MCPInstall

ae-dataops

Use ae-cli for DataOps warehouse tables/views and fields, recycling, workflow configuration and release, execution troubleshooting, backfill, SQL queries, and data integration.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/thinkingaiagenticengine/ae-cli --skill ae-dataops
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads ae-dataops automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

ae-dataops

Cross-skill collaboration

When remaining work is outside this skill's scope, or a necessary prerequisite needs another capability, follow the collaboration protocol. Choose from the skills available in this run by capability, preserve verified context, and continue the remaining task. Reuse this protocol if already loaded.

CRITICAL - This skill is self-contained. Use the Global AE CLI Rules below; do not require a separate shared skill for DataOps-side tasks.

Start with the user's intended outcome, then read only the matching reference below. These are files in this skill, not separately installed skills. Use the installed command's --help for exact flags; if it differs from this reference, report the version mismatch instead of inventing aliases.


Global AE CLI Rules

Use ae-cli and these references for DataOps tasks. Preserve an explicitly selected host and space across a workflow.

Global parameters:

ParameterDescription
--format <json|table>Output format. Default is JSON.
--jq <expr>jq over the command payload before wrapping; use .flowInstance.status, not .data.flowInstance.status. External jq over default CLI JSON includes .data.
--host <url>Override the active AE host. Available on every command and may be placed after the subcommand, e.g. ae-cli dataops_ide +<command> --host <url>.

Output and errors:

  • JSON results use an outer ok and data; command errors use ok:false with error.type/message/hint on stderr and exit non-zero. Inspect both streams.
  • ok:true is not a business completion check. Legacy commands can return REJECTED, FAILED, or CHECK_FAILED inside data. Check the specific result fields described by the reference before proceeding. Reading a failed task's status successfully is different from successfully executing that task.
  • For asynchronous work, preserve the returned ID and query that same operation. Submission is not completion; do not submit again because it is still running. Use bounded polling with backoff; when the wait budget is exhausted, report the ID and current state rather than claiming success or restarting work.
  • For an actual _notice.host_compat or explicit version-compatibility warning, briefly report it and quote the supplied upgrade commands accurately. Unrelated stderr output is not a version warning.

Preview semantics:

  • Table-field mutations and entity recycle/delete use server semantic --dry-run; inspect the returned blockers and planned changes.
  • Other current DataOps --dry-run implementations show a local request only. They do not validate server permissions, connections, SQL, or business state. For flow release differences use +preview_release_flow.
  • Current DataOps commands do not implement the global --validate server check. Do not treat its informational response as successful validation.

Safety constraints:

  • Read commands can execute directly after required IDs and references are verified.
  • Ordinary write commands execute without --yes; use --yes only for a high-risk-write command after explicit user confirmation.
  • Reuse trusted IDs and context already provided; otherwise discover the exact resource using the matching reference. Refine searches or paginate when truncated; an incomplete list cannot prove absence. Ask only when the target or a consequential choice remains ambiguous.
  • For existing resources, never invent IDs, fields, or parameter values. For creation, a new name is expected: use the requested name or propose one, check collisions, and do not mistake its absence for a blocker.
  • Stop at the requested outcome: saving DEV does not authorize release, release does not authorize a manual run, and recycling does not authorize permanent deletion. Do not substitute a broader operation for an unsupported narrower request.

Domains for DataOps: dataops_repo, dataops_datatable, dataops_flow, dataops_operations, dataops_ide, dataops_integration


Core Concepts and Rules

You must understand the following key concepts before use, otherwise errors are highly likely.

Environment and Defaults

ScenarioDefault EnvironmentDescription
Table creation/field edits and workflow definitionsDEVRelease separately when requested
dataops_datatable +entity_recycle / +recycle_bin_deleteExisting DEV and PRODUCT mappingsEntity lifecycle operations; not DEV-only edits awaiting release
Manual flow execution, operations and backfillPRODInspect the exact flow/job/instance
SQL queryDetermined by the SQL's discovered tablesDo not choose DEV or PROD solely from a sample query

Responsibility Boundaries

OperationCorrect ToolProhibited
Execute SELECT queriesdataops_ide—
Create tables/views or mutate table fieldsdataops_datatabledataops_ide
Recycle or permanently delete a table/viewdataops_datatable +entity_recycle / +recycle_bin_deletedataops_ide

Flow Lifecycle

Create DEV Flow → Configure Parameters/Tasks/Dependencies/Schedule → Preview and Release to PROD when requested → Manual Execution / Operations Troubleshooting when requested

Custom flow parameters: use +get_flow_params, +create_flow_param, +update_flow_param, and +delete_flow_param in dataops_flow; see the flow reference. Writes configure DEV definitions and may stop DEV debug executions. Value, type, name, and deletion changes require release for PROD; a remark update without renaming also synchronizes an existing PROD parameter remark.

Backfill lifecycle: Discover eligible PROD flow → Create or fully update DRAFT job → Run explicitly → Search / inspect plans → Stop or rerun the complete job; delete only after target inspection


Scenario Routing

User intentCommand groupReferenceCompletion boundary
Discover or select a spacedataops_repoSpace discoveryResolve the intended spaceCode; reuse trusted context
Create tables/views or change a field type/commentdataops_datatableTable workflowsVerify DEV state; publish only when requested
Delete a table/view; inspect or empty one recycle-bin entrydataops_datatableEntity lifecycleOrdinary deletion stops at recycling; permanent deletion needs separate authorization
Create/configure a flow; add/update/delete tasks and parameters; schedule or releasedataops_flowFlow configurationRead back the requested DEV or PROD definition; do not add a manual run
Execute, inspect, or stop a run; troubleshoot failed workdataops_flow, dataops_operationsExecution and monitoringMatch the exact instance; whole-flow execution is not failed-node retry
Fill multiple historical business datesdataops_operationsBackfill jobsDraft creation, job start, and completed plans are different outcomes
Create/test a datasource or configure/run a syncdataops_integrationData integrationVerify saved config or the exact run, according to the request
Schedule a sync and publish it without running nowdataops_integration, dataops_flowData integration, then Flow configurationConfirm the PROD node and schedule; no manual execution
Browse schemas/tables or answer a data questiondataops_datatable, dataops_ideQuery workflowRead the actual result and answer the question, not merely return a download task ID

1. Space Discovery

dataops_repo exposes only one read command. Use it to discover a valid spaceCode before calling DataOps commands that require one. It returns createTime, spaceCode, and spaceDisplayName.

  • If the user already provided a trusted spaceCode, reuse it.
  • If spaceCode is unknown, run +list_spaces first.
  • If exactly one space is returned, use its spaceCode.
  • If multiple spaces are returned and the user intent does not identify one, ask the user which space to use. Do not guess.
# List spaces accessible to the current user
ae-cli dataops_repo +list_spaces

2. Data Table and View Management

Detailed workflow, command flags, examples, and parameter notes live in references/dataops-table.md.

Key constraints:

  • Start with dataops_datatable +dict_search_tables for visible DataOps catalog discovery.
  • Use dataops_ide +search_tables only for raw engine metadata, and dataops_ide +ide_list_tables only for known catalog/schema browsing.
  • Create tables/views with dataops_datatable, not dataops_ide; creation is DEV-only. Use +publish_entity when publication is requested.
  • Add, modify, or delete one ordinary table field at a time in DEV; preview first, never mutate partition fields, and publish PROD separately.
  • An ordinary "delete table/view" request means recycle only with dataops_datatable +entity_recycle. Permanent deletion is a separate workflow using +recycle_bin_list and +recycle_bin_delete; read the table reference before either workflow. Both writes affect DEV and PRODUCT and require an exact entity ID plus name, semantic --dry-run, and explicit confirmation before --yes. Never automatically delete an old same-name recycled entity to bypass RECYCLE_NAME_CONFLICT.
  • After confirmed actual recycling, the Agent may ask whether to permanently delete the entity, warning that this is irreversible and may delete internal table data. Stop until a new explicit confirmation. This is an Agent reply, not a CLI prompt or JSON field. A preview, FAILED, PARTIAL, or unverified state must not trigger this offer; reconcile the same entity ID first.
  • DDL follows Trino syntax; current-space view DDL should keep the literal ${env} placeholder.

3. Flow Orchestration

Flow references cover creation and configuration, execution and monitoring, and backfill jobs.

Lifecycle: DEV configuration → Requested release → Requested execution or troubleshooting. Stop at the user's boundary.

Detailed creation/configuration commands live in references/dataops-flow-create.md. Detailed execution, monitoring, operation instance, task log, and stop commands live in references/dataops-flow-monitor.md. Persistent multi-date backfill jobs live in references/dataops-backfill.md.

Key constraints:

  • Create and update tasks in DEV, preview/release before PROD execution.
  • Use +get_flow_overview for custom flowParams and each task's dag.tasks[].taskParams; DEV is the default, PROD is supported, and both require dwWorkflowEdit. Definitions preserve expression text and code-parsed references whose source/value fields are null or omitted.
  • Instance details require dwOMInstanceView; inspect historical values in flowInstance.instanceParamMap or task log.params with --includeLog true.
  • Treat +delete_task as high-risk: verify the target with +get_flow_overview, preview with --dry-run, and use --yes only after explicit user confirmation. Deletion affects DEV; release the flow to apply it to PROD.
  • +execute_flow always runs PROD; it returns executeId for early stop.
  • Prefer flowInstanceId from operations search for stable inspection and troubleshooting.
  • A backfill job is persistent and batches multiple base dates; do not emulate it by looping +execute_flow.
  • Create a draft with +create_backfill_job and run it as a separate step. +rerun_backfill_job reruns the complete job, not only failed plans.
  • +update_backfill_job replaces a DRAFT job's complete configuration; inspect the job first and do not treat it as a partial patch. Treat +delete_backfill_job as high-risk and preview it with --dry-run before confirmation.
  • Reference flow or workspace parameters in task SQL as ${paramKey}. Inspect parameter sources in the overview before release.

4. IDE SQL Queries

Detailed metadata browsing, SQL query, async download, and cancel workflows live in references/dataops-query.md.

Key constraints:

  • IDE is query-only; create/modify tables and recycle/delete entities with dataops_datatable.
  • Prefer dataops_datatable +dict_search_tables for table discovery unless raw engine metadata or schema browsing is required.
  • Submit exactly one read-only SQL query. It creates a platform-bounded download task; rows are not returned inline and the result is not an unlimited or full export.

5. Data Integration

Detailed datasource, metadata browsing, sync solution, execution, and monitoring workflows live in references/dataops-integration.md.

Key constraints:

  • Generate sourceConfig, sinkConfig, channelConfig, and fieldsMapping from the reference templates; do not invent keys.
  • MySQL Source read partitioning uses sourceConfig.splitColumn; fieldsMapping.shardingKey is column metadata and must not be used for it.
  • +save_sync_solution is not a partial patch. Current +get_sync_detail --withParams true returns a summary plus used parameters, not complete editable configs. Do not reconstruct missing settings from it; see the integration reference's update boundary. syncName is accepted for compatibility but ignored.
  • Preset repository sync uses te_etl@TASK_ENGINE_TRINO; the server supplies gateway routing from the selected space. Do not copy sample company or space IDs into configuration.
  • Use +list_sync_runs to get taskId before stopping a running sync.

Reference Documentation

For detailed command flags and usage, please refer to the command documentation in the references/ directory.

FAQ

What does the ae-dataops skill do?

Use ae-cli for DataOps warehouse tables/views and fields, recycling, workflow configuration and release, execution troubleshooting, backfill, SQL queries, and data integration.

How do I install ae-dataops?

Run npx skills add https://github.com/thinkingaiagenticengine/ae-cli --skill ae-dataops in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ae-dataops/ (personal) or .claude/skills/ae-dataops/ (project).

Which AI agents can use ae-dataops?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is ae-dataops safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains ae-dataops?

It is published by thinkingaiagenticengine in thinkingaiagenticengine/ae-cli. It has 870 recorded installs.

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