「json output」の検索結果

Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.

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Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.

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。

Runs a Semgrep security scan over a codebase: detects languages, selects rulesets, presents the plan for explicit approval, then runs every approved ruleset through scripts/run-scans.sh, which batches the semgrep processes and writes scans.json, and merges the output to SARIF. Supports two scan modes, "run all" for full ruleset coverage and "important only" for security findings at medium-to-high confidence and impact. Uses Semgrep Pro for cross-file taint analysis when it is available. Use when asked to scan code for vulnerabilities, run a security audit with Semgrep, find bugs, or perform static analysis. For the same scan without the approval gate, use the /static-analysis:semgrep-scan workflow.

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 ChatGPT Apps MCP server codebase and generate chatgpt-app-submission.json with app info suggestions, tool hint justifications, test cases, and negative test cases, then report review-check findings and outputSchema warnings for submission review.

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Generate Excalidraw diagrams from natural language descriptions. Outputs .excalidraw JSON files openable in Excalidraw. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", "generate an Excalidraw file", "draw an ER diagram", "create a sequence diagram", or "make a class diagram". Supports flowcharts, relationship diagrams, mind maps, architecture, DFD, swimlane, class, sequence, and ER diagrams. Can use icon libraries (AWS, GCP, etc.) when set up. Do NOT use for code architecture analysis (use the architecture skills), Mermaid diagram rendering (use mermaid-studio), or non-visual documentation (use docs-writer).

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。

Handle files and binary data in n8n correctly. Use when working with files, images, PDFs, attachments, uploads or downloads, base64, vision/multimodal input, or when an AI agent needs a file as tool input or output — and whenever the user mentions $binary, binaryPropertyName, "read the PDF", "attach the file", "send the image", Merge losing binary, or a CDN for chat images. Covers the $binary vs $json split, reading/writing binary, keeping binary alive across transforms with Merge, the agent-tool binary boundary, and the CDN/URL requirement for chat surfaces.

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Use this skill before answering, planning, or editing whenever .NET tests or CI are switching from VSTest to Microsoft.Testing.Platform (MTP), or an MTP migration behaves differently. Triggers include "switch from VSTest"; MSTest/NUnit/xUnit MTP enablement; OutputType=Exe only for test projects in Directory.Build.props; EnableMSTestRunner, EnableNUnitRunner, UseMicrosoftTestingPlatformRunner, or YTest.MTP.XUnit2; .NET 10 global.json test.runner and TestingPlatformDotnetTestSupport; translating VSTest filters, logger, coverage, blame, or dump arguments; replacing VSTest@3; and exit code 8 or zero tests. Also use for xUnit v3 MTP filters during a v2-to-v3 upgrade. Do not use for framework conversion, TFM, UWP, or WinUI.

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Inspect GitHub pull requests, issues, workflow runs, and API data with the gh CLI. Use when the user wants repository facts from GitHub without opening the browser, especially for PR status, CI failures, issue lists, or 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。

Benchmark Jetson LLM/VLM serving performance across vLLM, llama.cpp, and Ollama with 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。

Use this skill when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary detection (GEBD) plus VLM classification. Trigger even if the user does not name it: verify operator step sequence, detect missing or out-of-order SOP steps, score factory/work-cell video for procedure compliance, run VLM-based SOP checking on industrial cameras, or call /v1/chat/completions with a file, RTSP, or Basler camera. Also trigger for its internals: SOPVideoProcessor, DeepStream GEBD model (e.g. DDM) via Triton CAPI, nvds_custom_postprocess, Cosmos Reason 1/2 vLLM, SSE streaming, Kafka NvProto/JSON output, Basler/Pylon camera + emulation, Docker compose, chunk-level latency. Do NOT trigger for generic DeepStream pipelines, object detection/tracking, NIM imports, or video summarization.

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。

Validate multi-skill workflows defined in CLAUDE.md by checking skill existence, inter-skill data contracts (JSON schema compatibility), file naming conventions, and handoff integrity. Use when adding new workflows, modifying skill outputs, or verifying pipeline health before release.

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Fetch upcoming economic events and data releases using FMP API. Retrieve scheduled central bank decisions, employment reports, inflation data, GDP releases, and other market-moving economic indicators for specified date ranges (default: next 7 days). The script outputs raw JSON or text; the assistant filters, assesses impact, and generates the Markdown report.

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Screen post-earnings gap-up stocks for PEAD (Post-Earnings Announcement Drift) patterns. Analyzes weekly candle formation to detect red candle pullbacks and breakout signals. Supports two input modes - FMP earnings calendar (Mode A) or earnings-trade-analyzer JSON output (Mode B). Use when user asks about PEAD screening, post-earnings drift, earnings gap follow-through, red candle breakout patterns, or weekly earnings momentum setups.

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 whenever datasets, cloud storage buckets, or data pipelines are mentioned — creating, saving, querying, listing, exploring, deleting, or processing data in S3, GCS, Azure Blob, or local storage. Also use when running any script that may create datasets as a side effect. Maintains a knowledge base at dc-knowledge/ (JSON + markdown). ALWAYS use this skill when the user creates a dataset, saves pipeline output, runs a data script, or references any storage bucket.

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。

Research what people are actually saying about a topic in the last 30 days across Reddit, X / Twitter, YouTube, Hacker News, dev.to, Medium, and other public discourse platforms. API-free; uses WebSearch with platform-targeted site operators plus recency filters. Produces DISCOURSE.md (a structured brief) and JSON output the writer can consume. Complements blog-researcher (which focuses on authority sources) with a recency-and-engagement lens. Use when user says "blog discourse", "discourse research", "what are people saying about", "research what people are saying", "voice of customer", "social listening", "30-day research", "trend research", "what's the discussion on", "real-time research", "practitioner discourse", "/blog discourse".

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。

Operate websites and Electron apps through CLI commands, reusing Chrome login sessions. Prefer over browser_task for supported sites (GitHub, Bilibili, Twitter/X, YouTube, etc.) — deterministic commands with 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。

Web data extraction using 55+ Apify Actors for AI-driven scraping. Supports Instagram, Facebook, TikTok, YouTube, Google, and more. Auto-selects best Actor for the task. Structured output in JSON/CSV with rate limiting and ethical scraping guidelines.

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 extracting plan-view architectural geometry from DXF files with semantic CAD layers, especially when outputs must normalize rooms, doors, fixtures, clearances, and grab bars into machine-checkable JSON.

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Set up or run the token build pipeline — transform the DTCG tokens/*.json (source of truth) into platform artifacts (CSS variables, Tailwind @theme, JS/TS, iOS Asset Catalog, Android, Compose) with Style Dictionary / Tokens Studio / W3C DTCG export. Use when the user wants to generate platform theme files from tokens, wire token CI, or multi-platform token 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。

Analyze Gluten ANSI-mode test results (run dev/verify-ansi-expressions.sh, parse JSON tracker output, produce root-cause analysis and fix recommendations). Trigger on user requests like "analyze ANSI tests", "run ANSI matrix", "why is this ANSI test failing".

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。

Scrape BOSS直聘 (job listing site) via Chrome CDP. Searches jobs by keyword/city/filters, fetches JD details, outputs structured JSON/CSV with plaintext salary, and can summarize scraped results into a job-market prompt. Use when user wants to search/analyze jobs on BOSS直聘 or zhipin.com.

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Collects real financial data for any US publicly traded company from free public sources (yfinance) and outputs structured JSON for downstream skills (DCF modeling, comps analysis, earnings review): market data, historical financials, WACC inputs, analyst estimates — never fabricated fallback values. Use to collect or pull financial/market data, or gather DCF inputs, for a ticker.

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。

Extracts files from repomix-packed repositories, restoring original directory structures from XML/Markdown/JSON formats. Activates when users need to unmix repomix files, extract packed repositories, restore file structures from repomix output, or reverse the repomix packing process.

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。

Detect insecure JWT (JSON Web Token) implementations in a codebase using a two-phase approach: first map all JWT issuance and verification sites to understand the token lifecycle and signing configuration, then check each verification site for exploitable weaknesses such as algorithm confusion, missing signature verification, weak secrets, header injection, and missing claim validation. Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/jwt-results.md. If no JWT usage is found in Phase 1, Phase 2 is skipped. Use when asked to find JWT, token forgery, or authentication bypass bugs.

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。