216 件 ・ 関連度順
概要と使いどころ
Analyze images using Dashscope (Qwen) Vision models for detailed description, OCR text extraction, object recognition, and visual Q&A. Use when the user needs to understand image content via Alibaba Cloud Dashscope API, especially for Chinese-language image analysis and documents.
日本語の概要は準備中です。原文の説明を表示しています。
Classify XQUIC GitHub issues with an evidence-backed L1-L4 resolution-complexity label while keeping validity, priority, severity, and disposition separate and skipping uniquely labeled issues unless the user explicitly forces a recheck. Use when triaging one issue or a batch of issues, creating an issue-complexity backlog, or applying or correcting L1-L4 GitHub labels.
日本語の概要は準備中です。原文の説明を表示しています。
Verify whether a proposed XQUIC bug issue is real by checking the cited RFC or Internet-Draft mechanism against the repository's actual source behavior, tracing any root cause to current file and line evidence, and returning a fail-closed boolean gate. Use when validating an issue draft, investigating a claimed QUIC, HTTP/3, QPACK, or MoQT protocol violation, or before issue-submit may publish an issue.
日本語の概要は準備中です。原文の説明を表示しています。
Safely stage, commit, and push XQUIC changes after scoped Git checks. Use when asked to stage, commit, push, publish a branch to a fork, or verify that only intended files will be sent to a remote.
日本語の概要は準備中です。原文の説明を表示しています。
Prepare and run XQUIC local compilation. Use when asked to compile, build, configure, or diagnose the local build environment for XQUIC, especially across macOS/Linux. Detect intent and platform, then delegate actual compilation to scripts/validate.sh instead of hand-writing CMake commands.
日本語の概要は準備中です。原文の説明を表示しています。
Enforce XQUIC test coverage and local validation gates. Use when code, build, or test changes need verification; before commit or pull request; or when asked to build, test, validate, or verify XQUIC. Requires paired happy-path and abnormal-path unit tests, the complete local unit suite, and explicit endpoint coverage or gap reporting for production behavior changes.
日本語の概要は準備中です。原文の説明を表示しています。
Address actionable GitHub pull request review comments for xquic. Use when asked to handle PR review feedback, requested changes, unresolved inline threads, or reviewer comments while preserving unrelated edits.
日本語の概要は準備中です。原文の説明を表示しています。
Fail closed on published draft XQUIC code pull requests before ready-for-review state. Use when asked to pre-review a code PR or move it out of draft; verify current base-to-head code, protocol conformance, validation coverage, adversarial bad cases, hot-path performance, and memory safety in a five-part local report.
日本語の概要は準備中です。原文の説明を表示しています。
Review XQUIC harness structure for bloat, duplicated facts, trigger overlap, reference drift, and missing hard checks. Use when changing AGENTS.md, harness specs, pipelines, skills, setup scripts, validation routing, or when asked to audit harness design.
日本語の概要は準備中です。原文の説明を表示しています。
Debug and fix failing GitHub PR checks for xquic. Use when a PR, branch, or commit has failed CI, GitHub Actions, build checks, test checks, or requested validation failures that should be diagnosed from logs before editing.
日本語の概要は準備中です。原文の説明を表示しています。
Compare job levels (职级) across Chinese technology companies and against international ladders, to judge an offer, a job move or a promotion target: what a level usually means in scope and expectations, how levels roughly line up, and what to ask the recruiter. Use when asked P7 相当于什么级别, 跳槽职级怎么对标, 字节 2-2 对应阿里什么, 这个 offer 的职级合理吗, compare Chinese tech levels, or how does Alibaba P-level map to Google levels. Produces a level comparison built from the person's own ladders and offer details, a scope-and-expectation profile for each level, the negotiation questions to ask, and a clear note on what is approximate.
日本語の概要は準備中です。原文の説明を表示しています。
Manual intent elicitation skill. Use when the user asks to clarify a fuzzy idea, run an elicitation session, design an agent/skill/workflow, identify strategic tension, do a postmortem, align taste/brand direction, or turn vague judgment into an actionable Intent Brief. The skill asks 3–5 sharp questions first, then compiles answers into an Intent Brief and routes the result into system/workflow change. Never run automatically or on a schedule.
日本語の概要は準備中です。原文の説明を表示しています。
Ask for missing deployment settings and summarize the requested configuration without deploying it.
日本語の概要は準備中です。原文の説明を表示しています。
Capture and review Agent Skill observations, and create, run, diagnose, or iteratively improve Skill evaluations (evals) with the skill-up CLI. Use when the user asks to record explicitly attributed Skill usage or feedback; review observations or user-supplied traces/transcripts; turn an approved observation into a regression case; evaluate, test, regress, verify, fix, improve, iterate, or evolve a Skill; add or strengthen eval cases; write eval.yaml/case.yaml; run skill-up run/validate/list-cases/report/import/init; or migrate from Anthropic evals.json. Plugin observation capture/review require either the Codex skill-up plugin or an observer-enabled DSH skill-up plugin. User-supplied external evidence can be reviewed without a plugin; evaluation remains multi-engine.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Decode the marker fixture used by the DeepSeek Harness integration example.
日本語の概要は準備中です。原文の説明を表示しています。
Use when generating or editing video with Wan models on Scenario via MCP: text-to-video, image-to-video from a still, first and last frame brackets, extending a clip, instruction-based video editing, character motion transfer or person replacement (Animate), aspect ratio reframing, video outpainting, uploaded-audio sync or auto-generated audio, or multi-shot prompts with timing brackets. Keywords: Wan 2.7, 2.6, 2.5, 2.2, Alibaba, VACE, T2V, I2V, V2V, lip-sync, multiShots.
日本語の概要は準備中です。原文の説明を表示しています。
Dung khi doanh nghiep Viet Nam muon ban ra NUOC NGOAI theo duong B2B — ho so nang luc tieng Anh, chon thi truong muc tieu, pitch deck cho buyer, email chao hang quoc te, chuan bi hoi cho thuong mai va kenh Alibaba, Amazon Business. Kich hoat khi user nhac 'xuat khau', 'ban hang nuoc ngoai', 'tim buyer quoc te', 'company profile tieng Anh', 'email chao hang export', 'di hoi cho thuong mai', 'khach nuoc ngoai hoi gia', 'lam sao co don hang xuat khau'. Khong dung cho — tim va nuoi lead B2B trong nuoc thi dung skill 33-b2b-lead-gen; bo tai lieu ban hang va pipeline thi dung skill 71-sales-enablement; chuoi email nuoi duong thi dung skill 14-email-marketing.
日本語の概要は準備中です。原文の説明を表示しています。
为 ROCK 项目管理 Docusaurus 文档版本。支持添加新版本文档、添加 Release Note、管理中英文 i18n 文档。当用户说"添加文档版本"、"发布新版本文档"、"添加 release note"、"rock-docs"或需要操作 docs/ 目录下的版本化文档时使用。
日本語の概要は準備中です。原文の説明を表示しています。
Read from a UModel object-graph semantic layer with the `umctl` CLI (MCP alternative noted). Three kinds of read: (1) entities & relationships / topology (`.entity`, `.topo`) and (2) the model itself (`.umodel`, and `.entity_set` methods, including entity-linked `list_skills`) return real rows; (3) metrics & logs (`get_metrics` / `get_logs`) return an executable *plan* — PromQL / Elasticsearch DSL with the entity id pre-substituted — that you run against the backend. Against a PaaS endpoint the same calls return data rows instead of a plan. Use to query or read UModel entities, relations, topology, or model metadata; to read a service's metrics or logs; to look up services and dependencies; or to discover what objects, datasets, and methods exist. For root-cause analysis on top of these reads, see the `umodel-rca` skill. Triggers: UModel, object graph, .entity / .topo / .umodel / .entity_set, query entities, read topology, read metrics / logs, get_metrics / get_logs, list services / dependencies / datasets / skills, 实体查询, 关系/拓扑查询, 读模型, 读指标, 读日志, 查指标, 查日志, 查服务依赖.
日本語の概要は準備中です。原文の説明を表示しています。
Model-guided autonomous root-cause analysis over a UModel object-graph semantic layer. Use with `umodel-query` when diagnosing service degradation, SLO breach, latency/error spikes, incidents, outages, or questions like why a service is slow, degraded, erroring, or timing out. The skill guides evidence collection across object graph, metrics, logs, topology, runbooks, deployments, config changes, business traffic, and runtime capacity; it is especially useful for ruling out red herrings such as recent but unrelated deployments. Triggers: root cause, RCA, incident, SLO breach, outage, postmortem, latency spike, error spike, timeout, 根因分析, 故障排查, 告警定位, 为什么慢, SLO 击穿.
日本語の概要は準備中です。原文の説明を表示しています。
Use when an EntitySet exposes list_skills, a user requests a UModel or RunbookSet Skill, or an entity task supplies SKILL.md. Triggers: list_skills, list_knowledge, RunbookSet Skill, UModel Skill, execute skill, run skill, 执行技能, 运行技能, Runbook 技能.
日本語の概要は準備中です。原文の説明を表示しています。
Produce text-to-speech with Alibaba/Qwen Qwen3-TTS through DashScope/Model Studio or open-weight Qwen3-TTS checkpoints. Use when an agent must choose Qwen3-TTS models, voices, realtime versus non-realtime synthesis, voice cloning, voice design, instruction/prosody controls, multilingual or dialect speech, audio artifact handling, regional routing, pricing/privacy/licensing constraints, or production QA for narration, characters, assistants, audiobooks, ads, localization, or voice-enabled media.
日本語の概要は準備中です。原文の説明を表示しています。
当任务需要创建、修改、扩展或重组阿里云 SLS 的 dashboard JSON 或可导入的大盘配置时使用；尤其适用于线上大盘、强对比的分析看板、已校验的查询包，或需要专业中文标签与指标定义的运维向大盘。
日本語の概要は準備中です。原文の説明を表示しています。