'segment' 검색 결과

Build structured ICP documentation by scraping case studies, testimonials, and solutions pages from your website. Produces firmographics, champion + economic buyer personas, pain points, decision criteria, anti-ICP, customer proof points, and voice-of-customer synthesis. Writes to marketing/icp/ICP.md as the canonical ICP every downstream skill reads. Triggers - "icp research", "ideal customer profile", "persona research", "customer segments", "who are our customers", "build ICP"

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Design retargeting and remarketing campaign architecture across platforms — audience segmentation, creative strategy, frequency caps, and platform-specific setup with ad policy compliance. Use when "retargeting", "remarketing", "retarget", "re-engage visitors", "abandoned cart", "pixel setup", or any request to bring back visitors who didn't convert.

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Uses AWS SDK CloudWatchClient GetMetricData and CloudWatch Logs Insights StartQueryExecution to automate incident triage. Correlates alarms via DescribeAlarms with X-Ray trace segments for root cause analysis.

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Streams audio from Twilio Media Streams over WebSocket to AssemblyAI real-time transcription, extracting speaker-diarized transcripts with word-level timestamps. Triggers entity detection and sentiment analysis via AssemblyAI LeMUR on completed call segments. Results are pushed to HubSpot or Salesforce contact records via their REST APIs.

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Expert customer lifecycle marketing guidance for retention, expansion, and advocacy. Use when designing onboarding programs, creating upsell strategies, preventing churn, building referral programs, or writing customer success content. Use for NPS programs, win-back campaigns, customer segmentation, and lifecycle email sequences.

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Expert product discovery guidance for user research and problem validation. Use when conducting user interviews, validating problems, applying jobs-to-be-done framework, sizing opportunities, customer segmentation, competitive analysis, prototype testing, usability testing, designing surveys, or synthesizing research insights. Covers discovery sprints, continuous discovery, and research operations.

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This skill should be used when the user asks to "add a Lottie animation", "play a .lottie or .json animation", "integrate a Bodymovin/After Effects animation on web or mobile", "control Lottie playback / segments", "make a scroll-driven Lottie", "recolor a Lottie at runtime", or "export an AE animation to Lottie". Covers dotLottie/lottie-web integration, playback control, interactivity, theming, and the AE export checklist.

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Builds credible TAM/SAM/SOM analysis with external validation and sensitivity testing for startup fundraising: top-down, bottom-up, or both. Use when the user asks to size a market or check market claims and gives something to size: a product or service description, a market segment, or a deck with TAM/SAM/SOM claims. A full run takes about 15–20 minutes and searches the web, 30 or more with a large data room or a revision round, so for a passing question about market size, answer briefly in chat and offer the analysis instead of starting it. When it runs, every figure is externally sourced and stress-tested, never estimated from memory.

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Assesses conglomerate break-up activism with segment valuation, separation feasibility, and tax-free qualification analysis. Use when evaluating SOTP activism, analyzing break-up proposals, or modeling separation value.

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Evaluates conglomerate discount with SOTP analysis, segment-level valuation, and separation benefit quantification. Use when analyzing conglomerate value, building SOTP models, or evaluating break-up scenarios.

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Reference for advanced data structure patterns used in competitive programming and technical interviews. Covers heaps, monotonic stacks, tries, segment trees, Fenwick trees, stack-based parsing, and ordered sets with Python templates, recognition signals, and edge case guidance.

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Vue Router v4 (Vue 3 default) — route configuration, nested routes, dynamic segments, navigation guards, lazy loading, typed routes, route meta, programmatic navigation. Vue Router v3 (Vue 2) pointer only. Use this skill to: - Configure routes with createRouter + createWebHistory. - Use useRoute / useRouter composables in components. - Lazy-load route components for code splitting. - Implement auth guards via meta + beforeEach. - Type route params for safer access. Do NOT use this skill for: - General Vue conventions (see vue-conventions). - State management (see vue-state-management). - Forms (see vue-forms). - Testing routes (see vue-testing).

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Fit hidden Markov models using the Baum-Welch (EM) algorithm with model selection, Viterbi decoding for state sequences, and forward-backward probabilities. Use when observations are generated by unobservable latent states, you need to segment a time series into latent regimes (market regimes, speech phonemes, biological sequences), compute sequence probabilities, decode the most likely hidden state path, or compare models with different numbers of hidden states.

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Perform a ruler-and-compass construction with step-by-step justification for each operation, producing a constructible geometric figure from given elements. Covers classical Euclidean constructions including perpendicular bisectors, angle bisectors, parallel lines, regular polygons, and tangent lines. Use when given geometric elements (points, segments, angles) and asked to produce a Euclidean construction, verify constructibility, or generate ordered construction steps for educational or documentation purposes.

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Build evidence-labeled semantic cores for SEO, LLM-readable architecture, and site information design. Use this skill when the user asks for semantic core, query clusters, search intents, audience segments, entity/topic maps, language or locale priorities, competitor/source evidence, pillar-topic planning, or a structured handoff before URL architecture and internal linking. It must mark unknown search volume, difficulty, and ranking data as unknown instead of inventing metrics.

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End-to-end system for creating supervised fine-tuning datasets from books and training style-transfer models. Covers text extraction, intelligent segmentation, synthetic instruction generation, Tinker-compatible output, LoRA training, and validation.

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Fix RFDB V2 graph data silently not persisting to disk after analysis. Use when: (1) rfdb-server reports "0 nodes, 0 edges" on restart despite successful analysis, (2) segment directories exist but are empty (no .bin files), (3) manifest_index.json shows total_nodes: 0 despite analysis logging 70k+ nodes, (4) Docker builds produce empty graph databases, (5) --clear flag used before analyze command. Root cause: GraphEngineV2::clear() replaces the store with MultiShardStore::ephemeral() which has path: None, causing all flush operations to write to in-memory buffers only.

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Recover from stale RFDB nodes that survive `commit_batch` cleanup despite having their `file` field listed in `changed_files`. Use when: (1) re-running an enricher/orchestrator step but old data with the same file field stays in the graph, (2) `commit_batch(changed_files=[X], ...)` reports success but a query later still returns nodes with `file = X`, (3) you re-pointed structural nodes (e.g., DIRECTORY/FILE) from a synthetic file path to real file paths and the old synthetic-path nodes won't go away, (4) graph-stream / find_by_type returns a mix of "stale" and "new" nodes for the same logical entity. Root cause: `handle_commit_batch` calls `engine.find_by_attr({file: X})` to enumerate nodes-to-delete, but that query path returns only a PARTIAL set after compaction (L1 segments and the file→nodes index can desync). Workaround: enumerate stale nodes via the TS rfdb client `queryNodes({file: X})` (different code path that finds all of them) and call `deleteNode(id)` per node, then re-commit the intended state. Related: rfdb-manifest-l1-carryforward (same L1 family).

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OpenTSC v2.1: Soul/Shell architecture for interpersonal intelligence. Local, offline-first, with event graphs, judgment engine (K7), self-creation (K8), three-layer attributes, calibrated predictions, 11 VSM professions, and a pluggable zvec memory index (semantic search, identity resolution, jieba segmentation, emotion scoring incl. batched+cached LLM) that runs with zero extra deps. Transforms implicit social judgment into explicit, queryable, auditable, evolving intelligence.

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分析 PyTorch memory snapshot pickle 文件（_dump_snapshot 导出的内存快照），提供内存峰值、碎片、泄漏、OOM 检测与交互式 HTML 报告。仅当用户明确提及 memory snapshot、内存快照、_dump_snapshot、pickle 内存文件 或需要对 snapshot pickle 做内存分析时才触发，不处理一般的 NPU 性能分析、SQLite 查询或通用内存问题。

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Query Strava fitness data including activities, athlete stats, segments, routes, clubs, and gear. Use when the user asks about cycling, running, swimming, workouts, training, or Strava data.

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Find and position a viable niche market for a one-person company by combining market mapping and customer segmentation. Use when Codex needs to explain niche concepts when needed, check founder-resource prerequisites, ask one question at a time, generate multiple niche options, and write user-confirmed positioning outputs into `opc-doc/`.

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Design value propositions for candidate customer segments and help the user choose the strongest one. Use when Codex needs to explain jobs, pains, and gains when needed, check niche-positioning prerequisites, ask one question at a time, present multiple value-proposition options, and write user-confirmed outputs into `opc-doc/`.

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Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.

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