equipment-failure-rca-draft

equipment-failure-rca-draft Visual Quality Diagnostic Assistant

Quick Start

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Overview

The Visual Quality Diagnostic Assistant is used to answer "Is this image good enough to publish?", RCA, equipment failure, manufacturing, and is suitable for operations, managers, manufacturing, and RCA to invoke after clarifying business goals, content materials, or analysis targets. It combines inputs such as fault phenomenon descriptions, pasted fault timelines, phenomena, alarms, impact scope, temporary handling, and recovery status, organizes key context, and outputs summaries, diagnostic conclusions, action recommendations, and reusable deliverables for further execution, review, or delivery.

When to Use

Applicable Scenarios

• The user asks questions such as "Is this image good enough to publish?" and needs to quickly break down goals, determine priorities, and form executable results
• Operations, managers, manufacturing, and RCA need to generate summaries, diagnostic conclusions, action recommendations, and reusable deliverables around the Visual Quality Diagnostic Assistant
• The user has already prepared the RCA focus (select the direction that most needs priority handling in this equipment failure RCA draft.), equipment and production line background (explain equipment model, production line, process, shift, maintenance cycle, environment, and operating conditions.), and fault phenomenon description (paste fault timeline, phenomena, alarms, impact scope, temporary handling, and recovery status.), and hopes to organize them into executable analysis or optimization results
• The user needs to turn Visual Quality Diagnostic Assistant-related materials into clear conclusions, priorities, and next steps

How to Invoke

Call the AI Skills API directly through the exported Python runner:

Command Examples

Basic Invocation

python3 scripts/run.py --params '{}'

Invocation with Common Parameters

python3 scripts/run.py --params '{"rcaDraftFocus":"现象归纳"}'

Parameter Description

Parameter	Type	Required	Default	Description
rcaDraftFocus	string	No	现象归纳	Select the direction that most needs priority handling in this equipment failure RCA draft; optional values:现象归纳、可能原因、验证计划、纠正预防
equipmentContext	string	No	-	Explain equipment model, production line, process, shift, maintenance cycle, environment, and operating conditions
failureSymptomText	string	No	-	Paste fault timeline, phenomena, alarms, impact scope, temporary handling, and recovery status
maintenanceLogFile	string	No	-	Upload inspection sheets, alarm logs, maintenance records, photo descriptions, or downtime reports
downtimeImpactNotes	string	No	-	Supplement downtime duration, yield impact, spare parts, personnel, temporary controls, and hypotheses to be verified
equipmentRecordLink	string	No	-	Enter publicly accessible equipment descriptions, maintenance procedures, or fault record links; a complete accessible URL is required

See the complete machine-readable parameter structure at references/form-schema.json。

Parameter Value Reference

This skill currently has no categorical parameters that require an additional lookup table.

Supported Input Formats

This skill directly accepts JSON parameters; if a parameter contains a link field, pass a complete, accessible URL.

Example Request

The example parameters below can be passed directly to scripts/run.py, and the runner will send them to the AI Skills API.

python3 scripts/run.py --params '{"rcaDraftFocus":"现象归纳"}'

Equivalent --params JSON：

{
  "rcaDraftFocus": "现象归纳"
}

Example output

{
  "success": true,
  "data": {
    "message": "示例结果请以技能真实返回结构为准。"
  },
  "meta": {
    "executionTime": 842,
    "cached": false
  }
}

Deliverables

• Summary, diagnostic conclusions, action recommendations, and reusable deliverables: organize the core results around the user's goals into content that can be directly read, reviewed, or delivered.
• Interpretation of input materials: combine the RCA focus (choose the direction that this equipment failure RCA draft most needs to prioritize.), equipment and production line background (explain equipment model, production line, process, shift, maintenance cycle, environment, and operating conditions.), and fault symptom description (paste the fault timeline, symptoms, alarms, impact scope, temporary handling, and recovery situation.) to extract key context and basis for judgment.
• Next steps: provide priorities, execution recommendations, or a content framework that can be further processed.

Suggestions for using the results

• First determine whether the output answers the user's core question about the "Visual Quality Diagnostic Assistant".
• Then check whether the results cover the summary, diagnostic conclusions, action recommendations, and reusable deliverables, and whether clear next steps are provided.
• If there is little input material, it is recommended to have the user supplement the goals, audience, constraints, or original materials before running.

Preparation before running

• AISKILLS_BASE_URL: default https://ai-skills.ai
• AISKILLS_API_KEY: required, used for authenticated calls
• AISKILLS_TENANT_ID: default default