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概要と使いどころ
Fetch transcripts from YouTube videos for summarization, analysis, or reference. Accepts video ID or full URL. No API key required. Triggers: YouTube transcript, video transcript, get subtitles, what did they say in the video, summarize YouTube, extract video text, caption from video.
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Turns one meeting transcript or recap into a DRAFT action-items document (decisions, owned and dated action items with verbatim source quotes, open questions), a task list ready for the user to enter into their task tracker, and one DRAFT follow-up email per action owner, all returned in the chat for the user to store, create or send. Use when the user shares, names or points to a meeting transcript or recap and asks to "pull the action items from this transcript", "who owns what after today's call", "draft follow-up emails to each owner" or "turn this recap into tasks". Do not use for full minutes, use meeting-minutes-writer instead; a brief before a meeting is meeting-prep-onepager. Drafts for human review; never approves, authorises or signs off.
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Pull an earnings call transcript from Financial Modeling Prep, extract qualitative signal (management commentary deltas, hedging shifts, Q&A tone), and produce a thesis-delta-first Research note. Use when user says "transcript", "pull earnings call", "transcript diff", or "ingest [TICKER] earnings".
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Pull an earnings call (or investor-event) transcript from Quartr (FMP fallback), extract qualitative signal (management commentary deltas, hedging shifts, Q&A tone), and produce a thesis-delta-first Research note. Use when user says "transcript", "pull earnings call", "transcript diff", or "ingest [TICKER] earnings".
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Automatically fetch YouTube transcripts, generate structured summaries, and send full transcripts to messaging platforms. Use when a request includes YouTube URLs and needs metadata, key insights, or downloadable transcripts.
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Granolaのローカルアーカイブから会議メモや文字起こしを検索・取得するスキル。同期状態を確認し、必要な更新やSQLによる正確な件数集計も扱います。
記事のURL、YouTube動画、ポッドキャスト、PDFやローカルファイルから要点をまとめ、長さを指定した要約や動画の文字起こし抽出に使うスキル。
Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization.
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OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.
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Ingest meeting transcripts from ANY meeting recorder into brain pages with attendee enrichment, entity propagation, and timeline merge. One unified pipeline: normalize the source into a standard transcript record, split multi-meeting recordings, resolve speakers by evidence, create the page, pass every surprising claim through the consistency check (transcript + brain + plausibility), enrich every entity, then run the verification checklist — substance AND sequence. A meeting is NOT fully ingested until the enrich skill has processed every entity AND the verification checklist passes, including the sequence verify (PASS or explicit user waive).
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 AI Services Scribe. Use after routing to a transcription workflow when handling uploaded or stored media, Build-platform JWT auth, fast mode transcription, batch jobs, or transcript pipeline design.
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Turn a call transcript or call notes into a customer follow-up email draft, an internal chat summary, and proposed CRM updates. Use when the user says "call summary", "follow up on my [company] call", "summarize my call with [account]", "write the recap", "process this transcript", "process my call notes", "capture the objections and next steps", or pastes rough notes or a transcript after a discovery, demo, or negotiation call.
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Analyzes meeting transcripts and recordings to surface behavioral patterns, communication anti-patterns, and actionable coaching feedback. Use this skill whenever the user uploads or points to meeting transcripts (.txt, .md, .vtt, .srt, .docx), asks about their communication habits, wants feedback on how they run meetings, requests speaking ratio analysis, mentions filler words or conflict avoidance, or wants to compare their communication across time periods. Also trigger when users mention tools like Granola, Otter, Fireflies, or Zoom transcripts. Even if the user just says "look at my meetings" or "how do I come across in meetings" — use this skill.
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。
OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.
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 a YouTube video's transcript fetched, wants to summarize/analyze/quote a YouTube video by its spoken content, wants to search YouTube (globally or a channel handle's videos), wants a channel handle resolved to its channel ID, or wants the videos in a YouTube playlist. Calls the getyoutubetranscript.com public API - requires an API key (free tier available, no card required).
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[omh] Audio, video, or screenshot to process: user-sent media - audio, video, YouTube links, screenshots, receipts, OCR, meeting recordings, transcripts, timestamps, and clip summaries, gated for source, permission, and hallucination risk. Use when the user says: media-input-operator, media input operator, media input, audio transcription, audio transcript, transcribe audio, transcribe this audio, meeting recording.
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Azure Speech to Text REST API for short audio (Python). Use for simple speech recognition of audio files up to 60 seconds without the Speech SDK. Triggers: "speech to text REST", "short audio transcription", "speech recognition REST API", "STT REST", "recognize speech REST". DO NOT USE FOR: Long audio (>60 seconds), real-time streaming, batch transcription, custom speech models, speech translation. Use Speech SDK or Batch Transcription API instead.
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Generates complete dual-disease shared-transcriptome biomarker and hub-gene research designs from a user-provided disease pair and shared-biology direction. Always use this skill whenever a user wants to design, plan, or build a non-oncology two-disease transcriptome study centered on per-disease differential expression, shared-signal intersection or concordance, PPI-based hub-gene prioritization, diagnostic evaluation across both diseases, immune infiltration context, pathway interpretation, and optional orthogonal validation. Covers five study patterns (shared-DEG-first workflow, hub-gene-first shared-biomarker workflow, hybrid shared-biomarker compression workflow, immune-context shared-biomarker workflow, orthogonal validation workflow) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path...
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Use when analyzing transcription factor (TF) regulatory networks using Dorothea database. Input gene list, identify regulating transcription factors, generate TF-Target network visualization. For: transcription factor enrichment analysis, gene regulatory network research.
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Transforms raw meeting transcripts into structured, evidence-based meeting minutes — merges multiple versions without losing content, reviews existing minutes against the transcript for missing items, and identifies anonymous speakers (Speaker 1/2/3, 发言人1). Triggers on 会议纪要, 会议记录, 整理纪要, 妙记转纪要, "write meeting minutes", "merge these minutes". Not for fixing ASR/STT errors first (use transcript-fixer).
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Turn TikTok, Instagram, YouTube, Facebook, X, LinkedIn, Reddit, or Rumble transcripts into timestamped hooks, claims, objections, proof, calls to action, sponsorship signals, and reusable content atoms. Use directly for transcript analysis or as support for creator, competitor, trend, demand, and repurposing work.
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Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.
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Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot; MetaPlotR; deepTools computeMatrix scale-regions), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (log2 IP/input bigWig via deepTools bamCompare; pyGenomeTracks; Gviz; IGV/UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), feature-distribution stacked bars, and volcano/MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012; Meyer 2012). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features, generating DRACH motif logos as sanity checks, rendering volcano plots of differential m6A, or reproducing the stop-codon enrichment plot.
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Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM), MeTPeak (HMM over sliding windows), MACS3/MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody methods cannot resolve, and orthogonal validation (miCLIP/GLORI/m6A-SAC-seq/m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set, reconciling differing peak sets across tools, validating MeRIP peaks against single-base methods, interpreting 5' peaks where m6Am contamination is possible, or recommending a consensus 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。