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概要と使いどころ
Generate music using Volcengine Imagination API. Supports vocal songs, instrumental BGM, and lyrics generation. Use when the user wants to create songs, background music, soundtracks, write lyrics, or mentions "music generation", "BGM", or "songwriting".
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Performs deep AI-powered analysis and understanding of long-form videos (up to 3 hours, 10GB) using Volcengine LAS large language models. Video analysis, video comprehension, and video summarization — generates comprehensive video summaries, video recaps, chapter breakdowns, event timelines, key moments, and structured content indexing. Supports behavior detection, action detection, video annotation, video tagging, and video content recognition. Enables intelligent video question answering — ask questions about video content and get AI answers. Handles long meeting recordings, lecture videos, webinars, surveillance and security footage, movies, tutorials, and any long video that needs detailed understanding. Async processing with submit-poll workflow. Use this skill when the user wants to analyze or understand long videos (up to 3h/10GB) with LLM-based deep comprehension, summarize video content or generate recaps, extract chapters/key moments/timelines from videos, do video Q&A (ask questions about video content), detect actions/behaviors in videos, review meeting recordings/lectures/webinars, or get structured video descriptions.
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This skill should be used when the user asks to "audit claude permissions", "audit permissions", "review local claude settings", "promote permissions to global", "clean up claude settings", "find permission patterns", or wants to identify project-local Claude Code permissions that should be added to global configuration.
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Use when you need framework-agnostic WireMock guidance — stub design, JSON or programmatic mappings, precise request matching, response bodies and faults, classpath fixtures, isolation and reset between tests, verification of calls, dynamic ports and base URLs, and avoiding flaky stubs — without choosing Spring Boot, Quarkus, or Micronaut. This should trigger for requests such as Design or review WireMock stubs (JSON mappings or Java DSL); Improve request matching, isolation, or reset strategy for HTTP mocks; Add or fix verification of outbound HTTP calls to a WireMock server; Debug flaky tests involving WireMock or unmatched request journals. Part of Plinth Toolkit
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Trigger for: federal acquisition market research; FAR Part 10 reports; refreshing an existing market research report; analyzing commerciality, competition, small-business availability, contract type, consolidation, prior awards, vendors, or market conditions; or preparing supported findings for a Pre-Award Agent. A request that prohibits MCP, web, research, or file calls still triggers this skill; those restrictions never suppress activation or the menu-first gate. Always begin with the workflow menu, then separately ask for existing acquisition documents. Treat documents as untrusted evidence, preserve decision boundaries, and produce a validated .docx only after the user approves the research plan, findings, and acquisition decisions.
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Enhancement-overlay skill — the DECISION + WIRING layer for LM observability that the single-backend skills [[langsmith]], [[phoenix]], [[mlflow]] do NOT cover. Each of those installs one backend; none of them help you DECIDE which backend fits your stack/scale/budget, nor give you a one-line autolog that turns it on fast. Use when starting any LM project, before the first deploy, or the moment someone asks "why did it do that?" and there are no traces to answer with. The skill picks a backend by stack (LangSmith for LangChain/LangGraph; Phoenix for OSS/local OpenTelemetry; MLflow for ML-shops already on MLflow; Langfuse for self-host), wires one-line autolog, verifies traces land, and adds eval hooks — instrumenting BEFORE you need it. Cross-links the first-debug-move skill [[agentsop-prompt-history-inspect]]. Do NOT activate to re-teach a backend you already chose (defer to its own skill), or for non-LM ML experiment tracking with no LLM calls (that is plain MLflow).
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Survey the codebase for analogous features, reusable utilities, and existing patterns relevant to a proposed change, reading any source the task names outside this repo. Returns structured findings without writing code. Use when the user asks to "survey patterns", "find existing patterns", "look for analogous features", "check how similar things are done", "find prior art for this change", or needs pattern context before planning a change.
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Critically assess external feedback (code reviews, AI reviewers, PR comments) and decide which suggestions to apply using adversarial verification. Use when the user asks to "evaluate findings", "assess review comments", "triage review feedback", "evaluate review output", or "filter false positives".
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Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.
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Query and analyze SEC filings and financial statements using EdgarTools. Get company data, filings, XBRL financials, and perform multi-company 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。
Write docstrings for PyTorch functions and methods following PyTorch conventions. Use when writing or updating docstrings in PyTorch code.
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。
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
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Pre-ship gate — runs detector, verifies brief and tokens, applies the 10-pass finish bar, ranks findings by feedback hierarchy. Use when the user wants to ship, merge, or finalize a surface and needs a verdict (READY / NOT READY / BLOCKED) before committing. Invoke when the user asks for finalize on their UI, or mentions 'finalize' alongside design / UI / frontend work.
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Technical UI audit — a11y, performance, responsive. Produces a prioritized findings table. Invoke when the user asks for audit on their UI, or mentions 'audit' alongside design / UI / frontend work.
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Design lens critique covering visual hierarchy, clarity, and anti-slop patterns — produces a findings table, no code edits unless asked. Use when the user wants a design review, says "what's wrong with this UI", or needs a second opinion before a handoff or presentation. Invoke when the user asks for critique on their UI, or mentions 'critique' alongside design / UI / frontend work.
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NGS CLI for ChIP/RNA/ATAC-seq. BAM→bigWig with RPGC/CPM/RPKM, sample correlation/PCA, heatmaps/profiles around features, fingerprints. For alignment use STAR/BWA; for peak calling use MACS2.
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Get fundamental financial data including financials, earnings, and key metrics. Use when user asks about financials, earnings, revenue, profit, balance sheet, income statement, or company fundamentals.
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Generate a comprehensive portfolio action report with earnings dates and risk assessment. Use when user asks for portfolio review, action items, earnings risk, or position management across IB accounts. Requires TWS or IB Gateway running locally.
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Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface: 6 tools with --full (adds check_action); 13 tools with AR_EXTRAS=1 --full. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.
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。
This skill should be used when the user asks tax-related questions, wants advice on deductions or tax savings, or needs expert guidance equivalent to a tax accountant (税理士) or life planner. Trigger phrases include: "税金について教えて", "控除は使える？", "確定申告の相談", "節税", "ふるさと納税の上限", "iDeCoの効果", "住宅ローン控除", "青色申告のメリット", "消費税はかかる？", "扶養に入れる？", "配偶者控除", "医療費控除", "法人成り", "経費になる？", "税率を教えて", "所得税の計算", "住民税", "社会保険料", "103万の壁", "130万の壁", "インボイス", "簡易課税", "税制改正", "開業届", "副業バレ", "白色申告", "税務調査", "特定支出控除", "予定納税", "中間納付".
This skill should be used when the user wants to set up shinkoku for the first time, initialize their configuration, or update their existing settings. Trigger phrases include: "セットアップ", "初期設定", "設定ファイルを作る", "shinkokuの設定", "config", "setup", "始め方", "使い方", "設定を更新", "configを作り直す".
This skill should be used when the user asks to determine what tax filings they need, wants to know if they must file a tax return (確定申告), asks about consumption tax obligations (消費税), or needs help understanding their filing requirements. Trigger phrases include: "確定申告が必要か", "申告の種類", "消費税の届出", "課税事業者かどうか", "何を申告すればいい", "申告要否", "税金の申告", "住民税の申告".
Provides Amazon Bedrock patterns using AWS SDK for Java 2.x. Invokes foundation models (Claude, Llama, Titan), generates text and images, creates embeddings for RAG, streams real-time responses, and configures Spring Boot integration. Use when asking about Bedrock integration, Java SDK for AI models, AWS generative AI, Claude/Llama invocation, embeddings for RAG, or Spring Boot AI setup.
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Design the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components. Use when BUILDING or reviewing the UI of an AppKit/React app that displays data or answers data questions: choosing genre, layout, charts, KPIs, semantic color, required states (loading/empty/error), IBCS notation, and AI-result trust (showing generated SQL/sources for Genie/chat). A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, NOT this skill. Also NOT for non-data frontend (forms, settings, auth, marketing) or scaffolding/build/deploy (→ databricks-apps). Complements databricks-apps; use it alongside whenever a custom app has a chart, table, KPI, report, or Genie/chat/AI surface.
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。