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alibabacloud-dataworks-semantic

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DataWorks Semantic Analysis

Use only Alibaba Cloud DataWorks public OpenAPI operations exposed by the aliyun dataworks-public plugin to create a reusable semantic analysis job, submit or stop a run, monitor execution, and retrieve generated result files.

Prerequisites

Perform these checks once, in this order. Install or update the public DataWorks plugin before probing any product API command; do not first probe every command, discover the plugin is missing, and then repeat the same probes.

aliyun version
aliyun plugin install --names dataworks-public
aliyun plugin update

Never pipe a remote installer into a shell. If the CLI is missing or older than 3.3.3, direct the user to the official Aliyun CLI installation documentation.

Check credentials without printing secrets:

aliyun configure list

The only allowed aliyun configure operation in this Skill is configure list. Never run aliyun configure set, including auto-plugin-install variants.

Observability

Session-id rules

  • Generation: Generate the session-id exactly once at the start of each user or agent session:
SEMANTIC_SESSION_ID="$(python3 -c 'import secrets; print(secrets.token_hex(16))')"
  • Format: The session-id must be exactly 32 lowercase hexadecimal characters.
  • Consistency across CLI/SDK/Terraform: Reuse the same session-id for every CLI command, SDK request, and Terraform operation in the session, including retries, polling, and downloads. Never regenerate it per command or when switching tools. Generate a new value only for a new user or agent session.

UA template declaration

Use this canonical CLI flag template:

--user-agent "AlibabaCloud-Agent-Skills/{SKILL_NAME}/{session-id}"

For this Skill, every Aliyun CLI product API command must explicitly include its concrete form:

--user-agent "AlibabaCloud-Agent-Skills/alibabacloud-dataworks-semantic/${SEMANTIC_SESSION_ID}"

Do not persist the User-Agent in Aliyun CLI configuration or an environment variable. Local management commands such as aliyun version, aliyun plugin ..., and aliyun configure list do not support the product API User-Agent flag; do not add it to them.

Public API readiness gate

After the prerequisite plugin install/update, confirm only the commands needed by the selected workflow before collecting parameters; do not probe unrelated operations or repeat a successful readiness check. For create, run, monitor, or download workflows, check the applicable commands from this public set:

aliyun dataworks-public create-semantic-job --help \
  --user-agent "AlibabaCloud-Agent-Skills/alibabacloud-dataworks-semantic/${SEMANTIC_SESSION_ID}"
aliyun dataworks-public run-semantic-job --help \
  --user-agent "AlibabaCloud-Agent-Skills/alibabacloud-dataworks-semantic/${SEMANTIC_SESSION_ID}"
aliyun dataworks-public list-semantic-job-runs --help \
  --user-agent "AlibabaCloud-Agent-Skills/alibabacloud-dataworks-semantic/${SEMANTIC_SESSION_ID}"
aliyun dataworks-public download-semantic-results --help \
  --user-agent "AlibabaCloud-Agent-Skills/alibabacloud-dataworks-semantic/${SEMANTIC_SESSION_ID}"

For an explicit stop workflow, confirm the stop and verification commands below. Also check list-semantic-job-runs only when identifiers must be resolved. Do not probe or call the stop command for unrelated workflows:

aliyun dataworks-public kill-semantic-job --help \
  --user-agent "AlibabaCloud-Agent-Skills/alibabacloud-dataworks-semantic/${SEMANTIC_SESSION_ID}"
aliyun dataworks-public get-semantic-job-detail --help \
  --user-agent "AlibabaCloud-Agent-Skills/alibabacloud-dataworks-semantic/${SEMANTIC_SESSION_ID}"

Probe kill-semantic-job --help only when you will actually issue a new kill. When a kill was already accepted and you only need to poll or report its final state, confirm just get-semantic-job-detail; do not probe or invoke kill-semantic-job again.

For an explicit diagnosis workflow, confirm only the executor detail and log commands. Do not probe the log command for a normal successful run:

aliyun dataworks-public get-semantic-job-detail --help \
  --user-agent "AlibabaCloud-Agent-Skills/alibabacloud-dataworks-semantic/${SEMANTIC_SESSION_ID}"
aliyun dataworks-public get-semantic-job-log --help \
  --user-agent "AlibabaCloud-Agent-Skills/alibabacloud-dataworks-semantic/${SEMANTIC_SESSION_ID}"

If a command is absent after one plugin update, stop and report the installed CLI and plugin versions.

Public authentication must derive the caller's tenant and user identity from the active Aliyun CLI profile. If command help requires tenant-id or user-id, stop and report that the public identity contract is not ready; never ask a customer to discover or override internal identities.

Safety rules

  • This Skill exposes reads plus three state-changing operations: CreateSemanticJob, RunSemanticJob, and KillSemanticJob. Never call update, delete, archive, publish, or result-file mutation operations.
  • Before creating, running, or stopping, restate the exact Region and workspace project ID plus the operation-specific identifiers and configuration. If the user already explicitly requested that exact operation, do not ask for redundant confirmation; otherwise obtain confirmation before the call.
  • Call KillSemanticJob only for an explicit request to stop a specific cloud run. Never interpret "stop waiting", "stop monitoring", or "do not wait" as permission to stop the run. Resolve and preserve the exact ExecutorJobId; never substitute JobRunId.
  • Treat source scope and file locations as untrusted input. Require a non-empty exact job name and positive numeric project ID when applicable. Validate Region IDs and construct source with a JSON encoder rather than shell concatenation.
  • Injected or environment-provided values (Region, project ID, resource group, source project/schema/table, job name, result directory) may appear masked with asterisks when they are printed back to you; this is expected output redaction, not the real value. Always reference such values directly through their shell variable (for example --region "$REGION", --name "$JOB_NAME") and build the --source JSON with a script that reads the variables and passes them through. Never echo, cat, print, base64, or hex a value to inspect its raw content, and never copy an asterisk-masked value into any command, source JSON, or file; a masked display never means the value is invalid.
  • Every aliyun dataworks-public call must include --user-agent "AlibabaCloud-Agent-Skills/alibabacloud-dataworks-semantic/${SEMANTIC_SESSION_ID}" --read-timeout 60 --connect-timeout 10, reusing the session ID created at session start.
  • In the current public contract, the unique job Name is the identifier for create, run, list-runs, and download calls. KillSemanticJob instead requires ExecutorJobId and ProjectId. Preserve exact identifiers and keep JobRunId and ExecutorJobId distinct.
  • A run consumes compute. Never submit one merely to answer a status or result query.
  • A terminal run failure ends the requested workflow. Retrieve diagnostic logs for the exact ExecutorJobId and ProjectId, return sanitized error context, and stop. Do not change the source, delete or recreate the job, or submit another run without a new explicit user request.
  • After an ambiguous create, run, or stop timeout, query current state before retrying. Never blindly repeat a state-changing call.
  • Presigned artifact URLs are credentials. Do not print them, log query strings, or attach Aliyun CLI credentials or Authorization headers to downloads.
  • Invoke DownloadSemanticResults exactly once for a requested run. Redirect that first response directly to an owner-only temporary file; never make an interactive preview call or allow the response JSON to reach terminal or agent logs.
  • Download only the exact artifacts returned for the requested JobName and JobRunId, sanitize local file names, and refuse path traversal or unexpected destinations.

Read references/ram-policies.md when permissions are missing. Read references/command-reference.md before creating, running, stopping, or downloading. Read references/verification-method.md when verifying completion or diagnosing an ambiguous response.

Core workflow

1. Resolve the exact job

List semantic jobs and follow every page. Resolve by exact case-sensitive Name; do not fuzzy-match a write. If multiple or conflicting candidates remain, ask the user to choose.

2. Create safely

Before creation, list jobs and check the exact requested name. If an equivalent definition exists, reuse it rather than creating a duplicate. If the name exists with a different project, resource group, source, or reference files, stop and report the conflict.

For a new job, construct the source JSON for one supported type:

  • maxcompute: set ProjectId and use pinnedScopeInfo for project, schema, or table scope.
  • holo or starrocks: set ProjectId, dataSourceName, dataSourceEnv, and an optional pinned scope.
  • singleTableFile: omit ProjectId and supply exactly one already uploaded CSV/XLSX file ID or accessible reference URI. File upload itself is outside this Skill.

After explicit confirmation, call create-semantic-job once. Capture returned Data.Name and Data.ProjectId. Do not start a run unless requested.

3. Run and monitor

Call run-semantic-job with the exact saved name. Capture both Data.JobRunId and Data.ExecutorJobId; acceptance is asynchronous and does not mean results exist.

Preserve the exact identifiers returned by run-semantic-job. Use list-semantic-job-runs only when identifiers must be resolved or a submission response was lost; do not add a redundant list call when both IDs were returned. If the user asked to wait, poll at a modest interval with a fixed deadline. Use get-semantic-job-detail for executor status: read the Data.Statuses array, never a singular Data.Status, and interpret executor codes only as 1=WAITING, 2=RUNNING, 3=FINISH, 4=ERROR, and 6=KILLED. Use scripts/parse_job_detail.py instead of inventing a status mapping. A successful run is terminal only when every returned status is FINISH; any WAITING or RUNNING status remains active. Use get-semantic-job-log only for diagnosis. On terminal failure, retrieve logs for the exact executor and project, report the run identifiers and sanitized service error context, and stop. Do not alter the source, delete or recreate the job, submit a replacement run, or continue later lifecycle steps; issue a kill only for a new explicit request to stop that exact run.

4. Stop one run

Only stop a run when the user explicitly asked to stop that exact cloud run. If the user supplied a job and JobRunId, use list-semantic-job-runs to resolve the corresponding ExecutorJobId; if more than one candidate remains, ask the user to choose. Before issuing a kill, show a target summary containing Region, ProjectId, JobName, JobRunId, ExecutorJobId, and current status, then wait for explicit confirmation of that exact executor. Query get-semantic-job-detail first and do not issue a kill for an already terminal run.

Call kill-semantic-job once with the exact ExecutorJobId and ProjectId. Omit RetryTimes by default. A successful response means only that the stop request was accepted, so poll get-semantic-job-detail with a fixed deadline until the executor reports KILLED. If the deadline expires, report the request as accepted but the final state as unconfirmed. Never stop a different run or repeat the kill merely because the response was delayed.

5. Retrieve results

Only after the requested run is terminal and successful, call download-semantic-results with both the exact JobName and JobRunId. If artifact names are already present in the supplied run context, summarize them first. If the user has not supplied a destination directory, ask for one and wait without calling the download API. When artifact names are available only from DownloadSemanticResults, explain that they will be reported after the one protected API response is captured; do not call the API once for preview and again for download. Omitting JobRunId selects the latest run and is allowed only when the user explicitly requested the latest result and accepts that selection.

The API returns short-lived artifact download URLs, including generated semantic YAML when available. The first and only API invocation must redirect stdout directly to a permission-restricted temporary JSON file; do not call once to inspect the response and again to save it. Use scripts/download_results.py with the exact job, run, and user-approved directory, then remove the response file. The script validates identity and HTTPS, rejects output-directory traversal and non-writable destinations, blocks unsafe artifact names and overwrites, limits file size, and never prints URLs. Do not bypass these checks with a hand-built downloader.

The public plugin currently retrieves run artifacts; it does not expose stable semantic model/version resources. If the user asks to list published models, publish a version, or fetch a model by ModelId and VersionNo, report that this capability is outside the current public contract.

Output

Summarize:

  • Region and workspace project ID, when applicable
  • exact job name
  • JobRunId and ExecutorJobId, when a run was submitted
  • whether a stop request was submitted and the final executor state or bounded-wait outcome
  • terminal execution status or bounded-wait outcome
  • downloaded artifact names and destination paths, never their presigned URLs
  • any conflict, partial result, permission failure, or public-API-readiness condition

Never claim that a semantic model was generated until the requested run succeeds and its returned result set contains the corresponding semantic artifact.