/alibabacloud-agentloop-management

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: agentloop@alibaba-inc.com

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-running Java or Python application with CMS ServiceTask: dynamic log/snapshot/metric/span probes, JVM commands (OGNL, decompile, thread/memory/runtime inspection), disable/clear probes, and query capture results through SLS | [references/live-debug-runtime.md](references/live-debug-runtime.md) | | 6 | High-code instrumentation | Teach, implement, or troubleshoot manual instrumentation for AgentLoop with loongsuite-genai-utils / language-specific GenAI Utils and OpenTelemetry SDK: Java, Go, Python, Node.js; LLM/Agent/Tool/Retrieval spans, LLM Trace field formats, async/cross-process context propagation, business attributes and broken traces | [references/instrumentation/instrumentation.md](references/instrumentation/instrumentation.md) — dynamically retrieves official documentation without a browser |

Dispatch Rules

1. Classify the request into one or more domains using the routing table, then read **only** the matched domain entry file(s). Never preload all domains. For high-code/manual instrumentation, GenAI field semantics, or context propagation, dispatch to **High-code instrumentation first**. Only add Application onboarding when cloud setup, endpoint discovery, or service registration is actually needed; code guidance must not be blocked by onboarding's CLI/workspace prerequisites. 2. Follow the matched domain file completely. Each domain defines its own prerequisites, credentials check, RAM policies, parameter confirmation, execution-safety protocol, and verification method. For Live-Debug, the migrated entry file preserves the original skill contract and is authoritative for that domain wherever its module-specific rules differ from the shared conventions below. For a vague Live-Debug request, apply its parameter-completeness gate immediately after reading the entry file: state which target information is missing and stop. Treat the clarification as a completed final response for this run, not a request for another message. Use only declarative wording such as `Required inputs for a future run: ...`. The response MUST NOT contain a question mark or any request/invitation phrase, including `please provide`, `provide`, `send`, `reply`, `tell me`, `can you`, `could you`, `请提供`, `请补充`, `提供`, `补充`, `告知`, or `回复`. End exactly with `No diagnostic or cloud action was executed; this run is complete.` Do not run prerequisite checks, discover workspaces/services, inspect credentials, create output files, or issue any cloud call until a future request already supplies the required information. 3. If the request matches none of the domains, state that it is out of scope for this skill and do not dispatch. 4. If the intent is ambiguous between two domains, ask one clarifying question before dispatching.

Disambiguating Dataset vs Pipeline vs Evaluation

• Writing or reading rows the user already has: **Dataset**.
• Deriving new rows from LogStore or trace data through processing nodes: **Pipeline**. Create or confirm the sink Dataset fi