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概要と使いどころ
专门用于将 Excel (.xlsx, .xls) 和 CSV (.csv) 文件转换为高度格式化的 Markdown 文档。 该工具具有内置的结构解析逻辑，能够自动识别表头、合并单元格并优化长文本换行。 当用户需要将表格数据转化为文档报告或在 Markdown 环境中展示数据时，**必须优先调用此工具**，以确保格式的准确性和视觉上的专业性，避免普通代码分析产生的格式错乱。
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。
Manages Volcano Engine (Volcengine/火山引擎) ESCloud and CloudSearch clusters for lifecycle operations and Elasticsearch/OpenSearch data-plane tasks. Use when the user mentions ESCloud, CloudSearch, or Elasticsearch/OpenSearch on Volcengine.
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。
使用 Seedance 2.0 "三层骨架" 方法论为电商商品生成高质量视频提示词。当用户想要从商品图片（单图或多图）创建视频提示词、需要专业商品展示视频脚本，或希望将静态商品照片转换为吸引人的视频内容时使用。
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。
Check refund, complaint, and sensitive customer-service actions against financial-service policy. Use before creating refund work orders, promising settlement times, exposing account data, or responding to complaints.
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。
深度研究代理系统社区版，对复杂主题进行多轮迭代的网络搜索和综合分析。使用场景：当用户需要对特定主题进行深入、全面的研究时，特别是需要基于最新网络信息生成详细分析报告的情况。支持多轮迭代搜索、智能搜索方向调整和综合分析报告生成。本skill是一个工作流描述文档，没有执行脚本，依赖byted-web-search skill进行网页搜索。
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。
Guide a user conversationally through generating a new ad video from a template video and a user-provided product image. The host agent performs video understanding, detailed analysis, and rewrite planning; BytePlus Seedance is used only for final video generation with local product images as generation references.
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。
ByteHouse 数据质量检查工具。当用户提供集群连接信息、数据库名和表名，需要检查排序键、主键和分区键所使用的列的空值、零值情况，是否存在异常分布，以及排序键、主键的重复情况时，使用此技能。适用于 "数据质量检查"、"排序键 / 主键 / 分区键分析"、"空值分析"、"零值分析"、"重复键检查"、"Top 分布" 等诉求。
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。
火山引擎 AntiDDoSPro 高防域名只读巡检。用户要查域名健康、攻击、流量、CC/WAF/区域封禁、智能防护或 CCAI 状态时使用。
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。
智能美颜SKILL：分析用户输入的图片，对画面中的人智能美颜，输出美颜后的图片，支持单张JPG、PNG格式，也支持多张图片URL或批量处理压缩包中的图片文件。当用户提及美颜、美化、美白、磨皮、瘦脸、人像美化、照片美颜、自动美颜、AI美颜时触发使用
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。
通过本地 Python CLI 和 OpenAPI 客户端管理、排查火山云手机资源。适用于查询实例和资源、截图、执行命令、查看任务、检查应用、主机和机房容量、标签、DNS、路由，以及操作已授权的测试云手机实例。
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。
抖音/短视频平台游戏视频裂变、克隆工具。支持复制热门游戏视频结构、文案、风格生成同款内容。当用户提及"游戏视频裂变、游戏视频克隆、复制游戏视频、克隆游戏视频、同款游戏视频"，或表达希望制作和某热门游戏视频一样的内容的等价意图时使用本技能。
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 a high-confidence, CONCISE implementation / architecture / strategy PLAN that follows the OMC plan system end to end. It first runs OMC's real requirements interview (auto-chains the `omc-plan` skill: one question at a time, explore-first, Analyst consult) to produce an initial plan in `.omc/plans/`, then DEEPENS that plan with a 3-round ITERATIVE fusion loop — each round an Opus 4.8 panelist (Agent subagent) and a GPT-5.5 panelist (via codex) critique/improve it IN PARALLEL and BLIND, then Opus 4.8 (the orchestrator) judges and synthesizes one tighter plan that seeds the next round, stopping early on convergence. Writes the final dense plan back to the same `.omc/plans/<slug>.md`, then offers the OMC handoff (`/omc-plan --review` → `/team` or `/ralph`). Use for high-stakes planning, architecture decisions, strategy calls, and hard debugging — especially when the user runs /plan or asks how to approach a non-trivial build. Reuses the `fusion` skill's run_codex.sh + judge rubric. NOT for trivial tasks (single pass is enough).
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 user asks to compare masters, compare schools, compare perspectives, 对比, 各宗怎么看, 不同宗派, 禅净之争, 性相之辩, 空有之争, or wants multiple masters to answer the same question. Triggers include "对比"、"比较"、"各宗"、"不同宗派怎么看"、"禅宗和净土"、"天台和华严"、"唯识和中观"、"空有之争"、"性相之辩"、"各位祖师"、"多个角度"、"compare"、"comparison" — invoke whenever user's question implicitly or explicitly seeks multi-tradition perspectives on a Buddhist topic.
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 you need to implement or improve distributed tracing with OpenTelemetry in Java — including trace/span modeling, context propagation, semantic conventions, span attributes/events/status, sampling strategy, baggage usage, privacy safeguards, and backend integration with OTLP collectors. This should trigger for requests such as Improve tracing; Apply OpenTelemetry tracing; Add distributed tracing; Refactor tracing instrumentation; Instrument Java services with OpenTelemetry spans. Part of Plinth Toolkit
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 you need to review, improve, or build Spring Boot 4.0.x applications — including proper usage of @SpringBootApplication, component annotations (@Controller, @Service, @Repository), bean definition and scoping, configuration classes and @ConfigurationProperties (with @Validated), component scanning, conditional configuration and profiles, constructor injection, @Primary and @Qualifier for multiple beans of the same type, bean minimization, graceful shutdown, virtual threads, Jakarta EE namespace consistency, and scheduled tasks. This should trigger for requests such as Review Java code for Spring Boot application; Apply best practices for Spring Boot application in Java code; Improve Spring Boot configuration properties and beans; Review Spring Boot component scanning and profiles; Tune Spring Boot graceful shutdown or virtual threads. Part of Plinth Toolkit
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。
Inspect a local or publicly accessible video through sampled scene frames, timestamps and a visual report, especially for motion and 3D reference analysis.
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。
Extract and document someone's authentic writing voice from samples. Use when someone needs a "voice guide," wants to capture their writing DNA, or needs to train AI to write in their style. Also useful for ghostwriting, brand voice documentation, or onboarding writers.
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。
Designs, sanity-checks and reads out product experiments (A/B tests, holdouts, pre/post, geo and incrementality tests) and produces an experiment brief plus a readout with a ship / hold / replicate call. Use when you ask 'should we A/B test this', 'how long should the test run', 'is this result real', 'sample size', 'stat sig', 'SRM', 'holdout', 'our win rate is low', or need to grade an existing test plan or readout. Draws on 191 insights from 71 Lenny's Podcast guests, led by Ronny Kohavi, Ramesh Johari and Archie Abrams.
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 auditing a codebase for semantic duplication - functions that do the same thing but have different names or implementations. Especially useful for LLM-generated codebases where new functions are often created rather than reusing existing ones.
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。
Build and govern a 50-200 example domain-specific held-out benchmark sampled from real traffic. Distinct from public benchmarks (MMLU/HumanEval/GSM8K via lm-evaluation-harness) which measure GENERAL capability. Only a held-out domain set predicts whether THIS system works on YOUR data. Collect real examples, label, hold out (never train/prompt on it), size 50-200, version it, refresh on drift.
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。
Pick an LM output format per (task x consumer x model) rather than by reflex: different formats carry different cognitive load (e.g. code-in-JSON makes the same model write worse code than plain-text+diff, while asking for prose when you need a typed object fails the other way). Use when designing or debugging an LM's output schema, choosing between plain text / diff / JSON / tool-call / grammar-constrained output, or when a model's quality drops after wrapping its output in a structured format.
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。
Decision rubric for promoting a prose prompt into a typed DSPy Signature. This is an ENHANCE overlay on top of the [[dspy]] library skill: it does NOT teach DSPy syntax — it answers the coder-agent decision "when do I stop hand-writing a prompt string and declare it as a `dspy.Signature`, and how do I name/describe its fields so the optimizer and the calling code both get a clean contract." Activate when: a prompt string grows past ~50 lines; the LM output is consumed by code (parsed, branched on, stored) rather than read by a human; the same prompt is reused across >1 call site; or a teammate asks "should this be a Signature?". Do NOT activate for one-shot throwaway prompts, or for HOW-TO questions about DSPy modules /optimizers/compile — defer those to the [[dspy]] skill and the [[agentsop-dspy]] workflow skill. Search keywords: typed prompt, structured prompt, DSPy Signature, prompt as a function, prompt contract, when to formalize a prompt.
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。
Split a multi-call LM workflow by cognitive load, not by accuracy: let one strong model make the few reasoning decisions and a cheap model do the many mechanical executions (Aider architect+editor, DSPy optimizer-LM vs task-LM, vLLM speculative draft+target, LangGraph supervisor+worker are the same shape). Use when designing or cost-optimizing a pipeline that calls an LM many times, when deciding which steps need a strong reasoner vs a cheap executor, or when adding an escalation valve for when the cheap tier degrades. Search keywords: reduce LLM cost, cheaper model, lower token cost, model cascade, route to cheap model, strong model plus cheap model, LLM cost optimization.
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。
A 5-minute gate the coder runs at project kickoff (and again whenever the repo shape changes). Classifies the workspace into Greenfield / Brownfield-large / Mid-size-familiar / Library-SDK, then maps the state to an agent strategy (autonomy, context primitive, tool choice). Use BEFORE picking Cursor vs Claude Code vs Aider, BEFORE turning on repo-map, BEFORE writing the first prompt. Skip only if the same repo was gated within the last day and nothing changed. Search keywords: greenfield vs brownfield, new project vs existing codebase, project setup strategy, legacy codebase agent, where to start a coding agent.
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。