Expert en Apache Kafka (producers, consumers, streams, connectors, schema registry)
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。
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Expert en Apache Kafka (producers, consumers, streams, connectors, schema registry)
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。
Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq c_curve / lc_extrap, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT — see Tips), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for downstream cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for downstream visualisation.
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。
Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq c_curve / lc_extrap, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT — see Tips), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for downstream cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for downstream visualisation.
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。
Architects the encryption, codec/transport, and premium-feature layer of a group video-calling product, on top of an already-chosen SFU topology. Covers E2EE patterns (Zoom leader-fan-out, Signal pairwise-fan-out, MLS/TreeKEM as in Discord's DAVE, Telegram's ledger consensus), the SFrame (RFC 9605) + WebRTC Insertable Streams mechanism letting an SFU relay encrypted frames it can't read, codec/transport choice (AV1/VP9 SVC vs. simulcast, Opus, congestion control, Media-over-QUIC, WHIP/WHEP), and which "premium" features (virtual backgrounds, live captions, recording, breakout rooms, spatial audio) survive E2EE vs. structurally can't. Use when adding E2EE to an SFU-based call, choosing a 2026 codec/SVC strategy, or deciding which features to disable under E2EE mode. NOT for call topology/platform choice (see webrtc-adhoc-video-chat, which this composes with). NOT for one-way broadcast (see managed-video-streaming-pipeline). NOT for the DM/group-chat text backend (see realtime-messaging-backend-architecture).
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。
Design and build mathematically rigorous, high-density, interactive data graphics and streaming telemetry visualizations following Mike Bostock's D3.js and Observable methodologies. Use when crafting custom SVG/Canvas charts, reactive streaming time-series, multi-dimensional brush-and-link coordinates, small multiples, or topological swarm graphs. NOT for generic dashboard template clones, basic spreadsheet charts, or off-the-shelf low-effort wrapper libraries.
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。
動画・音声をVideoDBに取り込み、発話や映像から場面を検索して切り抜きを作成。字幕や音声の追加、画面比率の変換、ライブ映像の通知設定も支援します。
リアルタイム画面やデータ配信の遅延、情報の鮮度、処理待ちを個別に測り、イベント発生から画面表示までの経路を改善して、稼働環境で結果を確かめるスキル。
公開後のURLを点検し、ページやAPIの応答、配信ファイル、継続通信、表示速度の悪化を確認します。単発チェック、一定期間の監視、本番と検証環境の比較に対応します。
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).
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。
A contract for what the agent does when a long, messy, stream-of-consciousness ramble arrives (usually voice dictation): act on nothing until the echo brief is approved. The echo audits the entire transfer, mission, locked decisions and constraints, open questions, flips and parked tangents, with the model's inferences and guesses quarantined away from the user's own phrasing, so the user verifies what the model believes, not just what it doubts. Use when the user says "let me think out loud" or wants to ramble a bit before building anything, when a message opens with a speech-to-text preamble like "switching to voice, sorry for typos", when input is a long weakly punctuated stream with restarts and mid-message reversals ("actually no, wait, scrap that idea entirely"), or when the user asks to be interviewed about a fuzzy half-formed idea. Includes an optional capture mode for rambles spread across several messages and an optional targeted interview.
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。
Stream live swarm events using the Monitor tool for real-time observability
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。
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
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。
QA opencode itself, per case: verify the CLI/terminal (opencode run, db, serve, export), prove a specific plugin hook/action/event fired via the SSE event stream, smoke-test the TUI under tmux, and investigate sessions in opencode's SQLite DB by id, title/name, or message text. Ships tested helper scripts (each with a --self-test) plus per-domain references. Use whenever someone wants to QA, smoke-test, verify, or debug opencode's CLI, HTTP server, plugin hooks/events, or TUI, or to find/inspect opencode sessions in the database. Triggers: opencode qa, qa opencode, test opencode, verify opencode hook, opencode session db, find opencode session by id/name/text, opencode tui test, opencode server health, opencode event stream.
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。
Contribute a verified bug fix for LazyCodex, lazycodex-ai, omo-codex, bundled Codex skills, or upstream Codex CLI bugs. Opens a fork PR only for upstream openai/codex; LazyCodex-owned defects become a verified-fix issue on code-yeongyu/lazycodex (never a PR — that repo is a generated distribution mirror). Use when the user asks to fix a bug, contribute a bug fix, contribute to fix bug, open a PR for a bug, or debug and PR a LazyCodex/Codex defect.
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。
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).
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。
Update the current Remotion branch with the latest canonical main branch. Use when asked to update or sync from upstream main, merge the latest main into a feature branch, fast-forward local main, or bring a pull request branch up to date without rewriting published history.
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。
Record upgrade instructions alongside a Prisma 8 breaking-change PR, so downstream consumers (users of `@internal/*` and authors of Prisma 8 extensions) can apply the matching code translation automatically via the published upgrade skills. Use when you have refactored framework code and the test suite went red in `examples/` or `packages/3-extensions/`, when you fixed those red tests by updating example or extension code, when you are told to "record upgrade instructions for this PR", or when you made a breaking change to Prisma 8 that downstream consumers will need help migrating across.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for packet capture analysis, session reconstruction, application-protocol decoding, stream reassembly, beacon timing, and packet-to-process correlation. Use when the user asks to analyze a PCAP, rebuild TCP or UDP sessions, decode HTTP, WebSocket, DNS, custom C2, or binary protocols, extract transferred artifacts, or tie packet sequences to host or malware behavior. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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。
Python resource management with context managers, cleanup patterns, and streaming. Use when managing connections, file handles, implementing cleanup logic, or building streaming responses with accumulated state.
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。
Ranks any list of impediments and their countermeasures using a value-stream scoring model (ROI, Cost to Implement, Ease of Deployment, Risk Factor) and a fixed prioritization formula. Use when someone asks to prioritize, rank, sequence, or triage impediments, countermeasures, remediation items, risks, findings, gaps, action items, or backlog entries; or mentions value-stream prioritization, A3 / lean countermeasure ranking, ROI vs. effort scoring, or building a remediation / improvement backlog. Works with GHQR findings, audit results, retrospective action items, risk registers, architecture review gaps, or any free-form `{impediment, countermeasure}` list.
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。
Build Model Context Protocol (MCP) servers in C#/.NET against the current ModelContextProtocol 2.x NuGet packages. Helps with cases the model gets wrong without guidance — stale versions (0.x preview or 1.x-era defaults), the v2 stateless-by-default HTTP flip, the 2026-07-28 spec deprecations (roots/sampling/logging), MCP Apps and Tasks extension packages, elicitation URL mode, per-session HTTP wiring, OAuth and reverse-proxy deploy specifics, and debugging MapMcp / STDIO / Streamable-HTTP errors. Also covers STDIO and Streamable HTTP transports (SSE is deprecated), tools, prompts, resources, completions, and a basic .NET MCP client. Trigger when the user says or implies any .NET MCP server work: ModelContextProtocol, McpServerTool, MapMcp, WithStdioServerTransport, "MCP server in C#", "MCP tool in dotnet", "expose this as MCP", or names a primitive (prompt/resource/elicitation/MCP App) in a .NET context. Skip for MCP work in other languages.
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。
Examine NTFS slack space, MFT entries, the USN Change Journal, and Alternate Data Streams (ADS) to recover hidden or residual data, reconstruct deleted-file metadata, and reconstruct available file-system change activity from USN records. Use during deep forensic analysis of an NTFS image when standard file recovery is insufficient, such as hunting for data hidden in ADS.
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。
Monitor Certificate Transparency logs using crt.sh and Certstream to detect phishing domains, lookalike certificates, and unauthorized certificate issuance targeting your organization.
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。
Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
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。