「transcript」の検索結果

Search a Claude Code session transcript for a string or regex pattern and show every matching message with surrounding context. Reads /api/sessions/:id/transcript and resolves sessions via /api/sessions?limit= from the Agent Monitor dashboard. Use when hunting for a specific message, prompt, tool call, or error inside a session's conversation.

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When you paste raw human input — a call transcript (Grain, Zoom, Granola, Fathom), a text or email from a client/partner/friend, a voice-memo dump, or meeting notes — and want it converted into structured work. Extracts decisions, action items (yours vs theirs), bugs/feature requests, and facts worth keeping; files GitHub issues in the right repo, captures to the second-brain vault, and drafts (never sends) the reply. Triggers on "/ingest", "ingest this", "here's my call with X," "here's the transcript," "this is from [person]," "[person] asked me this," "from [person]:", a pasted transcript with speaker labels, or a forwarded client message that clearly expects processing. Person→project routing lives in a private config; unknown senders get asked about once, then remembered.

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Generate structured meeting notes from transcripts or pasted text. Use when someone says "meeting notes", "process this transcript", "summarize this call", "notes from today's meeting", or pastes/references a meeting transcript. Auto-enriches attendees from stakeholder directory, routes output to correct project subfolder, flags downstream artifact impacts (PRD, deck, architecture), and proposes CLAUDE.local.md updates. Produces date-first Markdown files.

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Transcription, recording management, and quote extraction. Use when processing audio/video or generating timestamped transcripts.

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Download YouTube video transcripts when user provides a YouTube URL or asks to download/get/fetch a transcript from YouTube. Also use when user wants to transcribe or get captions/subtitles from a YouTube video.

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Use this skill when an agent needs AssemblyAI for speech-to-text or speech-understanding work in media production, including pre-recorded, synchronous short-file, and real-time streaming transcription; speaker diarization or speaker identification; captions and subtitles; timestamps; language detection, code-switching, or transcript translation; audio intelligence such as summaries, chapters, topics, entities, key phrases, sentiment, content moderation, profanity filtering, and PII redaction; webhooks, scaling, rate limits, retention, security, consent, and QA for podcasts, interviews, captions, call recordings, and edit workflows.

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Inspect and manually purge the Soniox artifacts — transcriptions and uploaded audio files — that offline transcription leaves behind, on dev or prod. Use when offline transcription silently stops working, when Soniox answers 429 `limit_exceeded`, when `SonioxTranscriberTest.OfflineTranscribeWorks` and `CleanerDeletesEnqueuedArtifacts` fail together, or when the user says "sweep Soniox", "soniox/sonoix sweep", "sonoix swipe", "purge Soniox", "check Soniox files", or "is Soniox at the cap".

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Select an OCR/HTR engine by benchmarking candidates on the same labelled samples — source ground truth from open datasets (e.g. Hugging Face PAGE-XML corpora) or hand-transcription, wrap every engine behind a transcribe(image_path) -> str adapter (vision-LLMs via any OpenAI-compatible endpoint, dedicated HTR REST APIs, cloud OCR), and score with zero-dependency raw CER, lenient CER, WER, and a critical name/date token diff. Use when choosing an engine for handwriting or historical-document transcription, when published accuracy claims need honest verification on your own material, or when a records workflow must surface confident-but-wrong names and dates that a low aggregate CER hides.

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Design small interfering RNA and antisense oligonucleotide sequences against a transcript, and screen them for the failure modes specific to nucleic-acid drugs. Use this skill to tile a target transcript, apply positional and thermodynamic selection rules including duplex asymmetry and nearest-neighbour melting temperature, scan candidates for seed-region complementarity to off-target transcripts, and lay out a chemical modification pattern — gapmer architecture, 2'-O-methyl and 2'-MOE wings, locked nucleic acid, and phosphorothioate placement. Also trigger on siRNA, antisense oligonucleotide, ASO, gapmer, RNase H, seed region, duplex asymmetry, 2'-MOE, locked nucleic acid, phosphorothioate, or GalNAc conjugate.

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Rewrite raw meeting audio transcriptions into clean, accurate meeting minutes in Traditional Chinese. Use when the user has an unprocessed audio transcription file with recognition errors and needs it cleaned up into proper meeting minutes.

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Use this skill when setting up Service Cloud Voice with Amazon Connect — provisioning the contact center, configuring phone numbers, enabling real-time transcription, and configuring After Conversation Work Time. Covers the deployable metadata behind it: CallCenter, ConversationVendorInfo (vendorType), CallCenterRoutingMap, the Voice ServiceChannel that carries ACW, ServicePresenceStatus, PresenceUserConfig, ServiceCloudVoice.settings, and the VoiceCall / VoiceCallRecording objects. Trigger keywords: Service Cloud Voice, Amazon Connect, contact center, softphone, call transcription, After Conversation Work, ACW, wrap-up time, VoiceCall, CallCenter metadata, vendorType, telephony provider. NOT for Omni-Channel routing setup — use admin/omni-channel-routing-setup. NOT for Open CTI softphone adapters — use apex/cti-adapter-development. NOT for Sales Dialer (Voice.settings) — that is a different product.

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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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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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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.

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Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization.

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Turns process notes, a walkthrough or interview transcript, a transcript of a meeting recording or a stale existing procedure into a draft standard operating procedure: purpose and trigger, scope, definitions, roles with a RACI table, numbered steps with the check that closes each one, records produced, controls as stated, and open questions for the process owner. Every step traces to the source or reads UNKNOWN. Use when the user asks to "write an SOP from these notes", "turn this walkthrough into a procedure", "document how we do this", "draft the standard operating procedure for", "build a RACI for this process" or "tidy up this old procedure". Do not use for technical runbooks with commands and rollback, use runbook-drafter instead; for meeting minutes and action lists, use transcript-to-actions. Drafts for human review; never approves, authorises or signs off.

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Turns one meeting transcript, recording recap or set of raw notes into concise DRAFT minutes returned in the chat as a single Markdown document: header metadata, attendance and apologies, agenda items each with a short neutral summary and outcome, numbered decisions with an owner, numbered action items with owner and due date, open questions, carried-over actions and the next-meeting line, every entry backed by a short verbatim source fragment and every missing fact marked UNKNOWN. Use when the user asks to "write the minutes", "draft minutes from this transcript", "turn these notes into meeting minutes", "produce the minutes of meeting for today's call", "tidy my rough minutes into the standard format" or "prepare the minutes for circulation". Do not use for extracting action items, task lists or per-owner follow-up emails alone, use transcript-to-actions instead. Drafts for human review; never approves, authorises or signs off.

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Organize user-scoped CLEC episodes into traceable Traditional Chinese Obsidian pages. Use when Codex must group X, pCloud, Google Drive, Bilibili, Odysee, and RSS or Podcast sources by five-digit episode number; obtain and review transcripts through media-transcript; clean readable transcripts with the confirmed CLEC lexicon; anonymize students; or build the standard three-layer episode page.

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Verify someone else's code fix against your own live corpora (agent transcripts, sessions, memory files) before trusting or merging it. Use when an external PR or patch touches how transcripts, sessions or agent memory are read. Triggers: /corpus-bench, run it on our corpus, verify the fix on our transcripts, corpus bench.

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Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization.

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。

Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization.

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。

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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Detects m6A modifications from Oxford Nanopore direct-RNA-sequencing (ONT DRS) signal data using m6Anet (Hendra 2022 *Nat Methods* 19:1590; multiple-instance-learning neural network over DRACH 5-mer signal). Covers the required upstream pipeline (Dorado / Guppy basecalling -> minimap2 transcriptome alignment with `-ax map-ont -uf -k14 --secondary=no` -> nanopolish eventalign with `--scale-events --signal-index` (m6Anet-required) plus `--summary` / `--threads` housekeeping -> m6anet dataprep -> m6anet inference), per-site vs per-read probability interpretation including the `mod_ratio` per-site stoichiometry column, the DRACH-only modeling constraint, minimum-coverage thresholds (20-50 reads per site for stable probability estimates), multi-condition comparison via xPore (Pratanwanich 2021 *Nat Biotechnol* 39:1394), Nanocompore (Leger 2021 *Nat Commun* 12:7198), ELIGOS (Jenjaroenpun 2021 *NAR* 49:e7), and Dorado native modification calling (RNA004 chemistry, 2024+), reference-transcriptome version pinning, the cDNA-vs-DRS chemistry distinction (cDNA-Nanopore CANNOT be used for modification detection), and orthogonal validation against MeRIP / GLORI. Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting per-site `probability_modified` vs `mod_ratio` vs per-read modification probabilities, comparing methylation between conditions from ONT data, deciding between m6Anet for known DRACH sites and Dorado/Remora for genome-wide screening, pinning RNA002 vs RNA004 chemistry and basecaller model versions, or troubleshooting eventalign / dataprep failures.

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