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Custom prompt shortcut / Software Development

/loganalysis — ChatGPT Prompt Guide

Explore the /loganalysis custom ChatGPT prompt shortcut with a practical template, examples, and review steps. Learn how to refine outputs for your workflow.

What does /loganalysis do?

Purpose: Diagnose failed checkout requests using sanitized web-server logs.

This is a custom prompt convention, not a built-in or official ChatGPT slash command. Entering the slash text alone does not guarantee that ChatGPT will invoke a special tool. Use the complete structured prompt below or configure your own app to interpret this shortcut.

Key decision for this workflow

Count 502s by endpoint and five-minute bucket; compare /checkout against /health; correlate the first spike with deploy and gateway traces.

Copy-ready task brief

/loganalysis Objective: Diagnose failed checkout requests using sanitized web-server logs. Inputs: Timestamped access records, deployment timestamps, and upstream error identifiers. Deliverable: A route-by-minute failure table, an incident timeline, and three testable hypotheses.

Reusable prompt template

/loganalysis Objective: Diagnose failed checkout requests using sanitized web-server logs. Available materials: Timestamped access records, deployment timestamps, and upstream error identifiers. Required output: A route-by-minute failure table, an incident timeline, and three testable hypotheses. Acceptance test: Count 502s by endpoint and five-minute bucket; compare /checkout against /health; correlate the first spike with deploy and gateway traces. Boundary: A 502 response identifies a failed gateway transaction, not the faulty component. Remove tokens, IP addresses, and customer data before sharing logs. Return the requested deliverable first, then a brief verification checklist and any unresolved questions.

How to use this shortcut

  1. Collect the source inputs: Timestamped access records, deployment timestamps, and upstream error identifiers.
  2. Run /loganalysis with the copy-ready task brief above.
  3. Request the concrete output: A route-by-minute failure table, an incident timeline, and three testable hypotheses.
  4. Check the result: Count 502s by endpoint and five-minute bucket; compare /checkout against /health; correlate the first spike with deploy and gateway traces.

Quality checks and limitations

Acceptance criteria: Count 502s by endpoint and five-minute bucket; compare /checkout against /health; correlate the first spike with deploy and gateway traces. Limitation: A 502 response identifies a failed gateway transaction, not the faulty component. Remove tokens, IP addresses, and customer data before sharing logs.

Related tools and learning

Continue with the 35 prompt generators, the technical AI Guides, and the Image Guides for deeper examples.

/code/debug/refactor/codeaudit/productrender/cinematic

Worked example: Diagnose failed checkout requests from a redacted web log

Illustrative output: Group HTTP 502 entries by route and minute; report that /checkout had 12 failures between 14:00 and 14:05, while /health remained 200.

Review and limitations

Do not infer the root cause from status codes alone; correlate upstream traces and deploy logs.

Field workflow: inputs, decisions, and validation

When to use this shortcut: Diagnose failed checkout requests using sanitized web-server logs.

What to provide: Timestamped access records, deployment timestamps, and upstream error identifiers.

What to request: A route-by-minute failure table, an incident timeline, and three testable hypotheses.

Practical method: Count 502s by endpoint and five-minute bucket; compare /checkout against /health; correlate the first spike with deploy and gateway traces.

Important limitation: A 502 response identifies a failed gateway transaction, not the faulty component. Remove tokens, IP addresses, and customer data before sharing logs.

Frequently asked questions

Is /loganalysis an official ChatGPT command? No. It is a reusable custom shorthand from this catalog. Does it work without a plugin? You can copy the full prompt into an assistant; automatic slash-command routing requires application logic. Can the output be trusted without review? No. Review accuracy, safety, and suitability before use.