

Not sure where to start? Copy a learning request.

You can download this reviewed version's resource package. If it includes SKILL.md, provide it and the related documentation to your AI tool; first read and check the materials and usage boundaries, and do not run anything automatically.

  1. Prepare assets: start with fictional or de-identified examples.
  2. Read the boundaries: confirm dependencies, inputs, outputs, and human checkpoints.
  3. Review outputs: keep source and failure records before deciding to pilot.

You can also select the text to copy directly; read it first before executing.

This version has been reviewed.1.0.0

FORMWEFT independently authored Chinese methods, templates, and fictional samples; Feishu original package not redistributed. Current fixed version has completed file structure, static rules, and content review, no high-risk behavior found. Real data permissions and generated results still need user review.

Maintainer
FORMWEFT
License
MIT
Source type
Clean-room original rebuild
Network permission
Offline by default
Local programs
None; documents, templates, and samples
Review date
2026/09/05

Review scope

Fixed version
true
Static rule results
eligible
File count
6
Packaging script
0
Dependencies and install hooks
0
Run online
Excluded
Content review
Done
Review scope
Hash only; does not guarantee safe execution
SHA-256 · current download package7327c789172552679238b42352d106b467bc8c82c407559703ab94d0d873a679

SuGuang, Silicon-Based Co-Creation Guide

Integrate this method into your business.

Enterprise RPA, AI workflow design, failure governance, and human takeover system