What makes alibabacloud-agentloop-management notable?

1K downloads. Trust evidence available

How should alibabacloud-agentloop-management be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

alibabacloud-agentloop-management

The skill should be used when the user asks about Alibaba Cloud AgentLoop platform for onboarding applications into observability, high-code instrumentation with loongsuite-genai-utils and OpenTelemetry SDK (高代码埋点、LLM Trace 字段、上下文传递与链路串联), Live-Debug runtime diagnostics, managing Datasets, building pipelines, and evaluating. Live-Debug covers ServiceTask dynamic logging, snapshots, metrics, spans, and JVM inspection.

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

0.1.5

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 0.1.5release · observed Oct 9, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s173swjet2yrebzqrp6hjkvmy583mxef:alibabacloud-agentloop-management

Install using `clawhub skill install s173swjet2yrebzqrp6hjkvmy583mxef:alibabacloud-agentloop-management` in an isolated environment before connecting it to live workloads.
No published capability contract is available yet, so validate auth and request/response behavior manually.
Review the upstream CLAWHUB listing at https://clawhub.ai/sdk-team/alibabacloud-agentloop-management before using production credentials.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-sdk-team-alibabacloud-agentloop-management/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

160,000 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: alibabacloud-agentloop-management
description: |
  The skill should be used when the user asks about Alibaba Cloud AgentLoop platform for onboarding applications into observability, high-code instrumentation with loongsuite-genai-utils and OpenTelemetry SDK (高代码埋点、LLM Trace 字段、上下文传递与链路串联), Live-Debug runtime diagnostics, managing Datasets, building pipelines, and evaluating. Live-Debug covers ServiceTask dynamic logging, snapshots, metrics, spans, and JVM inspection.
license: Apache-2.0
metadata:
  domain: aiops
  owner: agentloop
  contact: [email protected]
---

# AgentLoop Skill Router

> **Positioning**: This skill is the single entry point for Alibaba Cloud **AgentLoop** requests. It only classifies the user's intent and dispatches to one of the six domain playbooks below. All executable rules - prerequisites, credentials, RAM policies, parameter confirmation, safety protocols, command usage, and verification - live inside the domain files. Do not run any cloud operation before reading the matched domain file.

**Compatibility**: cloud-operation domains require Aliyun CLI 3.3.15 or later; Pipeline requires `aliyun-cli-agentloop` 0.7.4 or later; bundled evaluation, Pipeline, and public-document retrieval scripts require Python 3.8 or later. Instrumentation guidance and public-document retrieval do not require Aliyun CLI, cloud credentials, or a browser.

## Routing Table

| # | Domain | Intent | Entry file (read first) |
|---|--------|--------|-------------------------|
| 1 | Application onboarding (APM & AI observability) | Instrument an application so it reports to AgentLoop: probe or agent install, APM onboarding, `aliyun-bootstrap`, `AliyunJavaAgent`, `instgo`, `cms_node_sdk`, `ack-onepilot`, OpenTelemetry, LicenseKey, K8s/ACK/ECS onboarding, LLM and AI-framework tracing (Dify, LangChain, DashScope) | [references/onboarding.md](references/onboarding.md) - internally routes to [references/apm.md](references/apm.md) / [references/ai.md](references/ai.md) |
| 2 | Evaluation | Score model, agent, or trace quality: create and update evaluators and evaluator skills, one-shot sample tests, batch trace or Dataset evaluation, trace backfill, poll an evaluation task, analyze results and low-score cases | [references/evaluation/evaluation.md](references/evaluation/evaluation.md) |
| 3 | Dataset | Store and retrieve structured rows: Dataset lifecycle and schema, append rows with `add-dataset-data`, read-only queries with `execute-query`, SQL or SearchExpr, semantic search, embedding fields | [references/dataset/dataset.md](references/dataset/dataset.md) |
| 4 | Pipeline | Transform source data into a Dataset once or on a schedule: import Logstore/SLS data into a Dataset, import traces, design specs, preview/create/run, inspect runs, control the lifecycle, configure processing nodes, and map OT AI traces | [references/pipeline/pipeline.md](references/pipeline/pipeline.md) |
| 5 | Live-Debug runtime diagnostics | Diagnose an already-r

_meta.json

{
  "ownerId": "kn74p5w8ywv6prh40g0s82gmqh83nw54",
  "slug": "alibabacloud-agentloop-management",
  "version": "0.1.5",
  "publishedAt": 1791528271619
}

references/ai.md

# AI Observability Module

> Global conventions (credentials, Observability / User-Agent, output format, error codes, command prefix) - see [onboarding.md](onboarding.md).
> RAM permissions - see [ram-policies.md](ram-policies.md).
> Run `aliyun cms2 apm <subcommand> --help` for full flag lists and examples.

## Scope

Guided workflow to onboard AI applications (LLM-based services, AI Agents, custom instrumented apps) into AgentLoop application observability. Uses `aliyun cms2` CLI to initialize APM infrastructure, retrieve access credentials, and generate framework-specific configuration.

For high-code instrumentation with GenAI Utils / OpenTelemetry SDK, field-format questions, or broken async/cross-process traces, use [instrumentation/instrumentation.md](instrumentation/instrumentation.md) before this cloud workflow. That module dynamically retrieves the language guides, field specification, and all relevant best-practice candidates without requiring a browser or cloud credentials. Return here only when cloud setup is needed.

**In-Scope**: Initialize APM infra, retrieve LicenseKey/Endpoint, register app services, generate startup configuration for all supported AI frameworks.

**Out-of-Scope**: Model fine-tuning or training observability; GPU monitoring (see `cloud-acs-ecs-gpu` addon); general CloudMonitor (CMS) management; `default-cms-*` or other non-`agentloop-*` workspaces; alerts, RUM, Prometheus rules, and other non-onboarding CMS features.

---

## Workspace Mandatory Rules

> **CRITICAL** - AgentLoop AI onboarding requires an explicit AgentLoop workspace from the user.

1. **Always ask user to provide `workspace`**: Never auto-build workspace as `default-cms-{AccountId}-{regionId}`.
2. **Workspace format is `agentloop-{32-char-code}`**: If missing, run `aliyun cms2 workspace list -o json` and reuse the first `agentloop-[0-9a-f]{32}` match. On quota 403/400, reuse existing `agentloop-*` workspace and continue.
3. **Do NOT proceed without a valid workspace**: All APM commands require the user-provided or discovered `agentloop-*` workspace.

---

## Execution Safety Protocol

Follow the same Two-Phase Execution Protocol as [apm.md - Execution Safety Protocol](apm.md#execution-safety-protocol).

**Operations that do NOT require confirmation** (execute directly):
- Read-only commands: `get`, `list`, `--help`
- AgentLoop platform resource creation: `apm configuration create`, `apm service create`
- Retrieving credentials: `apm configuration get` (the returned LicenseKey must stay redacted - see [onboarding.md - Credential Output Redaction](onboarding.md#credential-output-redaction))
- Fetching addon templates: `integration addon get`

**Operations that REQUIRE confirmation** (must use Two-Phase Protocol):
- Deleting service records: `apm service delete`
- Modifying user application startup scripts or Dockerfiles

---

## Supported Frameworks

| Framework | Addon Name | Protocols | Underlying Agent |
|-----------|-----------|-----------|---

references/apm.md

# Application Monitoring (APM) Module

> Global conventions (credentials, Observability / User-Agent, output format, error codes, command prefix) - see [onboarding.md](onboarding.md).
> RAM permissions - see [ram-policies.md](ram-policies.md).
> Run `aliyun cms2 apm <subcommand> --help` for full flag lists and examples.

## Scope

Guided workflow to onboard server-side applications into AgentLoop application observability. Uses `aliyun cms2` CLI to initialize APM infrastructure and retrieve access credentials, then generates configuration for the user's specific language and deployment method.

**In-Scope**: Initialize APM infra, retrieve LicenseKey/Endpoint, register app services, generate startup configuration for all supported languages, **auto-modify K8s Deployment YAML** (with user confirmation) via `aliyun cs` + `kubectl`.

**Out-of-Scope (this version)**: Automatic modification of ECS host startup scripts / Dockerfile; agent binary downloads; general CloudMonitor (CMS) management; `default-cms-*` or other non-`agentloop-*` workspaces; alerts, RUM, Prometheus rules, and other non-onboarding CMS features.

---

## Workspace Mandatory Rules

> **CRITICAL** - AgentLoop onboarding requires an explicit AgentLoop workspace from the user. The following rules are **absolute and non-negotiable**.

1. **Always ask user to provide `workspace`**: Never auto-build workspace as `default-cms-{userId}-{regionId}` or any other derived format.
2. **Workspace format is `agentloop-{32-char-code}`**: Prefix `agentloop-` followed by exactly 32 characters (e.g. `agentloop-2694ecf8****************1f84542d`). If missing or invalid, run `aliyun cms2 workspace list -o json` and reuse the first matching `agentloop-[0-9a-f]{32}` workspace; if none match, stop and prompt: **Please provide a valid AgentLoop workspace in the format `agentloop-{32-char-code}`.**
3. **Quota fallback**: if workspace creation hits **403/400** quota errors, list existing workspaces, reuse a matching `agentloop-*` workspace, log the reuse reason, and continue - do not abort or fall back to `default-cms-*`.
4. **Do NOT proceed without a valid workspace**: All `apm configuration` / `apm service` commands require `--workspace`; do not guess or fabricate a value.
5. **Region must be obtained separately**: `regionId` is NOT encoded in the workspace name. Derive it from cluster metadata or kubeconfig context in container flows; ask the user in non-container flows.

---

## Container Onboarding Mandatory Rules

> **CRITICAL** - When the user selects container (ACK/ACS/K8s) onboarding, the following rules are **absolute and non-negotiable**. Violating any of them is a workflow error.

1. **Do NOT ask user for `regionId`**: In container onboarding, `regionId` must be derived automatically from cluster metadata or kubeconfig context. Never prompt the user for region. If derivation fails, use `aliyun cs describe-clusters` output to extract `region_id` from cluster info.
2. **Do NOT run any `integration ad

references/dataset/data-operations.md

# Dataset Data Operations

## Append Structured Rows

Prefer `add-dataset-data` for normal inserts. It accepts typed JSON and avoids SQL escaping mistakes.

```bash
aliyun agentloop add-dataset-data \
--region <region_id> \
--agent-space <agent_space_name> \
--dataset-name <dataset_name> \
--data-array '[
{
"question":"How do I inspect an error?",
"answer":"Start with the request ID.",
"score":0.95,
"metadata":{"source":"manual","latency_ms":120}
},
{
"question":"How do I retry safely?",
"answer":"Reuse the same idempotency token.",
"score":0.91,
"metadata":{"source":"reviewed","latency_ms":85}
}
]' \
--client-token <client_token>
```

Rules:

- `dataArray` cannot be empty and every item must be a JSON object.
- All rows in one request are committed atomically.
- The maximum request body is 100 MiB (`100 * 1024 * 1024` bytes). There is no separate fixed API row-count limit; the server streams rows and keeps the request atomic.
- Unknown fields fail the whole request.
- Missing schema fields are stored as `null`.
- Field names are matched case-insensitively; duplicate case variants are rejected.
- `text` accepts strings, `long` accepts integers, `double` accepts finite numbers, and `json` accepts any valid JSON value.
- Omit `id` to auto-generate it. A supplied `id` must be a UUID.
- Omit `__time__` to use the current Unix time. A supplied value must be non-null and a non-negative integer in seconds.
- Never supply `__dataset_seq`.

Before dry-run, compare every row's keys and nested values with the user's requested data, separately from schema validation. The payload may contain only user-authorized fields supported by the schema, plus system fields the user explicitly supplied. Existing optional schema fields may remain omitted; their presence is not permission to populate them. If the user requests a marker in `agentloop_annotations`, keep it inside that object, even if the schema also has a top-level `marker_id`. Do not add or duplicate a field to make verification easier.

Dry-run complex row-array structure and inspect that booleans, numbers, objects, arrays, and null values retain their JSON types. If rows contain real prompts, outputs, tokens, PII, or other sensitive content, use a shape-equivalent synthetic array for dry-run; do not print the real request body into terminal history or conversation output:

```bash
aliyun agentloop add-dataset-data \
--region <region_id> \
--agent-space <agent_space_name> \
--dataset-name <dataset_name> \
--data-array '<json_array>' \
--client-token <client_token> \
--cli-dry-run
```

Success returns `requestId` and `affectedRows`. Verify `affectedRows` equals the submitted row count, then query a narrow sample.

Use the same authorized field structure for the serialized dry-run body and the real write. Successful serialization or API acceptance does not prove that extra fields were requested. When the requested readbac

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