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Analyses sales call transcripts (and churn or renewal calls) to find why deals are won, lost, kept or churned. Reads each call across six dimensions (product, messaging, sales process, pricing, competition, customer context), pulls verbatim quotes with the speaker named, counts how often each pattern appears, and writes findings with recommendations. Works with any recorder export or pasted text. Triggers: "win/loss analysis", "why do we lose", "why do we win", "analyse these sales calls", "lost deals", "churn analysis", "objection patterns". Feeds /positioning and /messaging. Not for general meeting summaries or competitor research (use /competitor-research).

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Builds an ideal customer profile from evidence: case studies, testimonials, customer logos, solutions pages, reviews and any sales notes you have. Produces market sizing layers, firmographic segments, a champion and an economic buyer persona, a negative ICP, intent signals, customer proof points and a voice-of-customer section in the buyers' own words, every claim sourced. Triggers: "ICP research", "ideal customer profile", "who are our customers", "customer segments", "buyer personas", "who should we target". Run after /company-context. Not for competitor analysis (use /competitor-research) or call transcript patterns (use /win-loss).

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Analyze sales-call transcripts to extract why deals are won and lost across six dimensions — product, messaging, GTM/sales, pricing, competition, and customer context. Produces aggregated patterns with verbatim buyer quotes, frequencies, and recommendations. Writes to marketing/win-loss/win-loss.md as the evidence base under positioning, messaging, and ICP. Triggers - "win loss analysis", "why are we losing deals", "why do we win", "analyze sales calls", "churn analysis", "deal review"

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Extract voice patterns from existing content (website, blog, social, sales-call transcripts) and codify into voice rules. Produces voice analysis + voice guidelines (rules per pattern + violation pattern + fix template). Writes to marketing/brand/brand-voice.md as the canonical voice rules every content skill reads. Triggers - "tone of voice", "brand voice", "voice guidelines", "TOV audit", "writing rules", "extract voice"

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Scout a topic on YouTube. Searches YouTube Data API v3 for the most relevant videos, ranks them by views (or engagement, likes, recency, momentum, breakout), and writes a research workbook in the OWT-Social-Ads layout with Videos, Channels, and Summary sheets, plus optional Comments and Transcripts (hooks, replay hotspots) sheets. Can also append into an existing sheet. Use when the user says "scout <topic>", wants a spreadsheet of top YouTube videos on a topic, wants creator or hook research, or wants YouTube rows added to their social ads sheet.

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Transform any research material into SEO+GEO-optimized blog posts for Knox Hub. Takes any input (URL, article text, notes, video transcript, competitor post, topic idea) and rewrites it as natural human-sounding content, then fills in all fields for the Knox Hub post editor: title, excerpt, SEO settings, FAQ schema, table of contents, sticky sidebar, and full article content in Markdown. Output is ready to copy-paste into your blog/CMS post editor. Use when asked to "write a post", "rewrite this", "сделай пост", "напиши статью", "перепиши это для блога", "create blog post", or any content creation task.

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Reverse-engineer a supplied video into a shot-by-shot generation specification, displayed evidence frames, and paired Chinese-English prompt packages. Use for requests such as 视频反推提示词, 视频转提示词, 从视频生成提示词, 视频逆向提示词, 逐镜头反推, 分镜拆解并生成提示词, 反推视频 prompt, or video-to-prompt; also use for Veo, Sora, Kling, Wan, generic prompt adaptation, and bounded generate-compare-optimize loops. Do not use for ordinary video summaries or basic transcription.

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Add or maintain CodeBuddy hook observability for projects that need local metrics for tool latency, transcript token usage, session cost, and multi-agent trace attribution. Use when wiring hook-based telemetry, debugging missing usage events, and validating AgentLens state.

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Instruments and workflow for debugging the VirtualList — the chat transcript (`InfiniteList`) and the sidebar (`FiniteList`). Covers the built-in consistency checker, the on-screen overlay, the five `?vl*` URL flags, the touch-gesture rig, the frame recorder, attaching to a real Android device over CDP, and how to read a trace without being fooled by a measurement artifact. Use when the list jumps while scrolling, blanks, sticks on skeletons, or the overscroll/bounce misbehaves. The component's specification is `docs/ui/virtual-list.md`; this skill is only how to *measure* it. Pairs with /debug-ui (browser side) and /server-loop (server side).

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Transcribes one explicitly authorized Brazilian Portuguese or Spanish audio or video file through the metered BRAINIALL API, then creates speaker-labelled JSON, SRT, and WebVTT with word timestamps and no automatic retry.

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Summarizes audio content using AssemblyAI's LeMUR (Large Language Model for Audio Understanding) API. Chains the /v2/transcript endpoint with /lemur/v3/generate/summary for contextual audio intelligence.

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Extract structured intelligence from audio using the AssemblyAI API with sentiment analysis, entity detection, topic modeling, and auto-chapter generation. Uses the assemblyai Python SDK for transcript processing pipelines.

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Transcribes audio and generates auto-chapters with summaries using AssemblyAI's /v2/transcript endpoint with auto_chapters=true. Extracts key topics, sentiment analysis, and content safety labels via AssemblyAI SDK.

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Summarize or transcribe URLs, YouTube/videos, podcasts, articles, transcripts, PDFs, and local files.

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Process YouTube videos, playlists and channels with the youtube-to-docs MCP server. Use this when the user wants to: (1) get a transcript, (2) summarize a video or generate Q&A, speakers and tags, (3) make an infographic, (4) make an audio or video summary, (5) translate the outputs, (6) make the video accessible (WCAG 2.1 / Section 508 corrected captions, audio description), or (7) do a full 'kitchen sink' run.

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Render a dashboard of the current Claude Code session — message counts, token usage, cache read/write, dollar cost, context %, lines changed, cost-per-turn sparkline, and a tool-call histogram — parsed from the session transcript JSONL by a bundled bash script. One bash call, zero analysis, zero confirmation. Optionally pass a session id to inspect a different session. Use when the user asks for session stats, usage, token or cost breakdown, or a session dashboard.

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Capture outbound HTTP and telemetry from a CLI harness at runtime. Covers capture-channel selection (transcript file vs verbose-fetch stderr vs outbound proxy vs on-disk state), hook-driven per-event capture vs long-running session capture, JSONL output format for diff-friendly artifacts, and the observability table that maps each target to the cheapest channel that captures it. Use when a static finding needs runtime confirmation, when a payload shape is needed for a client re-implementation, or when dark-vs-live disambiguation requires watching what the binary actually sends.

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Analyze a YouTube video and create a vault note from its transcript. Use this skill whenever a YouTube URL is pasted — even with casual commentary like "check this out" or "interesting talk." Also triggers on "youtube", "take notes from this video", "summarize this video", or any YouTube link (youtube.com, youtu.be) regardless of surrounding context.

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Process an online course into detailed vault notes — one per lecture, with slides and transcripts synthesized. Use this skill whenever the user shares a course URL, syllabus page, or lecture playlist and wants structured notes from it. Also triggers on "process this course", "take notes on these lectures", "course notes", any university course page, or when someone pastes a link to a course with multiple lectures/sessions. Even if the user just says "check out this course" or drops a .edu link with a schedule — use this skill.

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Use when an agent runtime must choose or verify a /brain-* command route from a user request, handoff, adapter transcript, or stale command catalog.

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This skill should be used to search, inspect, list, and recover local Codex sessions through Codex's native App Server APIs. Trigger for requests such as find a prior Codex conversation, search Codex transcript text, list recent Codex work, locate matches inside a session, or produce a safe Codex resume command. Do not use for Claude Code sessions, project source-code search, or web search.

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Plan and record evidence-backed LLM and AI-search citation checks across approved assistant/search surfaces with timestamped queries, locales, modes, cited URLs, snippets, screenshots or transcripts, and caveats. Use this skill when the user asks whether ChatGPT, Perplexity, Claude, Gemini, Copilot, or another assistant cites a target site, but do not claim citation without direct evidence.

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Use when the user asks to beam, publish, or share the current local coding session to an authenticated remote receiver. Not for viewing a transcript locally: use session-viewer.

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Use when the user asks to view, export, or inspect a session transcript in a browser. Not for sharing a session: use session-share.

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