alibaba

内核参数自动调优。分析系统配置，输出调优建议并可一键应用，支持自动回滚。使用场景：系统性能优化、安全加固、内核参数诊断。

Use when troubleshooting Linux server performance or stability issues — CPU saturation, high load, scheduling delay, memory pressure, OOM events, high RSS, page cache / shared memory growth, memory cgroup residue, Java heap issues, disk IO saturation or latency, packet loss, network jitter, or a server that is slow, stuck, or unstable. Performs diagnosis and surfaces recommendations; does not apply fixes automatically.

Install and configure OpenClaw non-interactively with Alibaba Cloud Model Studio. Use when the user asks to install OpenClaw, configure Aliyun Bailian/Model Studio/DashScope credentials, choose pay-as-you-go, Coding Plan, or Token Plan billing, set Base URL/model config, optionally configure DingTalk, start the local gateway service, or troubleshoot OpenClaw model/auth/channel setup.

Skill 安全状态查看、风险暴露审查、用户决策、快速扫描认证与可选深度扫描。支持用户主动查看或扫描单个/全部 Skill；当用户要求 agent 安装 Skill 且安装成功后，必须自动对最终本地目录执行快速扫描认证。

工作区快照管理。用户说"保存一下"、"存个快照"时创建 checkpoint，仅限 Linux; 说"回滚"、"撤销"、"恢复到之前"时 rollback;说"删掉快照"时 delete; 说"对比快照"、"快照改了什么"时 diff; 说"看看快照"、"有哪些快照"时 list;说"查看快照状态"、"查看快照剩余空间"时 status。

Help users configure, mount, and unmount a skillfs FUSE virtual filesystem on their local machine (e.g. for openclaw). Covers analyzing existing skill usage to suggest view configuration, generating skillfs-views.toml, mounting, and graceful unmount. Use this skill when the user asks to: set up skillfs, configure skill views, mount or unmount skillfs, check if skillfs is running, or get help organizing their skills directory (especially for openclaw users).

Use this skill when the user asks about ANOLISA, Alibaba Cloud Linux 4 Agentic Edition, cosh, Copilot Shell, AgentSecCore, AgentSight, Tokenless, ws-ckpt, OS Skills, or any component of ANOLISA. This includes questions about usage, configuration, commands, free quota, billing, pricing, authentication, or how to use specific features like switching to bash, token optimization, checkpoint rollback, security features, or deploying OpenClaw/Claude Code.

A test skill used in integration tests.

Set up, run, and interpret Agent Skill evaluations (evals) with the skill-up CLI / 使用 skill-up CLI 给 Agent Skill 搭建和运行评测. Use when the user asks to evaluate, test, regress, or verify a Skill; add evals or cases; write eval.yaml/case.yaml; run skill-up run/validate/list-cases/report/import/init; or migrate from Anthropic evals.json. Handles Skill discovery, evals scaffolding, judge authoring, credentials, user config, validation, runs, and reports.

SDD-RIPER workflow executor with phase gating

帮助用户进行代码审查，识别常见代码问题并给出修改建议。

帮助用户管理待办事项，支持添加、删除、标记完成、列出任务等操作。

MCP mocked marker smoke skill used by skill-up e2e tests.

Analyzes code files and reports statistics including line counts, file counts by extension, and total size. Use this skill whenever the user wants to understand the composition of a codebase - asking about "how many lines of code", "what file types exist", "code distribution", or needing a quick audit of project size and structure. Make sure to invoke this skill when users mention analyzing codebases, counting lines, checking file distributions, or auditing code.

Reviews code and provides detailed quality assessment

Quickly produces accurate calculation results when a user needs arithmetic, unit conversion, or mathematical formula solving. Triggered when users say things like "help me calculate", "compute", "how much is", etc.

在进行 Code Review 时，使用这个技能对 LoongCollector 变更进行安全导向、架构一致性优先的深度代码评审。

按 Conventional Commits 规范编写提交信息。在创建 git commit 时使用。

编译 LoongCollector C++ 与 Go 组件。涉及构建、单元测试或镜像时使用。

Design document writing conventions. Use when writing or reviewing technical design documents.

LoongCollector E2E Testing Framework Development Guide. Use this rule when you need to understand E2E testing framework architecture, develop new test behavior functions, or extend testing capabilities. Contains detailed BDD testing framework usage, test behavior function development and registration workflows, and comprehensive reference of existing testing capabilities. Example: When adding custom test behaviors for new plugins, use this rule to understand how to write and register new test functions. Example: When developing E2E tests, use this rule to understand the e2e framework.

E2E Testing Standards Guide. Use this rule when you need to write or execute E2E tests, including test naming conventions, test workflows, and test behavior templates. This rule should be used for end-to-end integration testing scenarios to ensure test standardization and maintainability. Example: When writing new E2E tests, use this rule to ensure proper test format and workflow compliance.

LoongCollector E2E 测试全流程指南：设计、编写、运行和调试。当需要编写新 E2E 测试、运行现有测试、或排查 E2E 测试失败时使用此 skill。

Whenever diagrams or mermaid is mentioned

GreptimeDB provider for obz. Covers PromQL metric query, labels, label-values, and series commands via GreptimeDB's Prometheus-compatible API. Use this skill when the user mentions "GreptimeDB", "Greptime", "obz metric -p greptimedb", or needs to query metrics stored in a GreptimeDB instance.

Multi-backend observability CLI for querying metrics, logs, and traces across 12 backends. This skill should be used when the user runs obz commands, constructs metric/log/trace queries, configures providers, parses obz JSON output, or troubleshoots obz errors. Covers all core commands (metric query/list/info/labels/label-values/series, log search, trace search/get), global flags, time formats, output modes, config files, and exit codes.

Datadog provider for obz. Supports metrics (Datadog Query Language), logs (Datadog Log Query), and traces. Requires api-key and app-key configured under auth in config.yaml. Does not support metric labels, label-values, or series commands. This skill should be used when the user mentions "Datadog", "DD", "obz -p dd", Datadog Query Language, or needs to configure Datadog provider instances with regional endpoints.

Elasticsearch provider for obz. Supports log search and trace operations using Elasticsearch Query DSL (Lucene-based). Requires --index flag. This skill should be used when the user mentions "Elasticsearch", "obz -p es", or needs to search logs or traces from an Elasticsearch cluster.

Jaeger provider for obz. Covers trace search, trace retrieval by ID, and extension commands for service and operation discovery. This skill should be used when the user mentions "Jaeger", "obz trace -p jg", or needs to search distributed traces from a Jaeger backend.

Grafana Loki provider for obz. Covers log search using LogQL, a label-based query language for log aggregation. This skill should be used when the user mentions "Loki", "Grafana Loki", "LogQL", "obz log search -p loki", or needs to search logs from a Loki instance.

Grafana Mimir provider for obz. Covers all 6 metric commands using standard PromQL. Mimir is a horizontally-scalable Prometheus-compatible TSDB. This skill should be used when the user mentions "Mimir", "Grafana Mimir", "obz metric -p mimir", or needs to query metrics from a Mimir cluster.

OpenSearch provider for obz. Supports log search and trace operations using Lucene-based query syntax. Requires --index flag. This skill should be used when the user mentions "OpenSearch", "obz -p os", or needs to search logs or traces from an OpenSearch cluster.

Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.

Archive multiple completed changes at once. Use when archiving several parallel changes.

Continue working on an OpenSpec change by creating the next artifact. Use when the user wants to progress their change, create the next artifact, or continue their workflow.

Enter explore mode - a thinking partner for exploring ideas, investigating problems, and clarifying requirements. Use when the user wants to think through something before or during a change.

Fast-forward through OpenSpec artifact creation. Use when the user wants to quickly create all artifacts needed for implementation without stepping through each one individually.

Start a new OpenSpec change using the experimental artifact workflow. Use when the user wants to create a new feature, fix, or modification with a structured step-by-step approach.

Guided onboarding for OpenSpec - walk through a complete workflow cycle with narration and real codebase work.

Sync delta specs from a change to main specs. Use when the user wants to update main specs with changes from a delta spec, without archiving the change.

Analyze indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.

Submit a new issue to the project.

Submit a new Pull Request to the project.

Analyze new commits and generate related test cases.

Execute the implementation planning workflow using the plan template to generate design artifacts.

Create or update the feature specification from a natural language feature description.

Generate an actionable, dependency-ordered tasks.md for the feature based on available design artifacts.

Cerate or Update a GitHub Issue for each Module in the task document.

Run the MNN regression / CI suite for this fork — static checks, host-side (local) tests, and the on-device Android arm64 matrix — via the ./test.sh driver and test_stages.json. Use when the user asks to run the tests, run CI, smoke-test a build, verify a change on a device, benchmark on-device, or add / select / retune a test stage.

任务完成后的反思与经验沉淀。仅在任务非平凡且出现调试、失败修复、反复试错、明显误判或可复用教训时使用；简单执行、查询、常规 CI 通过等无新增经验的任务可跳过。

MNN Metal 后端 op/kernel 优化与扩展。覆盖 Metal shader 字符串嵌入流程、conv1x1 多 pipeline 选路、SIMD group reduce/matrix kernel、weightTransform CPU pack、Apple GPU 实测验证。

MNN OpenCL 后端 op/kernel 优化与扩展。覆盖 .cl + codegen 双轨、kernel 选路、packed weight 设计、tune 机制、Android 真机验证。

MNN Vulkan 后端 op/kernel 优化与扩展。覆盖 GLSL .comp + makeshader 双轨、conv1x1 dispatcher 多路径、coopMat/subgroup 路径、weight prepare/pack 流程、Adreno 真机稳定性。

MNN ARM CPU 算子和低 bit kernel 性能优化。重点覆盖正确性基线、C++ 标量 oracle、C++ SIMD 模拟、寄存器规划、ARM 汇编实现、dispatch/pack 集成、模型级回归和 roofline 性能分析。

为 MNN 框架添加新的 LLM 模型支持。支持从 HuggingFace/ModelScope 下载模型，分析架构，添加映射，Hook 对齐测试，导出 MNN 模型。采用 TDD 模式，分 6 步执行，每步有独立测试标准。

为 MNN 框架新增算子。包含 Schema 定义、形状计算、几何计算、后端实现、单元测试的完整 TDD 流程。分 5 步执行，每步有独立测试标准。

Runs .continue/checks locally against the current diff, simulating a PR review before pushing.

Deploys React/Vite applications to Alibaba Cloud ESA Pages. Use when the user needs to deploy frontend applications, manage deployments, configure custom domains, or troubleshoot deployment issues on ESA (Edge Security Acceleration) platform.

[project] OPC-Starter 测试驱动开发规范。管理和执行 Playwright E2E 测试、演示模式测试、单元测试的完整工作流。适用于新增功能时补充测试用例、运行认证/注册/演示测试、维护测试规划文档。当用户提到测试、E2E、Playwright、演示、用例、质量、补充测试时使用。

OPC-Starter 智能开发技能。AI 亲和的 React Boilerplate 项目开发规范，支持动态上下文感知、TDD 驱动开发、Agent Studio 扩展。适用于认证系统、组织架构、Agent 工具、数据同步等模块的迭代开发。

代码仓库 AI 亲和度审计工具，支持前端、后端、全栈等各类项目。此技能用于检查给定的代码仓库对 AI Coding 工具的友好程度，生成详细的分析报告和改进建议。当用户需要评估代码仓库是否适合 AI 辅助开发、希望提升仓库的 AI 可操作性、或准备引入 AI Coding 工具前进行仓库评估时，应使用此技能。支持 TypeScript/JavaScript、Go、Java/Kotlin、Python、Rust 等主流技术栈。融合 OpenAI Harness Engineering 方法论，评估 Outer Loop（反馈闭环、评估门禁、机械化不变量）建设。

OPC-Starter 智能开发技能。AI 亲和的 React Boilerplate 项目开发规范，支持动态上下文感知、TDD 驱动开发、Agent Studio 扩展。适用于认证系统、组织架构、Agent 工具、数据同步等模块的迭代开发。

IHS（IDE Harness Score）仓库健康评估技能。用于衡量 AI Coding IDE 驾驭能力下的代码仓技术债变化趋势，输出 IHS.md 评测报告（代码腐化、测试信号、文档对齐、变好/变坏结论）。

Create distinctive, production-grade frontend interfaces with high design quality using Mobile First strategy. Analyzes design systems from screenshots, generates Tailwind CSS + Shadcn UI configurations, and retrofits existing projects. Optimized for Claude Opus 4.5 with structured decision trees and self-verification checkpoints.

Prerequisites and connection details for attaching to a live Python process with PyFlightProfiler. Read this skill first before using any other PyFlightProfiler command (watch, trace, stack, perf, etc.).

Inspect Python module global field or class static field value in a running Python process. Use this for safe, read-only inspection of global variables, configuration objects, or class-level state.

Translate a file path to its Python module name in a running Python process. Use this when you know a file path but need the module name for commands like watch, trace, or reload.

Hot-reload a Python function implementation from the latest file content without restarting the process. Use this to apply code fixes live after identifying a bug with watch/trace.

Inspect stack frames of a running Python process including thread stacks, native C-level frames, and async coroutine stacks. Use this to see what each thread is currently doing and diagnose hangs, deadlocks, or stuck async tasks.

Trace execution time of Python method invocations in a live process with call stack visualization. Use this to see a hierarchical call tree showing which sub-calls are slow and where time is spent within a function.

Inspect live Python class instances in a running process — find all instances of a class, examine their attributes/state, or invoke methods on them. Also supports forcing GC. Use this when you need to probe object-level runtime state beyond what watch/getglobal can provide.

Display input/output args, return object and cost time of a Python method invocation in a live process. Use this to observe function behavior at runtime — see what arguments go in, what comes back, and how long it takes. This is the most frequently used diagnostic command.

Sample skill fixture for classpath registry enhancement tests.

商品文案写作助手。根据商品信息生成吸引人的营销文案。当用户提到"写文案"、"商品描述"、"营销文案"时使用此技能。

选品分析助手。根据市场趋势和用户需求，分析并推荐适合的商品品类。当用户提到"选品"、"商品推荐"、"品类分析"时使用此技能。

Database schema and business logic for inventory tracking including products, warehouses, and stock levels.

Database schema and business logic for sales data analysis including customers, orders, and revenue.

Test skill for groupedTools. When executing this skill, use the record_result tool to record the result value.

Extract text, tables, and form data from PDF documents for analysis and processing. Use when user asks to extract, parse, or analyze PDF files.

End-to-end GPU kernel implementation and optimization router. Use this skill to turn PyTorch logic into a high-performance kernel or to systematically optimize an existing kernel. It initializes the local knowledge base, identifies the current phase, and routes work to baseline implementation, bottleneck analysis, and profile-driven optimization sub-skills.

Learn the target framework from gpu-wiki and implement a baseline GPU kernel. Use this skill to understand compute semantics, determine the target platform and framework, search reference implementations, and produce a correct V0 baseline with performance records for later profile-driven optimization.

Helper skill for GPU kernel bottleneck analysis. It provides Roofline analysis, same-size bandwidth baseline measurement, TFLOPS and bandwidth utilization calculation, and profiling evidence extraction. This skill is not a standalone stage; its capabilities are reused by Step 0 and by the first Stage 2 iteration.

Final output contract for GPU kernel optimization tasks. Use this skill when a verified implementation must be packaged as generated_kernel.py for hidden evaluator import, especially when a stop hook blocks because the final candidate file is missing or non-compliant.

Partial-restart workflow for GPU kernel optimization when no new optimization direction is available. The main agent masks half of the optimization memory, then launches a fresh subagent that reads README.md and unmasked memory to extract experience, takes the latest kernel.py as the starting point, and continues optimization through the gpu-kernel-profile-optimizer workflow.

Profile-driven GPU kernel iterative optimization skill. Use this skill to run a closed loop in a temporary git workspace: profile evidence extraction, evidence-driven search and planning, single-category optimization, validation, memory update, and git commit.

排查 JVM / 应用 CPU 飙高（线程定位 + 代码路径分析）

使用 Arthas 的 watch/trace 获取 EagleEye traceId / 获取请求的 traceId

arthas 诊断 java应用，jvm问题 skill

排查 Spring ApplicationContext / Bean / 配置注入等问题