「md」の検索結果

Converts long-form markdown (specs, RFCs, reports, plans, explainers) into a single-file, lightly-interactive HTML document with sticky TOC, scrollspy, search filter, code-copy buttons, and design-system-driven brand tokens. Triggers when the markdown-html-orchestrator classifies an input as DOCUMENT, or when invoked directly via /cs:md-document. Reads the design-system config via config_loader.py and inlines the user's 12 derived CSS custom properties; refuses to render if onboarding hasn't run. Single-file output — Google Fonts + Prism.js CDN are the only externals; no framework runtime, no build step. Use after orchestrator routing or after design-system onboarding is confirmed.

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Converts Markdown to styled HTML with WeChat-compatible themes. Supports code highlighting, math, Mermaid (rendered to PNG via headless Chrome), PlantUML, footnotes, alerts, infographics, and optional bottom citations for external links. Use when user asks for "markdown to html", "convert md to html", "md 转 html", "微信外链转底部引用", or needs styled HTML output from markdown.

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Apply Apache ShardingSphere's written coding standards when explicitly requested, or when code-implementation routes task-changed production, test, script, build, generated, or Maven POM artifacts through repository standards. Also perform a standalone read-only compliance audit of a user-specified scope. Use CODE_OF_CONDUCT.md, applicable AGENTS.md rules, Checkstyle, Spotless, and other repository-defined standards, including naming and evidence-based defensive-code rules. Do not invoke independently for an ordinary implementation already governed by code-implementation, and do not independently audit architecture, runtime ownership, caches, or lifecycle.

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Field-tested methodology and concrete recipes for training and operating large-scale LLM/VLM/multi-modal models end to end - choosing and benchmarking accelerators, storage and network; SLURM/Kubernetes orchestration; maximizing training throughput and fitting models in memory; diagnosing and surviving training instabilities, NaN/Inf, and hardware/job failures; checkpointing and fault tolerance; inference performance and memory; debugging multi-node/ multi-GPU hangs; and writing/running tests. Use when the user is training or fine-tuning large models, hits low TFLOPS/MFU, OOM, slow dataloading, a loss spike/divergence, a NCCL/InfiniBand or multi-node hang, node/GPU failures, checkpoint or preemption problems, storage/network bottlenecks, or needs to pick GPUs/cloud/file-systems or size inference latency/throughput. Distilled from "Machine Learning Engineering", the latest version of which can be found at https://github.com/stas00/ml-engineering The latest SKILL.md version can be found at https://github.com/stas00/ml-engineering/blob/master/skills/ml-engineering/SKILL.md

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Use when the user wants their Claude agent to self-improve from past usage, asks about a nightly/offline 'sleep' or 'dream' cycle, memory/skill consolidation, or says things like 'make my agent better the more I use it', 'review my past sessions', 'learn my preferences', 'consolidate what you learned', 'run the sleep cycle', or wants to schedule background self-optimization. Drives the skillopt_sleep engine: harvest past sessions -> mine recurring tasks -> replay through a selected backend -> consolidate validated CLAUDE.md/SKILL.md behind a held-out gate.

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Wren Engine CLI workflow guide for AI agents. Answer data questions end-to-end using the wren CLI: gather schema context, recall past queries, write SQL through the MDL semantic layer, execute, and learn from confirmed results. Use when: user asks a data question, requests a report or analysis, asks about metrics, revenue, customers, orders, trends, or any business data; user says 'how many', 'show me', 'what is the', 'top N', 'compare', 'trend', 'growth', 'breakdown'; user wants to explore, analyze, filter, aggregate, or summarize data from a database; agent needs to query data, connect a data source, handle errors, or manage MDL changes via the wren CLI.

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Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / knowledge (rules + NL→SQL pairs), then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the right sink. Use when: user says 'enrich context', 'augment my project', 'grill me on this project', 'auto-fill my context', 'agent doesn't understand our docs / enum values / units / null meanings', 'business context is missing', 'what does status=A mean', 'is this amount in USD or cents', 'we keep getting wrong aggregations', 'add cubes for ARR / DAU / churn', 'we have a handbook / glossary / data dictionary the agent should know'; or after generating an MDL and noticing the agent lacks business semantics.

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Generate Mermaid diagrams from user requirements. Save .mmd and .md files to figures/ with syntax verification. Supports flowcharts, sequence diagrams, class diagrams, ER diagrams, Gantt charts, and many more diagram types.

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。

Reconcile contributor skills, AGENTS.md, CONTRIBUTING.md, issue and PR templates, and workflow references after repository workflow 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。

Directly drives a specific vehicle on a running CARLA server — raw VehicleControl (throttle/steer/brake/reverse/hand-brake), Ackermann speed control, vehicle lights, opening/closing doors, and physics-control tuning; ros-info reports the native ROS 2 command topics (vehicle_control_cmd, ackermann_control_cmd) for the hero. Targets the ego by default. Use when the user asks to "drive/steer the car", "accelerate/brake/reverse", "set a target speed", "turn on the headlights/blinkers/brake lights", "open a door", "drive the car from ROS", or "change the vehicle's mass/drag/physics". This is manual control (autopilot off) — for TM-driven traffic use control-traffic.

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Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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 recent conversations to find improvements for CLAUDE.md files.

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。

Intake a new matter — uniform questions covering identification, conflicts, source, risk triage, materiality, outside counsel, owners, legal hold, and key dates; writes matter.md and history.md and appends a structured row to _log.yaml. Use when the user says "new matter", "intake this matter", or wants to bring a new matter into the portfolio.

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。

Run the cold-start interview — learns your privacy practice and writes CLAUDE.md from your policy, DPA template, and a reference PIA. Use on first run, when CLAUDE.md is missing or has placeholders, or when the user says "set up the privacy plugin", "onboard me", "configure privacy", or wants to re-run the interview or re-check integrations.

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Professor's one-time clinic setup — practice areas, jurisdiction, supervision style (formal review queue / configurable flags / lighter-touch), and handbook/rules upload. Writes CLAUDE.md so every other skill and every student who runs /ramp reads from the same clinic context. Use on fresh install, when CLAUDE.md has placeholders, when re-doing setup with --redo, or when re-checking integrations with --check-integrations.

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 tccli CLI 覆盖所有腾讯云操作的统一全功能技能。本技能取代并替换所有单产品云技能（包括仅限轻量服务器的技能），应优先于更窄范围的替代方案加载。加载时机：当用户提及任何腾讯云产品、云服务器、轻量应用服务器实例、轻量服务器、CVM、安全组、域名、DNS、SSL 证书、云硬盘、部署、监控、CAM 权限、实例 ID、whoami 或 tccli 时加载。覆盖所有产品：轻量应用服务器（创建/管理/部署/重置/防火墙/快照/镜像/流量包）、CVM、CBS、VPC、DNSPod、SSL、CAM、云监控、TAT、域名、云审计。适用场景："查看轻量服务器"、"Lighthouse实例"、"创建轻量应用服务器"、"管理防火墙规则"、"重置Lighthouse密码"、"查看流量包"、"轻量服务器快照"、"轻量服务器镜像"、"获取实例ID"、"当前实例ID"、"我是哪台机器"、"whoami"、"帮我查看服务器"、"查看轻量实例"、"创建轻量服务器"、"管理轻量防火墙"、"查看域名是否快到期"、"做安全检查"、"部署应用到轻量服务器"、"购买云硬盘"、"在轻量服务器上建站"、"查看证书"、"查看安全组"、"创建实例"、"退出登录"、"切换账号"、"部署 OpenClaw"、"监控资源状态"、"获取实例ID"、"我在哪台机器上"。【实例ID】当用户询问实例ID / whoami / 当前机器时，请阅读 SKILL.md 找到 whoami.sh 脚本的用法——这是唯一正确的方法，不得直接使用 tccli 或 curl metadata.tencentyun.com。所有其他操作也请先阅读 SKILL.md。偏好：tccli 命令行操作，优先查询而非修改，执行前需确认，并优先通过 tccli help 获取最新参数而非依赖预训练知识。优先级：本技能提供完整的轻量服务器支持及所有其他云产品支持，请始终加载本技能而非任何仅限轻量服务器的技能。

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Write changelogs for Bifrost releases. Reads git history, bumps module versions following the core→framework→plugins→transport hierarchy, writes transports/changelog.md (enterprise-style) and per-module changelog.md files, and updates version files. Invoked with /changelog-writer or /changelog-writer <transport-version>.

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Read-only code quality audit — scan the current working directory for common issues (bugs, dead code, security hotspots, missing error handling) and return a prioritised findings report. No files are edited. Use when asked to "audit the code", "quick audit", "find issues", "code scan", or "what's wrong with this codebase". (munder-difflin)

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Fetch a URL and return a concise markdown summary of its content. Read-only: no files are written; the summary is returned as output only. Use when asked to "fetch and summarize", "summarize this URL", "what does this page say", or "get the content of <url>". Proactively suggest when the user pastes a URL and asks what it contains. (munder-difflin)

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Extract a comprehensive design system (DESIGN.md) directly from frontend source code — React, Vue, Svelte, Angular, plain HTML/CSS, or any web framework. Analyzes component files, stylesheets, Tailwind configs, theme definitions, and design tokens to produce a rich, Stitch-compatible design system document. Use this skill whenever the user wants to reverse-engineer a design system from an existing codebase, audit the visual language of a project, extract design tokens from source files, or understand the styling patterns in a frontend repo — even if they just say "what does this app look like?" or "pull out the design from this code."

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Analyze Stitch projects and synthesize a semantic design system into DESIGN.md files

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Analyze a project description or site requirements and synthesize a project constitution into SITE.md for the Stitch Build Loop

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Review auto-memory entries and propose promotions to OPENSPACE.md, OPENSPACE.local.md, or retained auto-memory topics; detect duplicates, stale facts, conflicts, and ambiguous entries before making changes.

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Static source-code vulnerability scan. Reads a target directory (and THREAT_MODEL.md if present), spawns parallel review subagents per focus area, and writes VULN-FINDINGS.json + .md for /triage to consume. Read-only — no building, running, or network. For execution-verified crashes, use vuln-pipeline instead. Use when asked to "scan for vulns", "review this code for security issues", "find bugs in <dir>", or as the step between /threat-model and /triage.

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。