What the data for this category looks like
This document is written based on FastGPT v4.17.0. Daily documents for level content creation include level design drafts, resource requirement sheets, test feedback forms, and version submission records. Data sources are level editors, art resource libraries, player test data, and defect systems. Data update cycles are mostly weekly or per version. Fields include level ID, resource path, test feedback entries, defect numbers, and others. Some fields are associated with specific map coordinates and resource file sizes. There is no unified standard format, so format alignment is required for data from different sources.
What constraints these characteristics impose on the "scheduled tasks and automation" workflow
Since data update cycles are weekly or per version, scheduled tasks should not use overly short trigger intervals to avoid repeatedly pulling redundant unupdated data. Non-standardized documents from multiple sources require automated workflows to first perform format verification and field alignment. Otherwise, the generated summary reports cannot be directly used for release review. Level data abnormalities will directly lead to level blockages or launch delays, so automated tasks must be configured with clear failure fallback and alert mechanisms, and cannot rely solely on single execution results. At the same time, task trigger timings must match version release rhythms to avoid performing data pull operations during version freeze periods.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Task trigger cycle | Every Monday 09:00 or within 1 hour after each version submission | Matches the weekly/per-version update rhythm of the business, avoids interfering with version release processes |
| Data pull scope | Last 7 days or current version dataset | Only retrieves the latest valid data, avoids redundant information occupying storage and computing resources |
| Report output format | Markdown + PDF dual format | Meets the dual needs of team shared documents and offline archiving, aligns with daily usage habits of R&D teams |
| Failure retry count | 3 times, retry interval 600 seconds | Addresses temporary fluctuations in data interfaces or network jitter, prevents task interruption from single execution failure |
| Exception alert threshold | Task failure rate ≥10% or empty rate of key fields ≥5% | Detects data pull or format processing abnormalities in a timely manner, avoids impacting subsequent release links |
Three common mistakes
- Phenomenon: Model access or task execution is automatically triggered outside the configured time, such as automatically calling the GPT-4-turbo model on the hour. Cause: The trigger rules for exclusive tasks are not correctly bound, and the global scheduled task configuration is mistakenly confused with the exclusive task for level content creation, leading to automatic operation of unrelated tasks.
- Phenomenon: Key fields are missing in the generated release notes and operations reports, such as empty level paths or resource requirement quantities. Cause: No associated verification logic for data pull is configured, and invalid exported data from the level editor is not filtered, leading to empty fields being mixed into the final report.
- Phenomenon: Automated tasks overwrite manually edited confirmed release notes. Cause: No version lock verification is configured before task execution, and team-reviewed and approved document content is directly overwritten, causing chaos in the release process.
How to confirm correct configuration
- Manually trigger a scheduled task, check whether the generated report includes the latest level design drafts, test feedback forms, and defect records for the current version.
- View task execution logs, confirm that trigger times match the configured cycle, with no abnormal errors or timeout records.
- Simulate a data abnormality scenario, such as deleting a valid test feedback form, check whether the system triggers the preset exception alert.
- Compare the manually compiled report of the same cycle with the automatically generated report, confirm field completeness and format consistency.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.