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Cloud Database

Store data in the cloud, shared across devices

Move your local project data to the cloud: CRUD, filtered queries, and one shared dataset across people and devices. No database to install or server to configure — IFQ Cloud hosts it all.

What this module can do

CRUD read/writeSync local data to the cloudFiltered queries & paginationShared data across devices/people
Scenario prompts

Cloud Database

Pick the scenario closest to your need and copy the prompt in one click.

CRUD read/write

Store your data in the cloud and create, update, delete, and read anytime.

The problem today

Data lives in local files — gone on another computer, impossible to share.

What you get

  • Data in the cloud, available on any device
  • Full create/read/update/delete
  • Save = sync, no manual export
For：Any tool that needs to save and share data

Copy the whole block below and paste it into Codex. Replace placeholders like API keys with real values from the IFQ Cloud console.

You are a senior full-stack engineer. In my currently open local project, migrate the project's data storage to a "cloud database" with full CRUD.

[Features to build]
- Identify existing data (lists, form records) and design matching cloud tables.
- Implement create, read, update, delete and wire them to the existing UI.
- Write to the cloud on save, read from the cloud on load, refresh the UI immediately after each action.
- Keep a local cache for offline reads; auto-sync when the network returns.

[Connect to IFQ Cloud — important]
- Integrate through IFQ Cloud. API base URL is https://api.cloud.ifq.ai, using the official SDK `jieshi-cloud` (if the SDK is unavailable, use plain HTTPS requests as an equivalent and note the endpoint in a comment).
- Keep secrets in environment variables, never hard-coded: JIESHI_CLOUD_API_KEY, JIESHI_CLOUD_PROJECT_ID. Generate a `.env.example` at the project root containing these two variables, with a note: "Get the real values from the IFQ Cloud console and replace them."
- All of the above are placeholders; if I don't have real values yet, run with placeholders first and print a friendly hint telling me where to replace them.

[Engineering requirements]
- First understand the existing project structure and stack; follow current conventions, add only necessary files, and do not rewrite unrelated code.
- Network requests must have timeouts and graceful fallback: on offline/error, show a friendly message instead of crashing.
- Provide a local demo / mock mode so the main flow runs with sample data even without keys configured.
- Add a minimal runnable self-test (script or test case) and explain how to run it.
- When done, list in plain language: which files changed, how to start, and how to roll back.

Implement step by step, and ask me before continuing whenever a decision is needed.

One-click local data to cloud

Bulk-import existing local files / spreadsheets into the cloud, migrate smoothly.

The problem today

You have lots of local data and want the cloud, but fear a messy, lossy migration.

What you get

  • One-click bulk import of historical data
  • Auto-backup before migration, rollback ready
  • Local and cloud consistent after migration
For：Teams with historical data who want a smooth move to the cloud

Copy the whole block below and paste it into Codex. Replace placeholders like API keys with real values from the IFQ Cloud console.

You are a senior full-stack engineer. In my currently open local project, add "one-click local data to cloud" to safely migrate existing data into the cloud database.

[Features to build]
- Scan local data sources (files, spreadsheets, local storage) and list what will migrate for my confirmation.
- Auto-create a local backup before migration and tell me where it is, ensuring rollback.
- Import to the cloud in batches with progress; list failed items separately for retry.
- After migration, run a consistency check on counts and key fields, and produce a report.

[Connect to IFQ Cloud — important]
- Integrate through IFQ Cloud. API base URL is https://api.cloud.ifq.ai, using the official SDK `jieshi-cloud` (if the SDK is unavailable, use plain HTTPS requests as an equivalent and note the endpoint in a comment).
- Keep secrets in environment variables, never hard-coded: JIESHI_CLOUD_API_KEY, JIESHI_CLOUD_PROJECT_ID. Generate a `.env.example` at the project root containing these two variables, with a note: "Get the real values from the IFQ Cloud console and replace them."
- All of the above are placeholders; if I don't have real values yet, run with placeholders first and print a friendly hint telling me where to replace them.

[Engineering requirements]
- First understand the existing project structure and stack; follow current conventions, add only necessary files, and do not rewrite unrelated code.
- Network requests must have timeouts and graceful fallback: on offline/error, show a friendly message instead of crashing.
- Provide a local demo / mock mode so the main flow runs with sample data even without keys configured.
- Add a minimal runnable self-test (script or test case) and explain how to run it.
- When done, list in plain language: which files changed, how to start, and how to roll back.

Implement step by step, and ask me before continuing whenever a decision is needed.

Filtered queries & pagination

Filter data fast by keyword, date, status, etc., with paginated results.

The problem today

As data grows, finding one record takes ages and loading the whole list lags.

What you get

  • Combined multi-condition filters
  • Keyword search
  • Pagination / infinite scroll for smooth lists
For：Data-heavy management tools that need retrieval

Copy the whole block below and paste it into Codex. Replace placeholders like API keys with real values from the IFQ Cloud console.

You are a senior full-stack engineer. In my currently open local project, add "filtered queries and pagination" to the project's data lists so users find records fast.

[Features to build]
- Add a filter bar above the list: keyword search + common conditions (date range, status, category).
- Run filtering in the cloud, returning only matches, avoiding pulling all data locally.
- Paginate or infinite-scroll results, showing total count and current range.
- Persist filter conditions (in URL or locally) so they survive refresh.

[Connect to IFQ Cloud — important]
- Integrate through IFQ Cloud. API base URL is https://api.cloud.ifq.ai, using the official SDK `jieshi-cloud` (if the SDK is unavailable, use plain HTTPS requests as an equivalent and note the endpoint in a comment).
- Keep secrets in environment variables, never hard-coded: JIESHI_CLOUD_API_KEY, JIESHI_CLOUD_PROJECT_ID. Generate a `.env.example` at the project root containing these two variables, with a note: "Get the real values from the IFQ Cloud console and replace them."
- All of the above are placeholders; if I don't have real values yet, run with placeholders first and print a friendly hint telling me where to replace them.

[Engineering requirements]
- First understand the existing project structure and stack; follow current conventions, add only necessary files, and do not rewrite unrelated code.
- Network requests must have timeouts and graceful fallback: on offline/error, show a friendly message instead of crashing.
- Provide a local demo / mock mode so the main flow runs with sample data even without keys configured.
- Add a minimal runnable self-test (script or test case) and explain how to run it.
- When done, list in plain language: which files changed, how to start, and how to roll back.

Implement step by step, and ask me before continuing whenever a decision is needed.