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概要と使いどころ
Use when reviewing scripts, client components, bundles, or runtime behavior related to Parse JSON safely with error handling. Inspect both source code and the browser execution path so fixes target the real bottleneck or bug.
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 preparing, tagging, and publishing a Cline desktop app (apps/examples/desktop-app) release — stable (desktop-vX.Y.Z from main), beta (desktop-vX.Y.Z-beta.N from desktop-experimental, shipped as the side-by-side "Cline Beta" app), or nightly (artifact-only test build of main's HEAD, tagged desktop-nightly-<stamp> after it builds). Guides changelog drafting, version bumps in package.json + tauri.conf.json, tagging, and the desktop-publish GitHub workflow that builds, signs, notarizes, and updates the per-channel auto-update feed.
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。
Create and edit JSON Canvas files (.canvas) with nodes, edges, groups, and connections. Use when working with .canvas files, creating visual canvases, mind maps, flowcharts, or when the user mentions Canvas files in Obsidian.
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。
Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.
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。
Audit MCP (Model Context Protocol) server configurations for security issues. Use this skill when: - Reviewing .mcp.json files for security risks - Checking MCP server args for hardcoded secrets or shell injection patterns - Validating that MCP package-runner dependencies use exact reviewed versions, not bare names, @latest, or ranges - Detecting mutable npm/npx, bunx, pnpm dlx, yarn dlx, and npm exec references in MCP configurations - Auditing which MCP servers a project registers and whether they're on an approved list - Checking for environment variable usage vs. hardcoded credentials in MCP configs - Any request like "is my MCP config secure?", "audit my MCP servers", or "check .mcp.json" keywords: [mcp, security, audit, secrets, shell-injection, supply-chain, governance]
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。
Triage npm packages and lockfiles for install-script malware, credential exfiltration, and worming behavior using GuardDog, manual tarball inspection, and dynamic detonation with network/filesystem monitoring. Use when vetting a new dependency, reviewing a package.json/package-lock.json during code review, checking lockfiles against a supply-chain advisory's known-bad versions, or investigating a host suspected of installing a trojanized package.
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。
Installs and runs the kube-bench tool against a Kubernetes cluster as a Job, DaemonSet, or standalone binary, selecting the correct benchmark version and targets (control plane, etcd, kubelet, worker nodes) and emitting JSON or JUnit output for pipelines. Use when setting kube-bench up for the first time, choosing which benchmark version and node targets to run, wiring it into CI, or troubleshooting skipped or misdetected checks. Keywords: kube-bench, DaemonSet, --benchmark, --targets, JSON output, JUnit, CI integration. Do not use for interpreting the findings or producing an audit report - use performing-kubernetes-cis-benchmark-with-kube-bench.
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 working with *.excalidraw or *.excalidraw.json files, user mentions diagrams/flowcharts, or requests architecture visualization - delegates all Excalidraw operations to subagents to prevent context exhaustion from verbose JSON (single files: 4k-22k tokens, can exceed read limits)
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。
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
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 structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
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。
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework
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。
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
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。
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.
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 the user needs to query, filter, reshape, extract, create, or construct JSON data — including API responses, config files, log output, or any structured data — or when helping the user write or debug JSON transformations.
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。
Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, and baseline placeholders you can edit before publishing or testing. Use when Codex needs to create a new local plugin, add optional plugin structure, or generate or update repo-root `.agents/plugins/marketplace.json` entries for plugin ordering and availability metadata.
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。
Summarizes WeChat group chat highlights into a structured digest using the local wx-cli binary (https://github.com/jackwener/wx-cli). Generates a normal digest by default; a roast (毒舌) version is opt-in. Maintains per-group history (history.json + history-digests.jsonl), per-user profiles, and per-group fact memory (memory.md) across runs, with privacy guardrails baked in. Use when the user asks to "总结群聊", "群聊精华", "群聊摘要", "summarize group chat", "group chat digest", mentions a WeChat group name with a time range, says "帮我看看 XX 群最近聊了什么", "XX 群有什么值得看的", or asks to "回溯画像" / "初始化画像" / "backfill profiles". Adds the roast version when the user says "毒舌版", "roast 版", "再来个毒舌的", or similar.
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。
Write JavaScript or Python for the n8n Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode) — the AI-agent-callable tool, NOT the workflow Code node. Use when building a Code Tool attached to an AI Agent, writing code that an LLM will invoke, parsing the `query` input, returning a string result, defining an input schema for structured arguments (specifyInputSchema, jsonSchemaExample, DynamicStructuredTool), or troubleshooting errors like "Wrong output type returned", "No execution data available", "The response property should be a string, but it is an object", "Cannot assign to read only property 'name'", or an AI agent that refuses to call the tool. Covers the critical differences between Code node and Code Tool: return format (string vs `[{json:{...}}]`), unavailability of `$fromAI`/`$input`/`$helpers` in the Code Tool sandbox, naming rules for AI invocation, and when to use `toolWorkflow`/HTTP Request Tool instead.
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。
Drive Anthropic's Claude Code CLI (`claude -p`) as a non-interactive coding sub-agent from inside Codex. Use when you want to delegate a coding/analysis/refactor task to Claude, get a second opinion / adversarial review from another model, hand off long-context planning, or fan out parallel agents. Covers the exact `claude -p` flags, permission modes, output capture, JSON/stream-json modes, session resume, parallel fan-out, and the mandatory "delegate → capture → independently verify, never trust the self-report" supervision loop.
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。
Generates production-ready TypeScript CLIs via a 3-tier template system (manual arg parsing, Commander.js, oclif), each shipping full implementation, docs, package.json, strict config, JSON output, and exit-code compliance. USE WHEN create CLI, build CLI, command-line tool, wrap API, add command, upgrade tier, TypeScript CLI. NOT FOR LifeOS skill scaffolding (use CreateSkill).
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。
Reads and summarises ScenarioRunner output — the criteria pass/fail tables from --output/--file, the machine-readable --json and --junit result files, and the criteria JSON written alongside a --record recording — and runs the metrics module (metrics_manager.py) to compute custom measurements over a recorded run offline, without the simulator. Use when the user asks "did the scenario pass", "why did it fail", "summarise these results", "compare these runs", or wants distance/speed/lane metrics from a recording.
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。
Reads a CARLA Leaderboard results.json — per-route driving score, route completion, infraction penalty and the infraction lists — explains how the score was computed for that leaderboard version, and rescores an existing result under the other version's rules (2.0 multiplicative vs 2.1 additive) without re-running anything. Also diffs two runs and merges sharded result files. Use when the user asks "what did I score", "why is my score low", "did my agent pass", "why don't my scores match the leaderboard", or hands over a results.json.
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。
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
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。
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework
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 structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
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。