| name | alibabacloud-agentloop-evaluation |
| description | Orchestrate AgentLoop evaluation workflows through the Aliyun CLI plugin with safe previews, saved evaluator and evaluator-skill management, one-shot sample tests, trace or dataset batch runs, polling, and result inspection. Analyze evaluation quality and low-score cases from SLS. Use for natural-language requests to create or update evaluators or evaluator skills, launch or monitor evaluation tasks, inspect evaluation results, diagnose low scores, troubleshoot evaluation API calls, and simplify AgentLoop evaluation commands. |
Alibaba Cloud AgentLoop Evaluation
Scenario Description
Orchestrate AgentLoop evaluation workflows through the Aliyun CLI plugin (aliyun-cli-agentloop). The skill converts a compact JSON specification into the required AgentLoop API sequence: discover evaluators, create/update saved evaluators, create one-shot or batch evaluation tasks, poll runs, and analyze results from SLS.
Architecture: Aliyun CLI >= 3.3.3 + aliyun-cli-agentloop 0.7.0 + aliyun-cli-sls plugin + AgentLoop API (2026-05-20) + SLS evaluation_detail Logstore
Use the bundled wrapper to convert a compact JSON specification into the required AgentLoop API sequence. Default to a dry-run; send mutations only with --execute.
Installation
Pre-check: Aliyun CLI >= 3.3.3 required
[MUST] Verify: aliyun version — must be >= 3.3.3.
- First install or major upgrade: Download, review, then execute the setup script. Avoid
curl | bash piping.
- Routine update (CLI >= 3.3.5):
aliyun upgrade — prefer this built-in self-update over re-running the install script.
- See references/cli-installation-guide.md for full installation instructions.
Pre-check: Aliyun CLI plugin update required
[MUST] run aliyun configure set --auto-plugin-install true to enable automatic plugin installation.
[MUST] run aliyun plugin update to ensure that any existing plugins are always up-to-date.
Verify the AgentLoop plugin:
aliyun plugin show --name aliyun-cli-agentloop
aliyun agentloop version
For result analysis, also install the SLS plugin:
aliyun plugin install --name aliyun-cli-sls
Script Dependencies
The bundled Python scripts in scripts/ use only the Python 3.8+ standard library — no external packages are required. See scripts/requirements.txt for the full declaration.
Environment Variables
| Variable | Required | Description |
|---|
SKILL_DIR | yes | Absolute path to this skill directory (containing SKILL.md) |
ALIBABA_CLOUD_PROFILE | no | Scope the workflow to a named CLI profile without switching the default |
SKILL_SESSION_ID | no | 32-char hex session ID for observability; injected by the agent at runtime |
Authentication
Pre-check: Alibaba Cloud Credentials Required
Security Rules:
- NEVER read, echo, or print AK/SK values (e.g.,
echo $ALIBABA_CLOUD_ACCESS_KEY_ID is FORBIDDEN)
- NEVER ask the user to input AK/SK directly in the conversation or command line
- NEVER use
aliyun configure set with literal credential values
- ONLY use
aliyun configure list to check credential status
aliyun configure list
Check the output for a valid profile (AK, STS, or OAuth identity).
If no valid profile exists, STOP here.
- Obtain credentials from Alibaba Cloud Console
- Configure credentials outside of this session (via
aliyun configure in terminal or environment variables in shell profile)
- Return and re-run after
aliyun configure list shows a valid profile
RAM Policy
This skill requires AgentLoop and SLS permissions. See references/ram-policies.md for the full permission list.
[MUST] Permission Failure Handling: When any command or API call fails due to permission errors at any point during execution, follow this process:
- Read
references/ram-policies.md to get the full list of permissions required by this SKILL
- Use
ram-permission-diagnose skill to guide the user through requesting the necessary permissions
- Pause and wait until the user confirms that the required permissions have been granted
Parameter Confirmation
IMPORTANT: Parameter Confirmation — Before executing any command or API call,
ALL user-customizable parameters (e.g., AgentSpace name, Region, evaluator names,
task names, dataset names, time windows, thresholds, etc.) MUST be confirmed with the
user. Do NOT assume or use default values without explicit user approval.
| Parameter Name | Required/Optional | Description | Default Value |
|---|
agent_space | Required | Target AgentSpace name | — |
region | Optional | Aliyun endpoint-selection region | — |
task.name | Optional | Evaluation task name | Auto-generated |
task.mode | Optional | oneshot or batch | Inferred from data_filter |
task.data_type | Optional | trace or dataset | trace |
data_filter.max_records | Required (batch) | Maximum records to evaluate | — |
window.start | Required (batch) | Backfill start time (timezone-bearing ISO-8601) | — |
window.end | Required (batch) | Backfill end time (timezone-bearing ISO-8601) | — |
threshold | Optional | Low-score threshold for analysis | 0.5 |
max_cases | Optional | Maximum low-score cases to return | 50 |
Mutation Confirmation Protocol
CRITICAL: All cloud mutation operations require explicit user confirmation before execution.
This skill can create, modify, and delete cloud resources. To prevent unintended
changes, every mutation MUST follow the three-step protocol below. No exceptions.
Mutation operations requiring confirmation
The following operations alter cloud resources and MUST NOT be executed without
explicit user approval:
| Operation | CLI/API | Risk |
|---|
| Create evaluator | create-evaluator | Creates a persistent saved evaluator |
| Update evaluator | update-evaluator | Modifies an existing evaluator's config/version |
| Create evaluation task | create-evaluation-task | Launches a potentially costly evaluation run |
| Execute evaluation task | run --execute | Sends the actual mutation request to the cloud |
| Continuous evaluation | continuous.enabled=true | Incurs ongoing compute costs until stopped |
| Unbounded batch run | --allow-unbounded | Evaluates all matching records with no record cap |
| Delete task | delete-evaluation-task | Irreversible resource deletion |
| Delete evaluator | delete-evaluator | Irreversible resource deletion |
| Terminate task | update-evaluation-task --status Terminated | Stops a running evaluation |
Three-step confirmation protocol
-
Preview (dry-run) — Always run without --execute first. Show the user
the exact commands and JSON payloads that will be sent to the cloud API.
python3 "$SKILL_DIR/scripts/agentloop_eval.py" run --spec /path/to/evaluation.json
-
Confirm — Present the rendered preview to the user and explicitly ask for
approval. Quote the key parameters (AgentSpace, evaluator names, task name,
data scope, estimated records, cost implications). Wait for a clear
affirmative response before proceeding.
Do NOT assume that running run without --execute implies intent to
execute. Do NOT proceed to execution based on silence or ambiguity.
-
Execute — Only after receiving explicit confirmation, add --execute and
any required override flags (--allow-unbounded, --allow-continuous):
python3 "$SKILL_DIR/scripts/agentloop_eval.py" run \
--spec /path/to/evaluation.json --execute
Read-only operations (no confirmation needed)
doctor, discover, status, and analyze (result analysis) are read-only
and do not require the confirmation protocol.
Observability (MUST follow for every aliyun command)
Upon loading this skill, generate a random session ID (32-char lowercase hex string) once for the entire session. Use it as {session-id} below.
Rule: Every aliyun CLI command that calls a cloud API MUST include the --user-agent flag.
Local utility commands (e.g. configure, plugin, version) do not support this flag and should be excluded.
--user-agent AlibabaCloud-Agent-Skills/alibabacloud-agentloop-evaluation/{session-id}
Example (assuming session-id is a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6):
aliyun agentloop list-evaluators --agent-space my-space --user-agent AlibabaCloud-Agent-Skills/alibabacloud-agentloop-evaluation/a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6
Do not skip, alter the format, or omit --user-agent on any aliyun API command invocation.
Script execution: The bundled Python wrapper automatically reads SKILL_SESSION_ID from the environment and injects --user-agent into every cloud API command. Inject the session-id via inline environment variable:
SKILL_SESSION_ID={session-id} python3 "$SKILL_DIR/scripts/agentloop_eval.py" run --spec evaluation.json --execute
SKILL_SESSION_ID={session-id} python3 "$SKILL_DIR/scripts/analyze_evaluation_results.py" --agent-space <space> ...
Core Workflow
Select the workflow
- Use a one-shot task with
dataFilter.provided to test an evaluator against one supplied sample.
- Use a batch trace task with a bounded time window and
maxRecords to evaluate observed traces.
- Use a batch dataset task for an existing AgentLoop dataset. Start from references/examples/batch-dataset-example.json and map evaluator variables directly to dataset columns.
- Dataset task with multiple saved evaluators: referencing two or more custom saved evaluators via
evaluatorRef/evaluator_refs triggers a backend defect that expands the data-record set by the evaluator count (rows × evaluators) and re-scores every expanded record with every evaluator, so each row is evaluated N times per evaluator. On --execute the wrapper auto-detects this and inlines the referenced evaluator definitions (fetched via get-evaluator) into the task to match the safe console behaviour; it prints a WARNING and the sent request differs from the dry-run preview. Built-in refs and single-evaluator tasks are untouched. See references/spec-format.md (Dataset batches) for details.
- Use result analysis to inspect evaluation quality and low-score cases from the fixed
evaluation_detail SLS Logstore.
- Use
discover or status for read-only inspection.
- Add
evaluator_actions only when the user asks to create or update saved evaluators. Use only AGENT or CODE as saved-evaluator create types.
- Decide the execution behavior by
config.agentEvaluatorMode, NOT by --type alone. Genuine StarOps Agents are standard mode; raw-prompt judges (including rawPromptBackend=starops) are stored as type=AGENT but execute as LLM judges.
- Genuine StarOps Agent evaluator (数字员工): create
type=AGENT and OMIT config.agentEvaluatorMode and config.rawPromptBackend entirely. The backend stores agentEvaluatorMode=standard and runs the standard digital-employee agent flow. This is the only form that a task executes as a real Agent (matches built-ins like , which report , ). Do NOT set if you want a real Agent evaluator.
Read references/spec-format.md whenever composing or changing a workflow specification. Read references/api-map.md when extending the flow, diagnosing a failed command, or when the installed plugin version differs from the documented version. Read references/result-analysis.md whenever analyzing evaluation results or low-score cases.
Run the workflow
-
Resolve the skill location from the absolute path of this SKILL.md. Prefer the path supplied by the host environment for the loaded skill. Do not assume a platform-specific skill home. Set the path once and verify that the bundled wrapper exists:
export SKILL_DIR="/absolute/path/to/alibabacloud-agentloop-evaluation"
test -f "$SKILL_DIR/scripts/agentloop_eval.py"
When multiple Aliyun CLI profiles exist, scope the workflow without switching the default profile:
export ALIBABA_CLOUD_PROFILE=<profile-name>
-
Check the local CLI and plugin. Add --agent-space to verify cloud authentication and access with a read-only request:
python3 "$SKILL_DIR/scripts/agentloop_eval.py" doctor --agent-space <space>
-
Discover saved evaluators, current built-in evaluator names, and tasks before inventing names or relying on an old built-in alias:
python3 "$SKILL_DIR/scripts/agentloop_eval.py" discover \
--agent-space <space> --all-pages
Task discovery defaults to the service's default channel. Add --channel <channel> to inspect another channel. One-shot tasks are not returned by the list API; retain their task IDs.
-
Create a spec from the nearest example, filling only required fields. Never put AccessKeys, bearer tokens, or other credentials in the spec.
-
Preview the evaluator and task requests. This invokes the plugin's --cli-dry-run and does not send the AgentLoop mutation request. The CLI may still resolve or refresh credentials:
python3 "$SKILL_DIR/scripts/agentloop_eval.py" run --spec /path/to/evaluation.json
If the specification enables continuous evaluation, add --allow-continuous only after the user explicitly accepts the ongoing cost. Use the flag for both preview and execution.
-
Inspect the rendered preview. Present the key parameters (AgentSpace, evaluator names, task name, data scope, estimated records, cost implications) to the user and obtain explicit confirmation before proceeding. Only add after the user approves the preview — even if the user initially requested execution, the preview must be shown and confirmed first:
Analyze evaluation results
Use the read-only analyzer after a run completes or when the user asks why evaluation quality is low. Preview the exact SLS queries first:
python3 "$SKILL_DIR/scripts/analyze_evaluation_results.py" \
--agent-space <space> \
--region <region> \
--from <timezone-bearing-ISO-or-epoch> \
--to <timezone-bearing-ISO-or-epoch> \
--task-id <task-id> \
--preview
Then run the bounded analysis when the user has asked for result analysis:
python3 "$SKILL_DIR/scripts/analyze_evaluation_results.py" \
--agent-space <space> \
--region <region> \
--from <timezone-bearing-ISO-or-epoch> \
--to <timezone-bearing-ISO-or-epoch> \
--task-id <task-id> \
--threshold 0.5 \
--max-cases 50 \
--output /path/to/evaluation-analysis.json
Narrow by --run-id or --evaluator-name when available. The analyzer resolves the SLS project from the AgentSpace, queries only evaluation_detail, and returns overview metrics, evaluator-level breakdowns, and low-score case evidence.
Raw input, output, evaluation process, and custom outputs are omitted by default. Add --include-content only when exact customer content is necessary and explicitly authorized.
Success Verification Method
See references/verification-method.md for step-by-step verification commands covering CLI checks, discovery, preview, execution, result analysis, and unit tests.
Cleanup
This skill does not automate delete, cancel, or terminate operations. To clean up resources manually after use:
Note: These are raw aliyun CLI commands and must include --user-agent for observability. Replace {session-id} with the current SKILL_SESSION_ID.
-
Terminate a running task (explicit user authorization required):
aliyun agentloop update-evaluation-task --agent-space <space> --task-id <task-id> --status Terminated --user-agent AlibabaCloud-Agent-Skills/alibabacloud-agentloop-evaluation/{session-id}
-
Delete a task (explicit user authorization required):
aliyun agentloop delete-evaluation-task --agent-space <space> --task-id <task-id> --user-agent AlibabaCloud-Agent-Skills/alibabacloud-agentloop-evaluation/{session-id}
-
Delete a saved evaluator (explicit user authorization required):
aliyun agentloop delete-evaluator --agent-space <space> --name <evaluator-name> --user-agent AlibabaCloud-Agent-Skills/alibabacloud-agentloop-evaluation/{session-id}
Warning: These are destructive operations. Always require explicit user permission before execution. One-shot task and run records are automatically cleaned by the backend after 24 hours.
Best Practices
- Require a timezone-bearing ISO-8601 window or epoch milliseconds for batch backfills.
- Require
dataFilter.maxRecords for batch tasks unless the user explicitly accepts an unbounded run and --allow-unbounded is supplied.
- Keep
continuous.enabled false unless the user explicitly requests continuous evaluation and understands the ongoing cost; the wrapper requires --allow-continuous when it is enabled.
- Preserve unique evaluator identities within a task. Use
evaluatorRef for saved evaluators and name for inline evaluators.
- Create saved evaluators only with an exact supported type:
AGENT or CODE (type=LLM is normalized to AGENT). Choose the mode deliberately: a genuine StarOps Agent evaluator requires type=AGENT with config.agentEvaluatorMode OMITTED (backend defaults to standard) — do not add rawPromptBackend. An LLM-style evaluator requires type=AGENT with config.agentEvaluatorMode=raw_prompt + config.rawPromptBackend=direct_llm. Note agentEvaluatorMode=raw_prompt always executes as an LLM judge even with rawPromptBackend=starops, so raw_prompt+starops is NOT a real Agent evaluator. Verify after create with get-evaluator and confirm agentEvaluatorMode is standard (Agent) vs raw_prompt (LLM).
- Use
config.outputSchema for custom result fields. When outputSchema is present, include or allow the wrapper to default score and explanation; define extra fields with type, required, rule, and optional range, options, or items.
- Treat evaluator version updates, evaluator-skill create/update operations, task creation, and continuous evaluation as cloud mutations. Preview them first.
- Keep result analysis read-only and time-bounded. Query only
evaluation_detail, cap low-score cases at 200, and omit raw customer content unless --include-content is explicitly authorized.
Refresh compatibility
If doctor reports a plugin or API version different from the Architecture line above, inspect these before execution and adjust only the affected mappings:
aliyun agentloop create-evaluator --help
aliyun agentloop create-evaluator-skill --help
aliyun agentloop create-evaluation-task --help
aliyun agentloop get-evaluation-task --help
aliyun agentloop list-evaluation-runs --help
aliyun agentloop get-evaluation-run --help
Reference Links
