354 件 ・ 関連度順
概要と使いどころ
When setting up commit message validation for a project. When project has commitlint.config.js or .commitlintrc files. When configuring CI/CD to enforce commit format. When extracting commit rules for LLM prompt generation. When debugging commit message rejection errors.
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 several language models as independent coders on the same labeling or discovery task and reads their disagreement as data — panel assembly for model diversity, keeping votes independent, consensus rules, agreement statistics (Cohen's and Fleiss kappa, Krippendorff's alpha), the correlated-errors caveat that stops agreement being mistaken for validity, human validation beyond the panel, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, asks how to combine several models' labels, or asks what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.
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。
Crawl GitHub trending repositories, analyze with LLM for Chinese insights, categorize by themes, compute diffs against history, and generate Markdown reports. Default brief mode stops at trend analysis; optional detailed mode appends per-project analysis. Supports incremental gap-filling and selective re-analysis with caching.
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 query a local Markdown knowledge base ("vault"). TWO functions — (1) CONVERT raw files (PDF, Word/docx, PowerPoint/pptx, Excel/xlsx, csv/tsv, images, html, md/txt, json/yaml/code, audio/video) into clean Markdown with retrieval-friendly frontmatter; local-first (pandoc / python-pptx / openpyxl / pymupdf4llm / whisper), with cloud OCR (MinerU) only as a fallback. (2) ANSWER questions over the resulting vault with retrieval discipline — self-monitor coverage, flag missing/lossy content, and propose Maps-of-Content (MOCs). Triggers: "build/sync my local knowledge base", "convert these files to markdown for AI", "整理我的资料库", "把文件转成 md 给 AI 读", "本地知识库", "读我的本地 vault 回答", "这个主题我的资料里怎么说". Not for: one-off web research, or files that are already in a single doc you can read directly.
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 production-ready AI agents with Microsoft Foundry and Agent Framework. Use when creating AI agents, selecting LLM models, implementing agent orchestration, adding tracing/observability, or evaluating agent quality. Covers agent architecture, model selection, multi-agent workflows, and production deployment.
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。
Comprehensive AI/ML development guide for LangChain, LangGraph, and ML model integration in FastAPI. Use when building LLM applications, agents, RAG systems, sentiment analysis, aspect-based analysis, chain orchestration, prompt engineering, vector stores, embeddings, or integrating ML models with FastAPI endpoints. Covers LangChain patterns, LangGraph state machines, model deployment, API integration, streaming, error handling, and best practices.
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 the codebase to create a concise, LLM-optimized structured overview in .agent/map.md.
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。
Respan for LLM observability. Use to set up tracing or the gateway, manage prompts (create/version/deploy), run evals, experiments, and datasets, or work with traces and logs. Trigger for any Respan platform task or when instrumenting an app to send telemetry to Respan.
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。
Turn a KOL roster + subreddit brand-fit analysis into a deployable creator network. Splits creators into KOL (authority) and KOC (grassroots) layers, matches each to a main + secondary subreddits by real gear/shooting-type ∩ community fit, and emits an account×subreddit deployment_matrix (with a ≤3-per-sub ceiling + no-cross-interaction rule) plus per-creator persona_cards. Route A disclosed-compliance baked into every card (FTC disclosure required on any material connection). Consumes reddit-kol-finder's kol_roster.json natively. READ-ONLY, deterministic, no LLM key needed.
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 verifying that a model in the imp inference engine produces coherent output without repetition loops, token-stuck states, or state corruption across turns or streams. Triggers on "degenerates", "check degeneration", "repetition loop", "own own own", "stuck token", "empty content", "multi-turn regression", "does it still work", "NIAH", and after enabling CUDA graphs / changing forward pass / MoE routing / KV cache or KV dtype / GDN state or scan / sparse attention / PDL / speculation (MTP, n-gram) / ragged prefill / batched-decode kernels (smallm, producer quantize) / FA2 softmax.
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。
Hugging Face Hub operations, model inference, dataset management, PEFT/LoRA fine-tuning, and Spaces deployment via MCP tools and Python APIs
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。
AI and ML expert covering PyTorch, TensorFlow, Hugging Face, scikit-learn, LLM integration, RAG pipelines, MLOps, and production ML systems
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 proposed or existing backlinks for relevance, editorial legitimacy, link attributes, spam/toxicity risk, platform compliance, anchor quality, target-page fit, and monitoring evidence. Use this skill when the user asks whether an external link opportunity or placed backlink is safe and useful, and reject toxic, irrelevant, paid-undisclosed, fake, or spam placements.
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。
Work with the @upstash/vector TypeScript/JavaScript SDK, a serverless vector database for embeddings, similarity search, semantic search, and RAG (retrieval-augmented generation). Use when upserting, querying, fetching, ranging, or deleting vectors, upserting raw text against an index with a built-in embedding model, choosing dense, sparse, or hybrid indexes, filtering by metadata, organizing data with namespaces, running resumable queries, or connecting Upstash Vector to an AI or LLM application. Also use when the user asks for a vector store, vector search, nearest-neighbor or kNN search, embeddings storage, semantic cache, recommendations or similarity features, or a hosted vector index that needs no infrastructure.
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。
Auto-activates when coordinating sequential validation specialists with workflow management, dependency tracking, and overall validation status monitoring.
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 publish project documentation — Starlight (Astro) for apps, markdown for libraries/services, with IT+EN i18n and VPS deploy. Use when writing user guides, scaffolding a docs site, or publishing docs. Trigger on "write docs", "documentation site", "user guide", "publish docs".
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。
Post-merge deployment workflow covering feature flags, canary/blue-green rollout, smoke-test verification, and rollback triggers. Use after a PR is merged and you need to ship it safely to production. Trigger on "deploy", "roll out", "canary", "feature flag", "rollback".
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 merge GitHub pull requests using comprehensive pre-merge validation. Use this skill when the user asks to merge a PR, complete a PR workflow, or check if a PR is ready to merge. Validates CI/CD status, reviews, and conflicts before merging with the appropriate strategy.
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。
Initialize a new project with full setup — repo structure, docs, quality tools, and CI/CD. Use when starting a brand-new project from scratch. Trigger on "create a new project", "initialize a project", "set up a new repo", "bootstrap a project".
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。
Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browser_use, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle hooks, MCP server setup, or monitoring/observability with Laminar or OpenLIT. Also trigger for questions about browser-use installation, prompting strategies, or sensitive data handling. Do NOT use this for Cloud API/SDK usage or pricing — use the cloud skill instead. Do NOT use this for directly automating a browser via CLI commands — use the browser-use skill 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。
LLM observability with Langfuse — OpenTelemetry-based tracing, evaluations, prompt management, datasets, and production best practices
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。
Provider-agnostic patterns for LLM function calling, tool loops, and agentic workflows
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。
Local LLM inference with the Ollama JavaScript client -- chat, streaming, tool calling, vision, embeddings, structured output, model management, and OpenAI-compatible endpoint
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。
Testing and evaluation framework for LLM prompts and applications -- promptfooconfig.yaml, assertions, model-graded evals, red teaming, CI/CD integration, custom providers, and comparative evaluation
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。