874 件 ・ 関連度順
概要と使いどころ
Surface what customers are actually saying - direct attributed quotes on a topic across call transcripts and email, across your accounts. Use when the user asks "what are customers saying about [topic]", "pull quotes on [objection/feature/competitor]", "voice of customer on [X]", or "what's coming up in calls".
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。
Pre-call brief for an upcoming meeting - attendees, account history, prior call context from transcripts, open opportunity status, and suggested discovery questions. Use when the user asks "prep me for [meeting/company]", "call prep [company]", "I'm meeting with [company], prep me", "get me ready for [meeting]", or "what do I need to know before my [time] 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。
Keeps the CRM current without the owner ever opening it. Creates and updates contacts and deals from email, calendar, and call transcripts; logs activity automatically; drafts follow-ups on deals that have gone quiet; and maintains a next-step queue so nothing sits unowned. Runs on demand or on a schedule, includes a hygiene sweep for duplicates and stale records, and builds a lightweight CRM in a spreadsheet for owners who don't have one. Use this whenever CRM data or pipeline hygiene comes up — including phrasings like "update my CRM," "log this call," "clean up HubSpot," "clean up Salesforce," "add this to the deal," "turn my meeting into a deal note," "which deals have gone quiet," "I hate data entry," or "my pipeline is out of date." Reach for it when the owner describes a call or meeting worth recording, even without naming the CRM.
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。
Analyze recently closed opportunities to find patterns in what wins and what loses - stage of loss, common objections from transcripts, deal characteristics. Leader-focused. Use when the user asks "win loss review", "why are we losing deals", "what's working", or "analyze closed opps".
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。
Synthesize user research into themes, insights, and recommendations. Use when you have interview transcripts, survey results, usability test notes, support tickets, or NPS responses that need to be distilled into patterns, user segments, and prioritized next steps.
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。
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
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 fuzzy intention into a /goal objective the Goal verifier can actually judge - one outcome, numbered binary "Done when" checks that leave evidence in the transcript, guardrails, a budget, and a block protocol. Use when the user wants to set or define a goal, asks whether a goal is good enough, or says "keep going until X". Usage - /goal-draft <what you want done>, or /goal-draft <existing goal> to tighten it. This skill only writes the objective; it never starts the work.
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。
/cs:decide <memo> — Log a decision to two-layer memory via decision-logger. Approved memo becomes durable; raw transcripts kept for reference. Use when the founder has approved a boardroom memo and the decision must become durable company memory — e.g. right after /cs:boardroom concludes.
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。
Two-layer memory architecture for board meeting decisions. Manages raw transcripts (Layer 1) and approved decisions (Layer 2). Use when logging decisions after a board meeting, reviewing past decisions with /cs:decisions, or checking overdue action items with /cs:review. Invoked automatically by the board-meeting skill after Phase 5 founder approval.
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 on the JetBrains chat session UI or SessionModel/SessionController — transcript cards, retained Swing lifecycle, session background surfaces, editor-derived styling, adding session events, and session/controller testing with FakeSessionRpcApi.
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。
Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; Kiro skill instructions and steering state read selected project planning context. Recovery reads project planning files and their timestamps only, not agent transcript stores. This adapter registers no Stop hook, never requests continuation, and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.
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 a meeting transcript into structured notes with date, participants, topic, key decisions, summary points, and action items. Use when processing meeting recordings, creating meeting notes, writing meeting minutes, or recapping discussions.
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。
Fetch any URL and convert to markdown using baoyu-fetch CLI (Chrome CDP with site-specific adapters). Built-in adapters for X/Twitter, YouTube transcripts, Hacker News threads, and generic pages via Defuddle. Handles login/CAPTCHA via interaction wait modes. Use when user wants to save a webpage as markdown.
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。
Query the user's local and synced-device Screenpipe data via the REST API at localhost:3030. Use for screen activity, meetings, apps, productivity, other-device or cross-device history, media export, retranscription, or connected 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。
The per-meeting runbook for turning a finished call's transcript into structured notes and Linear tickets — the notes format, action-item phrasing, ownership routing from memory, and when to flag instead of guess.
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。
The per-call runbook for turning a finished sales call's transcript — found in the connected Google Drive folder — into an updated HubSpot deal (or a flagged handoff when no deal matches), filed follow-up tasks in {{linear_team}}, and a drafted recap email — held for the rep's approval before it sends.
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。
Execute any of 240+ specialized prompt patterns natively across Extraction, Summarization, Analysis, Creation, Improvement, Security, Rating. Common: extract_wisdom, create_threat_model, analyze_claims, improve_writing, review_code, mermaid, youtube_summary. CLI used only for YouTube transcript (-y) and URL fallback (-u). Two workflows: ExecutePattern, UpdatePatterns. USE WHEN fabric, fabric pattern, run fabric, update patterns, threat model, analyze claims, improve writing, review code, mermaid, STRIDE, sigma rules. NOT FOR multi-agent investigation (Research) or content-adaptive extraction (ExtractWisdom).
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 audio editing pipeline: Whisper word-level transcription → Claude segment classification (KEEP/CUT_FILLER/CUT_FALSE_START/CUT_STUTTER/CUT_DEAD_AIR) → ffmpeg with 40ms qsin crossfades and room-tone fill → optional Cleanvoice cloud polish; plus GateScan/GateRepair for noise-gate ticking artifacts. Modes: --preview, --aggressive, --polish. Workflow: Clean. USE WHEN clean audio, edit audio, remove filler words, clean podcast, remove ums, cut dead air, polish audio, trim recording, cut stutters, ticking audio, clicking audio, audio clicks, gate artifacts, popping audio. NOT FOR video composition (use Remotion).
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。
Multi-agent collaborative debate producing visible round-by-round transcripts with real intellectual friction — members are topic-briefed custom agents, run as a 3-round DEBATE or a 1-round QUICK check, to find the best path. USE WHEN council, debate, multiple perspectives, weigh options, deliberate, get different views, what would experts say, pros and cons. NOT FOR pure adversarial attack (use RedTeam).
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。
Watch a video (URL or local path). Downloads with yt-dlp, extracts auto-scaled frames with ffmpeg, pulls the transcript from captions (or local WhisperX / cloud Whisper fallback), and hands the result to the agent so it can answer questions about what's in the video. With a Gemini API key, Google's agentic video model watches the full video 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。
Use when the user wants Cursor to learn from recent local sessions, asks for an offline sleep or dream cycle, wants to consolidate recurring work into a Cursor skill, or requests SkillOpt-Sleep status, harvest, dry-run, run, scheduling, review, or adoption. Drives the validation-gated skillopt_sleep engine with Cursor transcripts and the optional Cursor Agent CLI backend.
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 knowledge for AI video clipping — yt-dlp downloading, whisper transcription, SRT generation, and ffmpeg processing
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。
Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership skills.
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。