Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
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。
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概要と使いどころ
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
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 the user needs to transform business requirements, use case descriptions, or meeting transcripts into a technical Power Platform solution architecture, including component selection and Mermaid.js diagrams.
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。
Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).
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。
Compile documents, notes, web content, transcripts, research materials, or code repositories into an evidence-grounded, visualization-ready knowledge graph with semantically typed entity nodes, statement-level provenance, and typed relationship edges. Use with ov compile to create or incrementally refresh `entities/*.md` node artifacts and a root `relations.jsonl` edge file for people, organizations, groups, animals, places, products, projects, systems, services, modules, datasets, standards, documents, events, and other identifiable things.
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。
Compile heterogeneous knowledge sources—including documents, notes, web content, transcripts, research materials, and code repositories—into a Karpathy-style, evidence-grounded LLM Wiki with a maintained index; default entity and concept pages; and selective method, comparison, analysis, or instruction-requested summary pages. Use with ov compile to create or incrementally refresh knowledge that is easy for people and agents to retrieve, navigate, and reuse.
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。
Generate a concise 4-5 page equity research earnings preview for a single company. Analyzes the most recent earnings transcript, competitor landscape, valuation, and recent news to produce a professional HTML report.
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。
Generate an explorable HTML report of Claude Code session usage (tokens, cache, subagents, skills, expensive prompts) from ~/.claude/projects transcripts.
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Generate a personal Claude Code usage & impact report ("receipts") from this machine's local session transcripts — for justifying Claude Code usage/spend to a manager, self-review, or "what have I been using this for" check-ins. Mines ~/.claude/projects locally (no extra API calls beyond one final write-up), cross-references local git history, and writes a markdown report plus a self-contained HTML receipt to your home directory. Use when the user asks for "receipts", an "impact report", "usage report", wants to "show my Claude Code activity", "prove the value of Claude Code", or runs `/receipts`.
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。
Expert in building voice AI applications - from real-time voice agents to voice-enabled apps. Covers OpenAI Realtime API, Vapi for voice agents, Deepgram for transcription, ElevenLabs for synthesis, LiveKit for real-time infrastructure, and WebRTC fundamentals. Knows how to build low-latency, production-ready voice experiences. Use when: voice ai, voice agent, speech to text, text to speech, realtime voice.
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。
Author an eval for an existing skill from its REAL usage history, not its spec. Mine invocations from the brain's conversation archive (conversations/) and per-harness session transcripts — a user correction after an invocation is the gold signal — then synthesize an eval_contract plus 4-8 replayable cases with honesty labels (SPEC-DERIVED vs HISTORY-IMPLIED) and stage the result at skills/<name>/eval/autobench-<date>.md as PENDING-HUMAN-APPROVAL. Never rewrites SKILL.md. Ships two guard companions: panel integrity (multi-model judging must prove each provider actually responded) and the fail-improve taxonomy (logged LLM-fallback cases convert to deterministic code over time).
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。
Read a book, article, transcript, or case study through the lens of a specific strategic problem you're facing. Produces an applied playbook that maps the source onto the problem and gives short/medium/long-term recommendations. NOT for general book summaries.
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-call handling for a voice session — turn the transcript into a brain page, post the summary to the operator's messaging surface, archive the audio. The pipeline is the contract; the firing paths are operator-wired (see "Two firing paths" below for what ships today).
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。
Tiered LLM extraction pattern for large corpus processing (email archives, document dumps, transcript libraries). A utility-tier model triages and classifies at speed; the reasoning tier does the default deep read; the deep tier is the escalation for the highest-value content. Prevents spending deep-tier money on noise while ensuring the important content gets the best eyes. A deterministic privacy wall runs before any LLM call.
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。
Import AI-assistant chat exports (ChatGPT, Claude, Perplexity) and agent session transcripts into the brain as one dated page per conversation under conversations/, validate each page against the native conversation parser, extract facts via the native conversation-facts flow, and keep the archive gap-free with a detect-and-backfill loop. Then answer archive questions: "when did I first discuss X", trace how an idea evolved across past conversations, pull a specific thread.
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。
Speech-to-text via 9Router /v1/audio/transcriptions using OpenAI Whisper / Groq / Gemini / Deepgram / AssemblyAI / NVIDIA / HuggingFace models. Use when the user wants to transcribe audio, convert speech to text, or get subtitles from audio 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。
Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).
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。
Reference skill for Zoom RTMS. Use after routing to a live-media workflow when processing real-time audio, video, chat, transcripts, screen share, or contact-center voice streams.
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。
Zoom Meeting SDK for Linux - C++ headless meeting bots with raw audio/video access, transcription, recording, and AI integration for server-side 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。
Zoom Video SDK for Web - JavaScript/TypeScript integration for browser-based video sessions, real-time communication, screen sharing, recording, and live transcription
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。
Guidance for the bundled Zoom MCP connectors. Use after routing to an MCP workflow when planning or troubleshooting tool-based access to meetings, recordings, meeting assets, or transcripts. Route Zoom Docs requests to the dedicated Docs MCP server and Whiteboard-specific requests to `zoom-mcp/whiteboard`.
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 generates, creates, or builds brand voice guidelines from source materials. It should be used when the user asks to "generate brand guidelines", "create a style guide", "extract brand voice", "create guidelines from calls", "consolidate brand materials", "analyze my sales calls for brand voice", "build a brand playbook from documents", "synthesize a voice and tone guide", or uploads brand documents, transcripts, or meeting recordings for brand analysis. Also triggers when the user has a discovery report and wants to convert it into actionable 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。
Build a Zoom meeting bot, recorder, or real-time media workflow. Use when joining meetings programmatically, processing live media or transcripts, or combining Meeting SDK, RTMS, and backend services.
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。
Turns whatever came out of a discovery conversation — a call transcript, a voice memo, jobsite photos, an RFP document, a set of drawings, or scrappy notes — into a branded, costed proposal, estimate, or statement of work built on the owner's own templates and historical pricing. Routes it for signature and triggers the deposit invoice once it's accepted. Works entirely from uploaded files when no connector is available, producing DOCX and PDF the owner can send themselves. Use this whenever a quote, bid, estimate, proposal, or SOW is the deliverable — including phrasings like "write this up for them," "put together a quote," "I need to bid this job," "turn my notes into a proposal," "respond to this RFP," or "price this out." Reach for it when the owner describes a job they just looked at, even without saying the word quote.
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。
Log a call, meeting, or email exchange to the CRM after the fact as a completed activity on the right account, opportunity, and contact - drafted from your description or a transcript, shown with its sources and saved when you ask. Use when the user says "log this call", "log my meeting with [account]", "log that I emailed [contact]", "track this conversation in the CRM", or "what activities are logged on [account]".
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。