「stream」の検索結果

On-Behalf-Of (OBO) Flow for web APIs to call downstream APIs while preserving user identity in MSAL.NET

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Curate .unikit/MCP-RECHECK-NOTES.md — the project's log of findings about the configured engine MCP server. Four jobs: re-stamp (the server moved, so every entry is suspect), replay (reproduce a `replay: safe` finding inside a disposable sandbox to see whether it still holds), retire (offer to drop what was fixed or went upstream), and upstream (print a ready diff for the packaged INDEX.md). Use for "audit the MCP notes", "are these findings still true", "recheck the MCP traps", "clean up MCP-RECHECK-NOTES", "the server was updated — revisit the notes". Replaying mutates a live editor, so it starts by telling you how to prepare it — save your scene, open an empty one — and asks once before touching anything. Everything it creates lives under a single UNIKIT_AUDIT_<runid> prefix, is deleted in one action, and the scene is never saved. To record a NEW finding use /unikit-mcp-trap instead.

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Checks the canonical upstream issue tracker, discussion forums, and open pull requests, merge requests, or equivalent change queues for existing reports, discussion, or work on a problem just discussed. Use before diagnosing, fixing, or reporting an apparent open-source problem to avoid duplicate effort and missed context.

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Process multimedia files with FFmpeg (video/audio encoding, conversion, streaming, filtering, hardware acceleration) and ImageMagick (image manipulation, format conversion, batch processing, effects, composition). Use when converting media formats, encoding videos with specific codecs (H.264, H.265, VP9), resizing/cropping images, extracting audio from video, applying filters and effects, optimizing file sizes, creating streaming manifests (HLS/DASH), generating thumbnails, batch processing images, creating composite images, or implementing media processing pipelines. Supports 100+ formats, hardware acceleration (NVENC, QSV), and complex filtergraphs.

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Comprehensive guide to designing reliable, scalable data systems covering databases, streaming, and consistency

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Design Kafka consumer groups that survive rebalances, hit the right delivery guarantee (at-most/at-least/exactly-once), and handle poison messages without stalling the topic. Use when picking a partition assignment strategy (Range vs Sticky vs Cooperative-Sticky vs the new KIP-848 protocol), tuning heartbeat / session / max.poll.interval timeouts, choosing manual vs auto offset commits, designing dead-letter or retry topics, or migrating from Classic to the next-gen Consumer protocol. NOT for Kafka cluster ops (broker tuning, partition reassignment), schema design (Avro/Protobuf/Schema Registry), or stream processing topology (use a kafka-streams skill).

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Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).

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Spark, pandas, polars, DuckDB optimization for batch data processing. Activate on: batch processing, Spark optimization, polars, DuckDB, pandas performance, data frame, shuffle, partition, memory optimization. NOT for: streaming pipelines (use streaming-pipeline-architect), warehouse queries (use data-warehouse-optimizer).

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Apache Airflow DAGs, operators, SLA monitoring, and workflow orchestration. Activate on: Airflow, DAG, operator, sensor, scheduler, task dependency, SLA, backfill, XCom. NOT for: dbt transformations (use dbt-analytics-engineer), streaming pipelines (use streaming-pipeline-architect).

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Build whole-genome alignments using Progressive Cactus (Armstrong 2020 reference-free clade-level WGA), Minigraph-Cactus (Hickey 2024 pangenome-aware), LASTZ chain/net (UCSC pipeline), MUMmer4 (Marçais 2018 pairwise), minimap2 -x asm5/10/20 (Li 2018 fast pairwise), AnchorWave (Song 2022 WGD-aware), and Mauve / progressiveMauve (bacterial). Operates the HAL toolkit (Hickey 2013) for downstream extraction including halSynteny, halLiftover, halBranchMutations, and hal2maf. Use when constructing multi-species alignments for comparative-annotation projection (TOGA), synteny detection, conservation analyses (phyloP / PhastCons), or pangenome graph construction; selecting between reference-free (Cactus) and reference-anchored (LASTZ chains/nets) approaches; tuning sensitivity for closely vs distantly related genomes; or producing HAL files for genome-wide downstream tools.

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Detects allele-specific transcription factor or histone modification binding from heterozygous-variant ChIP-seq using WASP (reference-bias filter; mandatory upstream), RASQUAL (joint QTL + bias-corrected testing), BaalChIP (Bayesian beta-binomial with copy-number-aware overdispersion), and AlleleSeq (personalized diploid genome). Handles imprinted-locus awareness, X-inactivation artifacts, cancer copy-number imbalance, and integration with downstream caQTL / bQTL mapping. Use when identifying variants with allelic effects on TF binding, fine-mapping causal regulatory variants, validating deep-learning variant predictions, or characterizing cis-acting regulatory effects.

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Traceability review — every spec AC must trace to a resolvable upstream AC in the intake (and BRD if present), and no upstream AC is silently dropped. Read-only. Run alongside `spec-diagram-review` before implementation.

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Build real-time streaming applications with Azure Event Hubs SDK for Java. Use when implementing event streaming, high-throughput data ingestion, or building event-driven architectures.

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Use when reviewing a .NET pull request for breaking changes that may affect other microservice repositories. Detects cross-repo API, DTO, endpoint, EF entity, and NuGet-package breaks, and checks whether a compatible downstream PR already exists. Keywords: cross-repo impact, breaking change, microservice compatibility, downstream break, compatible PR, blast radius review, api compatibility, contract break, dto shape change, endpoint break.

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Plataforma de observabilidade de IA de código aberto para rastreamento de LLM, avaliação e monitoramento. Use ao debugar aplicações de LLM com rastreamentos detalhados, executar avaliações em datasets ou monitorar sistemas de IA em produção com insights em tempo real.

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Especialista em Langfuse - plataforma de observabilidade LLM de código aberto. Cobre rastreamento, gerenciamento de prompts, avaliação, datasets e integração com LangChain, LlamaIndex e OpenAI. Essencial para depuração, monitoramento e melhoria de aplicações LLM em produção. Use quando: langfuse, observabilidade llm, rastreamento llm, gerenciamento de prompts, avaliação llm.

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Load this skill before implementing a fix for a barrier that may originate in a shared component, design system, renderer, or external dependency rather than this project's own code. Decide whether the responsible fix is local, an existing installed capability, or an upstream contribution before writing a workaround. Absolutely always record any temporary local divergence with an owner and removal condition. Under no circumstances open, comment on, or submit anything in an external project without explicit human approval.

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Cursor Cloud Agentにタスクを委託するよう依頼された際に発動。エージェント作成、SSEストリーム監視（stream）、実行キャンセル（cancel）、ステータスポーリング（フォールバック）、Pull Request状態確認、Linear紐付け、レビュー・フォローアップを行う。

Lark/Feishu real-time event listening / subscribing / consuming: stream events as NDJSON via `lark-cli event consume <EventKey>` (covers IM message receive, reactions, chat member changes, etc.). Use for Lark bots, real-time message processing, long-running subscribers, streaming webhook/push handlers. Supports `--max-events` / `--timeout` bounded runs and a stderr ready-marker contract — designed for AI agents running as subprocesses.

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Cloudflare Workersのコードを本番運用品質のベストプラクティスに照らして作成・レビューする。新しいWorkerの実装、Workerコードのレビュー、wrangler.jsoncの設定、streaming・floating promises・global state・secrets・bindings・observabilityなどのanti-pattern確認で使用する。事前学習の記憶よりCloudflare公式ドキュメントからの取得を優先する。

吉里吉里Z (kirikiri Z) 本体クラス API のリファレンス。TJS2 で吉里吉里Z 上のスクリプトを書く、レビューする、デバッグするときに使う。Layer / Window / Bitmap / System / Storages / Font / Plugins / Timer / Debug / AsyncTrigger / Scripts / BinaryStream / Matrix32 / Matrix44 / Rect / ImageFunction のコア API、サウンド系 (WaveSoundBuffer / SoundBuffer / VideoOverlay)、DrawDevice (BasicDrawDevice / SDLDrawDevice / OGLDrawDevice / GPUDrawDevice / NullDrawDevice)、OpenGL 描画系 (Canvas / Texture / ShaderProgram / Offscreen / VertexBinder / VertexBuffer)、および主要プラグイン提供クラス (HttpRequest / GdiPlus.* / WIN32Dialog / CSVParser / LineParser / Process / Pad / MenuItem / Unzip / Zip / PSD / SimpleHTTPServer) を網羅。**呼び出されたら必ず「共通パターン」と「クロスカッティング概念」を確認し、必要な詳細クラスは doc/reference/*.md を Read しに行くこと。** TJS2 言語そのものや組み込みクラス (Array / Dictionary / Math 等) は別 skill (`tjs2`) を参照。エンジン内部構造 (C++ 実装、レンダリングパイプライン詳細) は対象外。

Deep research workflow that starts from a GitHub repo URL/slug, pulls upstream intel, then expands to Reddit + open web for community reception. Use whenever Ahmad gives a GitHub link and asks to understand it, see updates, audit the community, or compose a report. Trigger phrases include "research this repo", "report on this github", "what's happening upstream", "community reception", "compose a report on <repo>", and any message that contains a github.com URL plus a research/report ask.

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Integrate and maintain Robelest Convex Auth in apps by always checking upstream before implementation. Use when adding auth setup, updating auth wiring, migrating between upstream patterns, or troubleshooting @robelest/convex-auth behavior across projects.

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Integrate and maintain Robelest Convex Auth in apps by always checking upstream before implementation. Use when adding auth setup, updating auth wiring, migrating between upstream patterns, or troubleshooting @robelest/convex-auth behavior across projects.

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