「stream」の検索結果

Applies Steve Blank's Customer Development methodology from The Four Steps to the Epiphany. Use when a startup is searching for customers and a business model before scaling. Covers Customer Discovery, Customer Validation, Customer Creation, and Company Building. Triggers include 'we built it but no one's buying', 'should we hire salespeople yet', 'how do we find our first customers', 'we're burning cash and sales aren't scaling', 'are we in a new or existing market', 'when do we scale'. NOT for companies that have already crossed the chasm into mainstream (use Crossing the Chasm instead), not for optimizing an existing sales funnel, not for product development methodology (this is its companion, not replacement).

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Contribute skills back to upstream via branch and PR

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Sync local skills repository with upstream changes from obra/superpowers-skills

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Detect potential code-level performance smells in Java - streams, collections, boxing, regex, object creation. Provides awareness, not absolutes - always measure before optimizing. For JPA/database performance, use jpa-patterns instead.

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Tests whether a scene is structurally worth generating before any shot is prompted — a five-element engine of Goal, Obstacle, Tactic, Reversal, and Value Shift. Use when the user has a scene, sequence, beat outline, or script and wants it audited or strengthened; when a sequence generates cleanly but lands flat and nobody can say why; when shots look good individually but the run of them does not build; or when the user asks 'is this scene working', 'what's weak here', or 'why doesn't this land'. Upstream of prompting — it decides WHICH shots deserve the credits, not how to write them. Pairs with higgsfield-character-design (who the characters are) and higgsfield-shotlist-director (turning the settled scene into shots).

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Pre-production story-and-character development for Higgsfield projects — the upstream layer that decides WHAT to prompt before any model runs. Use when the user wants to build a character, design a world, develop a story or premise, create a character sheet / character bible / story bible, lock a visual style or 'visual DNA', plan a multi-shot narrative with consistent characters, or says things like 'help me design a character', 'build the world', 'I need a backstory', 'make this character consistent across shots', 'develop my film/series concept', or 'I keep getting generic AI characters'. Routes the locked outputs into higgsfield-prompt + the right model. Adapted from Higgsfield's official character-design framework by @vavavinca.

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Upload and fully manage YouTube videos and live events: metadata, thumbnails, captions, playlists, scheduled broadcasts, encoder streams, binding, lifecycle transitions, and cleanup.

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Download or open videos and recover platform captions, audio transcripts, keyframes, screen text, visual facts, and editing observations as a plain multimodal_transcript.md. Use for Douyin, Bilibili, YouTube, Xiaohongshu, experimental WeChat Channels links, or local video files when preparing complete source material for analysis or downstream Skill distillation.

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Search an index of public repositories, GitHub issues, merged pull requests, repository READMEs, and curated documentation sites. Use when a programming question needs external documentation or upstream evidence.

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Use when setting up firebase_ai, generating text/chat with Gemini, streaming AI output, building multimodal prompts, or handling AI errors.

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Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.

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Run a TypeScript script via tsx to fetch an HTTP endpoint and stream the response. Use when you need a small Node.js helper for HTTP calls.

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Advanced distributed patterns - event sourcing, CQRS, saga, stream processing, append-only log, exactly-once delivery, sequencer, double-entry ledger, erasure coding, order book, watermarks

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Event Sourcing and CQRS as DDD implementation patterns — when to use, aggregate event streams, projections, snapshots, sagas, upcasting, conflict resolution

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Data architecture patterns (warehouse, lake, lakehouse, mesh), ETL/ELT pipelines, streaming architectures, scaling strategies, and schema design patterns

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Use when the user wants a video of a website (site tour, portfolio, docs or landing-page showcase) captured from a URL. Deprecated upstream since v0.7.59: folded into /product-launch-video, which owns URL promo work.

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Install, discover, and wire registry blocks and components into HyperFrames compositions. Use when running hyperframes add or hyperframes catalog, installing one item or every block matching a tag, wiring an installed item into index.html, or working with hyperframes.json. Covers discovery, install locations, block sub-composition wiring, component snippet merging, and authoring a new block or component to contribute upstream (idea → scaffold → validate → PR).

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Use when implementing GPT chat, streaming, function calling, embeddings for RAG, images, audio or batch jobs, or troubleshooting 429 rate limits and API or TypeScript errors. Stateless OpenAI patterns that prevent 16 documented errors.

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Use when building AI features into a product: LLM integration, RAG pipelines, guardrails, streaming, AI UX, prompt engineering, or AI cost control. Treats prompts as code and validates every model output.

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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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Build AI applications with OpenAI Agents SDK - text agents, voice agents, multi-agent handoffs, tools with Zod schemas, guardrails, and streaming. Prevents 11 documented errors. Use when: building agents with tools, voice agents with WebRTC, multi-agent workflows, or troubleshooting MaxTurnsExceededError, tool call failures, reasoning defaults, JSON output leaks.

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Use when linked docs drift, when a source file changes and downstream PRDs, decks, or notes must follow, or when a .sync-report.md exists. Diffs the proposed edits, applies them only after approval, and never overwrites a target file.

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Validate committed feature-branch changes through the no-mistakes pipeline: intent, rebase, review, test, docs, lint, push, PR, and CI. Use when the user asks to run no-mistakes, ship safely, validate before pushing, or gate a change before it reaches upstream.

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Standards for Feign clients and asynchronous messaging with Spring Cloud Stream. Use when implementing Feign HTTP clients or async event messaging in Spring Boot microservices.

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。