363 skills · sorted by relevance
Use when user asks YouTube video extraction, get, fetch, transcripts, subtitles, or captions. Writes video details and transcription into structured markdown file.
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Translate video subtitles (SRT, VTT, TXT) between languages. Supports batch translation and maintains timing synchronization. Use when creating multilingual subtitles, expanding to international audiences, or translating video transcripts.
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Fetch transcripts from YouTube videos for summarization and analysis.
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Transcribe audio and video from URLs (YouTube, direct media links) using WhisperKit locally. Optionally analyze transcripts with AI when explicitly requested. Use when users provide URLs to media content and request transcription or speech-to-text conversion.
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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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AssemblyAI integration. Manage Transcripts, Speakers, Jobs. Use when the user wants to interact with AssemblyAI data.
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Prepare for a sales call with account context, attendee research, and suggested agenda. Works standalone with user input and web research, supercharged when you connect your CRM, email, chat, or transcripts. Trigger with "prep me for my call with [company]", "I'm meeting with [company] prep me", "call prep [company]", or "get me ready for [meeting]".
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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.
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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.
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Redpanda's Agentic Data Plane: governance infrastructure for building, running, and governing AI agents and MCP servers, plus a proxying AI Gateway for LLM providers, operated via `rpk ai` and the ADP API. Use when creating or managing AI agents (managed or self-managed) via `rpk ai agent` or `AgentRegistryService`; configuring MCP servers (remote or managed catalog, code mode, auth); setting up LLM providers or querying models via `rpk ai llm`/`rpk ai model` or the AI Gateway proxy; or configuring budgets, guardrails, or Cedar access-control policies through the governance APIs. Also covers reading agent transcripts and spending insights, and wiring OAuth clients or providers to the aigw Authorization Server. For the separate rpk cloud mcp control-plane MCP server, see `/redpanda:rpk-cloud`.
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Fetches Apple documentation as Markdown via Sosumi. Use for Apple API reference, Human Interface Guidelines, WWDC transcripts, and external Swift-DocC pages.
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Use when capturing ONE new idea the user voices and wants recorded — "save this idea", "I have an idea", "log this idea", "/idea", "idea: ...". Creates a provenance-tracked folder (one folder per idea) in your ideas repo, dedups against an index, optionally mirrors to a GitHub Project view filtered by label:idea. NOT for mining ideas out of transcripts/meetings (use a dedicated extraction skill) and NOT for dated work tasks (use an issue tracker / the manager skill). Triggers also in Russian: «запиши идею», «у меня идея», «зафиксируй идею».
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Run the weak-agent adversarial test harness against docx-cli. Spawns weak exercise agents (Haiku by default, Sonnet to probe, or a local agent harness's pre-produced runs) to perform real document tasks over six scenarios — five editing (MNDA form-fill + font fidelity, invoice table-edit/restructure + logo replace, résumé styling, contract redlining + commenting, contract finalize via accept/reject + comment reply/resolve) and one authoring (T. S. Eliot poetry journal: multi-column, verse, footnotes, links, figure) — renders every result with Word, has opus judge them against ground-truth rubrics, measures each exercise's tool economy, token cost, wall-clock, and correctness (from transcripts for Claude, the exercise.json ledger for the local harness), and synthesizes a prioritized ergonomics report. Use when the user says 'adversarial review', 'test docx-cli with weak agents', 'run the haiku harness', 'weak agent test', or wants to re-run yesterday's adversarial process.
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Profile and debug Hermes sessions from their JSONL transcripts. Find a session and its subagents, build a queryable event table, summarize the work as a hierarchical table of contents, break down wall time, inference, tools, tokens, and estimated cost per agent, identify errors and improvement opportunities, and export a shareable Perfetto trace. Use when a user wants to inspect what a Hermes session did, debug agent or subagent activity, understand session cost or latency, create a session timeline, generate a trace, or learn how to improve the next agent run.
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Build production realtime voice agents with Hume's Empathic Voice Interface (EVI): speech-to-speech sessions, empathic/prosodic response design, EVI 3 versus EVI 4-mini selection, WebSocket and SDK integration, tools/function calling, interruption/barge-in, turn-taking, transcripts/audio artifacts, pricing/limits, privacy, safety, consent, and QA.
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Use for ElevenLabs provider-specific dubbing, localization, voice changer, speech-to-speech, and voice isolation/enhancement workflows. Applies when an agent must prepare source media, choose ElevenLabs dubbing versus voice conversion, manage language/speaker/timing decisions, handle voice identity and consent, call or plan against documented ElevenLabs APIs, export dubs/transcripts, estimate cost/limits, or quality-control localized speech while avoiding generic text-to-speech guidance.
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Provider-independent captions and media accessibility direction for AI agents producing or finishing generated videos, ads, social clips, explainers, avatar videos, documentaries, podcasts/video recuts, training media, and localized content. Use when planning, authoring, reviewing, localizing, burning in, exporting, or QAing captions, subtitles, SDH, transcripts, audio description, flashing/motion safety, caption readability, or accessible media handoff files.
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Produce audio-first podcast episodes with generative tools — design the show and episode format (interview, narrative, news brief, two-host conversational), write scripts for the ear, decide between fully-synthetic and hybrid (recorded human + synthetic) production and meet the disclosure duty each triggers, cast and direct multi-voice TTS for consistency and chemistry, build episode structure (cold open, intro/outro, segments, ad slots), assemble and edit, clear music and SFX rights, hit podcast loudness and delivery standards, ship metadata/chapters/transcripts through RSS, and pass platform AI-content policy and QA before publishing. Use this skill whenever the deliverable is a podcast episode or a podcast show bible, or when an agent must make production, rights, disclosure, or delivery decisions for spoken-word audio. Not for music tracks, single-voice notification prompts, or video where picture leads (route audio-for-video to a video skill).
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Provider-independent dialogue editing and ADR direction for AI agents producing generated videos, films, ads, avatar clips, explainers, localization/dubbing, podcasts, recuts, and social content. Use for dialogue prep, repair, room tone, take comping, sync, ADR cueing, dubbing direction, pronunciation, de-essing, plosives, mouth clicks, voice continuity, synthetic voice consent boundaries, captions/transcripts handoff, mix handoff, loudness, intelligibility, accessibility, and QA.
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Institutional-grade equity research skills for AI agents. 80 Claude-type skills across 14 verticals (equity-research-core, models-and-pitches, bio-pharm, scenarios, quantitative-analysis, idea-generation, options-derivatives, business-intelligence, industry-analysis, macro-strategy, portfolio-strategy, technical-analysis, risk-and-psychology, trading-as-business) powered by agentii.ai's agent-use-ready SEC filing data plane — SEC filings and 15K+ earnings call transcripts, 15.99M XBRL facts, and company profiles for 1,146+ US-public-equity tickers. Features the three-layer retrieval protocol (Document Discovery → Page Map → Deep Read, with deep-outline escalation) — measured, not asserted: 97.5% saved against a sequential read, and the page map itself is 10.0% of one (spec 062 T030, 2026-09-23) — server-side parallel multi-period search via search_cross_period, a full Excel/PPT generation pipeline with 3-tier office backend support, and the spec-046 governance layer (constitution, thesis and single-skill modes, mechanical gates, and a template-owned disclaimer on generated reports).
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Quantify transcript expression using pseudo-alignment with Salmon or kallisto. Use when quantifying transcripts with Salmon or kallisto.
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Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.
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Query Ensembl for genomic data. Use when user asks about gene coordinates, genomic sequences, variants, gene structure, exons, transcripts, or species comparison. Triggers on "ensembl", "gene coordinates", "genomic location", "exon", "transcript", "variant location", "rsid", "rs number".
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Extract full transcripts and captions from YouTube videos for content analysis, summarization, note-taking, and repurposing. Supports multiple languages and auto-generated captions. Ideal for research, content creation, and accessibility workflows.
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