Cloud Database

Move data to the cloud and share it across devices

Put project data in a cloud database: CRUD, filtered queries, and one shared dataset across people and devices. No database to install or server to configure — IFQ Cloud hosts the underlying connection and permissions.

What this cloud module can do

Cloud CRUD read/writeSmooth local-to-cloud migrationCloud queries & paginationShared data across devices/people

Scenario prompts

Cloud Database

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

Cloud 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 layer 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]
- API base https://api.cloud.ifq.ai, official SDK `jieshi-cloud` (if unavailable, use plain HTTPS equivalently and note the endpoint in a comment).
- Secrets via environment variables, never hard-coded: JIESHI_CLOUD_API_KEY, JIESHI_CLOUD_PROJECT_ID; generate `.env.example` with both, noting "get real values from the IFQ Cloud console and replace".
- All placeholders; without real values, run on placeholders and print a hint on where to replace them.

[Engineering requirements]
- Read the existing structure and stack first; follow current conventions, add only necessary files, leave unrelated code untouched.
- Network calls: timeout + graceful fallback; friendly errors, never crash.
- Demo / mock mode: the main flow runs on sample data without keys.
- Include a minimal runnable self-test with run instructions.
- On completion list: files changed / how to start / how to roll back.

Ask before any decision point.

*— [ifq.ai](https://work.ifq.ai/) · AI-augmented. Full-stack crafted*

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]
- API base https://api.cloud.ifq.ai, official SDK `jieshi-cloud` (if unavailable, use plain HTTPS equivalently and note the endpoint in a comment).
- Secrets via environment variables, never hard-coded: JIESHI_CLOUD_API_KEY, JIESHI_CLOUD_PROJECT_ID; generate `.env.example` with both, noting "get real values from the IFQ Cloud console and replace".
- All placeholders; without real values, run on placeholders and print a hint on where to replace them.

[Engineering requirements]
- Read the existing structure and stack first; follow current conventions, add only necessary files, leave unrelated code untouched.
- Network calls: timeout + graceful fallback; friendly errors, never crash.
- Demo / mock mode: the main flow runs on sample data without keys.
- Include a minimal runnable self-test with run instructions.
- On completion list: files changed / how to start / how to roll back.

Ask before any decision point.

*— [ifq.ai](https://work.ifq.ai/) · AI-augmented. Full-stack crafted*

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]
- API base https://api.cloud.ifq.ai, official SDK `jieshi-cloud` (if unavailable, use plain HTTPS equivalently and note the endpoint in a comment).
- Secrets via environment variables, never hard-coded: JIESHI_CLOUD_API_KEY, JIESHI_CLOUD_PROJECT_ID; generate `.env.example` with both, noting "get real values from the IFQ Cloud console and replace".
- All placeholders; without real values, run on placeholders and print a hint on where to replace them.

[Engineering requirements]
- Read the existing structure and stack first; follow current conventions, add only necessary files, leave unrelated code untouched.
- Network calls: timeout + graceful fallback; friendly errors, never crash.
- Demo / mock mode: the main flow runs on sample data without keys.
- Include a minimal runnable self-test with run instructions.
- On completion list: files changed / how to start / how to roll back.

Ask before any decision point.

*— [ifq.ai](https://work.ifq.ai/) · AI-augmented. Full-stack crafted*